Previously Funded Projects

PI: Jian Hu, Co-PIs: Rongheng Li, Xuan Zhou, Van-Hai Bui, & Antonios M. Koumpias
Awarded: $50,000
Lay Abstract: N/A

PI: Xiao Zhang, Co-PIs: Shengquan Wang, Gengxin Li & Zheshen Zhang
Awarded: $50,000
Lay Abstract: Indoor positioning serves as a foundational technology for the next-generation IoT, smart manufacturing, autonomous robots, and precision logistics. The typical tool, GPS signals, is unavailable in enclosed spaces, while conventional radio-frequency solutions suffer from electromagnetic interference, low security, poor positioning stability, and vulnerability to spoofing attacks. The new technology, visible light positioning (VLP) offers unique advantages including low cost, green illumination integration, and electromagnetic interference-free operation, yet classical VLP faces critical bottlenecks: performance degradation under weak light, ambient noise, and multipath reflections; excessive computational complexity in fingerprint matching; and limited security against forgery attacks. To overcome these limitations, this CORE project proposes Quantum-Enhanced Visible Light Positioning (Q-VLP), an innovative framework that integrates quantum sensing principles and quantum search acceleration with classical visible light localization. By leveraging both quantum algorithms to reduce complexity and quantum-enhanced signal processing to improve weak optical signal detection, the proposed Q-VLP system achieves robust, high-precision, low-latency indoor positioning with strong anti-interference capability and secure antispoofing features, enabled by an interdisciplinary framework combining optical communication, quantum information processing, mathematical optimization, and quantum sensing. This project aligns with the Advanced Technology pillar of the University of Michigan’s “Look to Michigan” initiative. Preliminary experimental outcomes based on this CORE project will directly support a future NSF quantum and cyber-physical systems proposal for deeply investigating quantumenhanced sensing, next-generation IoT, and smart manufacturing innovation, strengthening U.S. leading edge.

PI: Karishma Patnaik, Co-PIs: Nitya Sethuraman & Erik Mueller
Awarded: $39,863
Lay Abstract: Everyday tasks like writing and drawing seem simple, but they require careful control of hand movement and pressure. For many people, such as those recovering from injury, living with motor impairments, or even beginners, these tasks can be difficult because the hand may shake, tire easily, or press too hard or too lightly. As a result, people may struggle to participate in activities that are important for learning, creativity, and recovery. This project explores how a robotic stylus can support drawing and writing in a naturalistic and assistive manner. Rather than relying on conventional approaches that infer intent implicitly, we propose to externalize “candidate” actions by projecting them onto the surface (e.g., via laser-guided light) and allowing the user to select the desired trajectory via explicit cues before assistance is applied[NS1] . This approach simultaneously preserves user agency and authorship while addressing the challenge of intent disambiguation. As a result, the system can provide targeted assistance that stabilizes motion, reduces unwanted variability, and maintains consistent contact with the surface, while still supporting creative and expressive interaction. The core team comprising of experts in contact-rich robotics and embodied cognition and language, will enable the translation of verbal cues into interpretable signals of user intent and perceived ownership. An applied artist on the team will guide the identification of what makes a stroke appear natural and meaningful. Collectively, this interdisciplinary effort demonstrates how technology can enhance learning and quality of life while preserving human creativity and control. This work supports the Look to Michigan mission by advancing (i) Life-Changing Education - making hands-on learning and creative expression more accessible to students with diverse physical abilities, (ii) it advances Human Health & Well-Being by supporting recovery of fine motor skills through task-based interaction and (iii) Advanced Technology by developing new assistive robotic tools that combine sensing, adaptability, and human-centered design. The insights and design principles developed in this work will position the team for future federal funding opportunities in areas such as assistive robotics, rehabilitation, robot-assisted surgery, agriculture and manufacturing where human-robot shared control is imperative to perform any task.
 

PI: Krisanu Bandyopadhyay, Co-PIs: Utkarshani Jaimini & Karishma Patnaik
Awarded: $47,907
Lay Abstract: Copper nanoparticles are promising materials for sustainable catalysis because copper is abundant, inexpensive, and chemically versatile. However, generating stable, surface-bound two-dimensional assemblies of copper nanoparticles remains challenging because copper readily oxidizes in air. This oxidation can alter nanoparticle composition, surface structure, electronic properties, and catalytic performance. Compared with noble-metal nanoparticles such as gold and silver, literature reports on controlled two-dimensional assemblies of oxidation-free or oxidation-minimized copper nanoparticles are limited. This challenge creates an important opportunity to develop new approaches that combine surface chemistry, robotic automation, advanced characterization, and artificial intelligence. This project will establish a new interdisciplinary collaboration among chemistry, robotics, and artificial intelligence/machine learning at UM-Dearborn. The chemistry team will develop surface-bound copper nanoparticle assemblies by modifying substrates with selected ligands, adsorbing copper ions, and reducing them to form nanoparticles directly on surfaces. The robotics team will design a robotic-arm-assisted workflow to automate key steps in surface modification, copper ion adsorption, washing, and chemical reduction. This automation is expected to improve timing, reproducibility, and control over air-sensitive steps, in line with emerging robotic and autonomous strategies for nanomaterials synthesis. The AI/ML team will develop data-driven models that connect synthesis conditions, surface chemistry, nanoparticle morphology, oxidation indicators, electrochemical behavior, and catalytic performance. The project will produce a structured dataset using UV-vis spectroscopy, electrochemistry, atomic force microscopy (AFM), catalytic testing, and selected advanced microscopy and elemental analysis through the Michigan Center for Materials Characterization (MC)2 as a user facility. The team will use these data to identify synthesis conditions that favor reproducible, oxidation-minimized copper nanoparticle assemblies with promising catalytic activity. The long-term vision is to create a closed-loop materials discovery platform in which robotic preparation generates consistent samples, characterization produces quantitative descriptors, machine learning identifies structure-property-performance relationships, and model-guided experiments refine the next round of synthesis. This seed project will generate preliminary data, establish a new interdisciplinary team, and position UM-Dearborn faculty to compete for major external funding from programs such as the National Science Foundation (NSF) Designing Materials to Revolutionize and Engineer our Future (DMREF), NSF Future Manufacturing (FM), and NSF Chemical Catalysis (CAT).

PI: Zheng Liu, Co-PIs: Xin Xia, Charu Chandra, & Chuqiao Peng
Awarded: $14,000
Lay Abstract: N/A

PI: Kalyan Kondapalli, Co-PIs: Karishma Patnaik & Suvranta Tripathy
Awarded: $30,000
Lay Abstract: N/A

PI: Gengxin Li, Co-PI: Jian Hu
Awarded: $20,000
Lay Abstract: N/A

PI: Anys Bacha
Awarded: $19,982
Lay Abstract: N/A

PI: Xiao Zhang, Co-PIs: Xuan Zhou, Pravansu Mohanty, Feng Xu
Awarded: $20,000
Lay Abstract: Smart battery manufacturing, a cornerstone of global clean energy transition, demands high
precision, EMI-free operations and secure human-robot collaboration, especially in high-risk processes like electrolyte injection, electrode welding and spent battery dismantling. Existing humanrobot interaction and digital twin (DT) integration solutions have critical limitations: RF technologies disrupt sensitive battery materials via EMI; visual/voice systems are vulnerable to environmental interference and cyber hijacking; current DTs lack real-time synchronization of human intent, robot action and environmental data. To address these gaps, this RID project proposes a light-enabled secure collaboration framework for DTs in smart battery manufacturing. Leveraging visible light communication (VLC)’s EMI immunity, line-of-sight security, millimeter-level positioning and resistance to harsh factory conditions, we design a sandwich-structured passive (SSP) optical film for wearables paired with robot-mounted light sensors. The framework integrates secure gesture recognition, physics-informed AI-driven anomaly detection with DT synchronization, and multi-factor optical authentication to prevent hijacking. This solution enables reliable human-robot collaboration in high-risk scenarios and bridges DT data gaps for remote diagnostics. Preliminary results will support a subsequent NSF CPS proposal, advancing cyber-physical systems in smart manufacturing and U.S. clean energy agendas.
 

PI: Anwar Ghammam
Awarded: $20,000
Lay Abstract: N/A

PI: Cheol Lee, Co-PI: Oleg Zikanov
Awarded: $19,523
Lay Abstract: N/A

PI: Caymen Novak, Co-PI: Rafael Ruiz
Awarded: $20,000
Lay Abstract: N/A

PI: Zheng Liu
Awarded: $20,000
Lay Abstract: N/A

PI: Foyzul Hassan, Co-PI: Bruce Maxim
Awarded: $19,377
Lay Abstract: N/A

PI: Ang Li
Awarded: $18,940
Lay Abstract: N/A

PI: Aditya Raghunandan
Awarded: $19,995
Lay Abstract: Alzheimer’s disease is believed to result from the unhealthy buildup of brain waste, particularly the amyloid-β (Aβ) protein. Aβ accumulates deep in the brain and around arteries entering the brain, but rarely around veins exiting it. This is surprising because it was recently discovered that clean fluid enters the brain along arteries, collects waste like Aβ, and exits through veins. Why would waste build up where the fluid is cleaner and not where it is dirtier? We hypothesize that fluid flow could explain why. Since flow puts stress on proteins, it can alter their folding pathways and buildup, similar to blood clotting. Specifically, pulsation occurs around arteries but not veins, which may dictate how Aβ accumulates. To test this, we will create a lab device that simulates both steady and pulsating fluid flows with Aβ and use fluorescent dye to measure the buildup. Previous studies have only examined Aβ buildup in steady flows. We will instead emulate realistic flow and vessel conditions based on measurements from live mice and measure the buildup of two forms of amyloid, Aβ40 and Aβ42. We believe pulsation affects them differently: Aβ40 tends to accumulate near arteries where pulsation is strong, while Aβ42 builds up deeper in the brain where pulsation is absent. If pulsation influences buildup, drugs might be designed to counteract it or target
regions with stronger pulsation. Our device could also be used to study other disease-related proteins, such as those linked to Parkinson’s.

PI: Besa Xhabija
Awarded: $20,000
Lay Abstract: N/A

PI: Xin Xia, Co-PIs: Taehyun Shim & Jaerock Kwon
Awarded: $19,978
Lay Abstract: N/A

PI: Daniel Lawson, Co-PIs: Christos Constantinides & Simona Marincean
Awarded: $18,859
Lay Abstract: This research project will enhance the understanding of the role of electronic exchange and correlation effects in functionalized Blatter radicals using advanced density functional theory (DFT) methods. By focusing on the accurate modeling of both idealized and synthesized compounds, the project seeks to drive the synthetic development of Blatter radical chemistry, thereby expanding its potential applications. An organic radical is a molecule with at least one unpaired electron resulting in many exciting electronic and magnetic properties but also typically a very short lifetime. The Blatter radical and its many functionalized variations boast long term stability under ambient conditions. This remarkable stability opens a wide range of applications, including controllable polymerization, electrical storage, photodetectors, liquid crystal photoconductors, and spintronics. Accurately modeling the electronic structure of these radicals is crucial for advancing their synthesis and application. The selection of functionalized Blatter radicals in this work are chosen due to their significance and available experimental data. The study will employ a group of five functional to compute properties including: PBE, B3LYP, BHandHLYP, CAM-B3LYP, and LC-wPBE. By analyzing these properties from pure generalized gradient approximation (GGA) to long-range-corrected hybrids, the project aims to enhance our understanding of the electronic structure and spin characteristics of these radicals along with the impact of long-range interactions on their electronic and magnetic properties. Results from this research will be disseminated via peer-reviewed publications, conference presentations and future external proposals.

PI: Guilherme Vieira Hollweg, Co-PI: Van-Hai Bui
Awarded: $19,377
Lay Abstract: The proposed research addresses critical challenges in optimizing the performance of control algorithms for grid-following (GFL) and grid-forming (GFM) power converters. These converters play a key role in power electronics, enabling the transformation of direct current (DC) into alternating current (AC) for integration with electrical grids and loads. The primary objective of this work is to develop a twostage optimization control framework that enhances the reliability and performance of low-level control strategies for DC-AC converters across a range of operating conditions. Current optimization methods usually rely on meta-heuristic techniques that provide efficient but suboptimal solutions due to their stochastic nature. To overcome this limitation, the proposed framework will combine offline meta-heuristic optimization with Bayesian Optimization (BO) in order to better tune the control system. The first stage will leverage meta-heuristics to identify a near-optimal solution, which will then serve as an input for BO in the second stage, ensuring global optimization and robustness under weak grids operation. This novel optimization approach is expected to enhance fault tolerance, system reliability, and power conversion efficiency, making it highly suitable for power grids with varying conditions, from strong grids to very weak grids. The final system will be tested in a controller-hardware-in-the-loop (C-HIL) environment.

PI: Rafael Ruiz, Co-PI: Caymen Novak
Awarded: $19,961
Lay Abstract: N/A

PI: Anna Muller
Awarded: $7,000
Lay Abstract: This project explores the unique yet broadly resonant experiences of Kasia Korzeniecka, a Polish painter of Georgian descent, who grew up in the socio-historical landscape of communist Poland in the 1980s. Through an interdisciplinary approach, combining history, sociology, and art, the study examines the cultural dynamics of identity formation in post-war Poland. It specifically focuses on how Korzeniecka’s family history—marked by a forced migration from Georgia after the Bolshevik takeover in 1917—and the stories, traditions, and artifacts passed down during her upbringing shaped her artistic perspective and sense of belonging. The study also delves into the material history of objects from Książ Castle, which became part of Korzeniecka’s everyday life and were recently discovered to have origins in German heritage, illuminating broader themes of cultural appropriation and post-war transformations. Archival research in Poland and Georgia will provide insights into her family's migration story, while cultural analysis of Georgian-Polish narratives from the communist era will reveal how these stories influenced Korzeniecka’s social imaginary. The project aims to produce a blog documenting the research journey, an academic article on immigrant identity formation, and a collaborative exhibit and art book with Korzeniecka. By exploring the intertwining of personal and collective histories, this research contributes to a deeper understanding of how heritage, memory, and material culture shape identity in post-war societies.

PI: Natalie Sampson
Awarded: $10,000
Lay Abstract: The proposed project, Badania do Działania ("Research for Action"), will build upon the existing DigiClimAct initiative, aimed at enhancing climate change adaptation in the South Baltic region in Poland, Sweden, and Lithuania with leadership for this DigiClimAct in  Gdańsk, Poland. In particular, Poland is home to 36 of the 50 most polluted cities in the EU, with Gdańsk among the EU cities facing significant environmental health challenges in our changing climate. This collaboration with Gdańsk University of Technology’s Digital Architecture Lab will allow me to bring fresh perspectives from environmental public health and cross-disciplinary methods to engage youth (ages 18-24) in climate adaptation efforts. Over the course of spring and summer 2025, we will conduct 3-4 focus groups involving youth from Gdańsk, Poland and other South Baltic communities, discussing their experiences, knowledge, and concerns about climate change, as well as their reaction to digital tools from the DigiClimAct initiative for understanding potential issues and solutions. Findings will be synthesized into multimedia materials—fact sheets, multimedia, and visual art—to communicate these insights to both local and international audiences. By involving youth in the process, the project will empower them to contribute to the ongoing climate adaptation discourse. Through collaboration with community leaders, local organizations, and Gdańsk Tech scholars, this initiative aims to deepen the understanding of youth-driven climate adaptation strategies and inform local policy. Additionally, the project will foster international connections by sharing findings with other youth-focused initiatives, advancing global learning opportunities, and contributing to the wider conversation on climate resilience.
 

Team: Antonios Koumpias & Wencong Su
Awarded: $28,500
Lay Abstract: N/A

Team: Caleb Siefert, Areen Alsaid & Abdallah Chehade
Awarded: $29,170
Lay Abstract: "Advances in Artificial Intelligence (AI) and Natural Language Processing are expected to produce innovative applications for mental health and psychological research (Graham et al., 2019; Koutsouleris et al., 2022). Currently, adoption of such technologies is limited. Proponents of AI attribute this to an absence of data for training AI models and uncertainty regarding current Natural Language Processing models’ capacity to infer meaning from complex narratives (Minerva & Giubilini, 2023). The proposed project seeks to address both issues through the systematic development of AI models that can rate narratives using the Social Cognition and Object Relations Scales - Global Rating Method. If AI models can be developed that rate narratives in a manner similar to expert clinical psychologists, it would support the utility of current Natural Language Processing models for mental health applications. If models are only partially successful, it will clarify facets of Natural Language Processing that need to be improved prior to advancing such applications. Beyond treatment applications, AI models for rating narratives will vastly increase the efficiency in this domain of psychological research. At this time, narrative research requires expert coding that can take years. AI models have potential to reduce this time requirement down to days, vastly improving efficiency. The proposed project will generate preliminary data and trained (and confirmed) AI models that will be leveraged to pursue external funding. Dr. Siefert is in possession of over 1,400 expert-rated narratives, giving us a competitive advantage over other investigators in this area when pursuing funding."'

PI: Areen Alsaid 
Co-PIs: Feng Zhou & Yi Lu Murphey
Awarded: $19,838
Lay Abstract: N/A

PI: Jaerock Kwon
Awarded: $20,000
Lay Abstract: "The new industry of highly automated mobile robots, including autonomous vehicles, is in high demand for skilled engineers. Engineers for the industry require interdisciplinary knowledge and skillsets, including basic programming skills, electric circuitry, robotic kinematics, machine learning, and Artificial Intelligence (AI).
Academia has been trying to respond to the high demand, and there have been efforts to integrate the interdisciplinary knowledge of highly automated intelligent systems, including autonomous vehicles, into their curricula. The integration of the new skillsets or restructuring of the existing curricula is, however, a very challenging task. Some efforts have been made by introducing a small-scale (1/24th, 1/16th, or 1/10th) vehicle to teach the relevant knowledge and skillsets and train researchers and engineers. The MIT RACECAR, F1TENTH, MuSHR, Go-CHART, Dockiebots of Duckietown, and Donkey Car are part of the efforts. A major limitation of the current approaches is in the following two dimensions: (i) The lack of reproducibility owing to heavy craftsmanship requirements due to extensive modifications of the vehicular platform that include removal and replacement of motors, installation of a new ESC (Electronic Speed Controller), custom Printed Circuit Boards, etc. (ii) The restricted onboard processing capabilities due to the platform size (1/24th scale two-wheel or four-wheel differential driving and 1/10th scale Remote Controlled (RC) car). To overcome these major limitations, this project brings forward an innovative idea of building a 1/4th scaled vehicle without extensive modification and providing full-stack software for AI-based perception, planning, and control."'

PI: Simona Marincean
Co-PI: Marilee Benore
Awarded: $19,883
Lay Abstract: "Riboflavin is an essential, water-soluble vitamin from the B family characterized by two distinctive moieties: flavin and ribityl tail, which have contrasting polarity and structures. Dietary riboflavin is required to meet the metabolic needs of many species and has a critical role in metabolism as the precursor to the active forms flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD). Riboflavin levels have been linked to health issues such as growth delays, brain development, and cancer. A simple, facile detection of riboflavin deficiency or accumulation in biological samples is possible with an in-depth understanding of its transport protein, riboflavin binding protein (RBP) structure and its functional interaction with the vitamin. This research proposes to synthesize strategically modified novel riboflavin derivatives that will conserve the flavin moiety while will have ribityl-like tails tailored and differentiated by the length of the carbon backbone and the hydroxyl groups’ position and number. The structure/function binding relationship between RBP and the novel riboflavin derivatives will be investigated via fluorescence binding and competition studies.

The results will inform the design of novel molecules with applications as drug analogs or riboflavin detection for nutritional deficiencies in in at-risk populations. Preliminary studies with the synthetic derivatives have demonstrated successful binding to the target protein. The initial results have been shared with the Tech Transfer/Innovation Partnership Services, University of Michigan, a patent attorney has been assigned to this project, and guided the directions and next steps necessary to apply for a patent on the project idea and product(s).''

PI: Peter Oelkers 
Co-PI: Keshav Pokhrel
Awarded: $19,100
Lay Abstract: N/A

PI: Van Hai Bui
Co-PIs: Wencong Su & Jian Hu
Awarded: $15,340
Lay Abstract: "Modern power and energy systems are becoming increasingly complex due to the high levels of uncertainty introduced with the integration of distributed energy resources (DERs) and electric vehicles (EVs) with high levels of uncertainty. This complexity presents numerous challenges in the real-time operation of power systems, where response time is crucial for system stability and reliability. Developing mathematical models for such systems is challenging, leading to the utilization of dynamic simulation for training and testing of optimization models in environments with a high penetration of DERs. However, dynamic simulation is time-consuming and impractical for real-time applications. To address this issue, surrogate models are employed to replicate behaviors of simulation models, significantly reducing simulation time. Nevertheless, inaccuracies in the output from surrogate models may have adverse consequences for the system operation, including inefficient resource utilization, decreasing power supply reliability, and/or even system instability. To mitigate this issue, this proposal introduces a novel surrogate model integrated with an optimization framework to enhance the performance of the entire system. In contrast to conventional surrogate models that provide deterministic outputs, the proposed model can estimate a probabilistic output to improve the model accuracy. The integration of the proposed surrogate model with an optimization strategy results in the formation of an advanced stochastic optimization framework to achieve operational objectives in power and energy systems. The utilization of the proposed optimization framework can be applied to various applications, including system behavior estimation, power converter design, optimal power flow, and preservation of privacy in energy transactions."

PI: Hafiz Malik
Awarded: $19,915
Lay Abstract: "Voice-activated devices and services (VAD/S), including Amazon Alexa, Google Home, and Apple Siri, are increasingly integrated into our everyday routines. These VAD/S-enabled voice biometric systems cater to a diverse array of needs, encompassing everything from financial transactions, eCommerce, to healthcare information access. However, the advent of AI-fortified deepfake technologies coupled with technological progress in audio processing represent substantial threats to voice biometric systems. An illustrative threat model for VAD/S systems, depicted in Figure 1, provides a logical representation of AI-impersonation (2) and replay attack (3). The overall objective of this project therefore is to safeguard VAD/S and voice biometric-based access control systems, such as speaker verification systems, from impending AI-impersonation attacks."

PI: Armagan Bayram
Co-PI: Zhen Hu
Awarded: $19,377
Lay Abstract: "The lifeblood of American commerce, inland waterways transport billions of tons of goods annually. Yet, these critical networks face a hidden vulnerability: locks are prone to failures and require timely maintenance. These disruptions spread across the network, causing economic losses and impacting interconnected infrastructure such as traffic flow and supply chains. This project addresses this challenge by developing a novel framework for optimizing lock maintenance scheduling by minimizing maintenance costs while keeping network disruptions to a minimum. We achieve this by: i) quantifying network-wide impacts: we measure the true financial consequences of lock failures and maintenance, considering not just the immediate cost but also the domino effect on the entire network, and ii) making uncertainty-aware scheduling: We make smarter decisions under the uncertainty of lock failures. By predicting potential issues and proactively addressing them, we minimize disruptions and costs. Optimized lock maintenance in inland waterways promises several benefits. Firstly, minimizing disruptions through optimized scheduling directly reduces economic losses for businesses and communities that rely on the smooth flow of goods through inland waterways. Secondly, proactive maintenance ensures locks are in good condition, enhancing network resilience and preventing unexpected failures that can trigger huge disruptions. Finally, considering the network-wide impact of maintenance decisions allows for efficient resource allocation, maximizing the benefits of available resources across the entire system. Ultimately, smarter lock maintenance means smoother waterways, contributing to a stronger, more efficient economic state."

PI: Taehyung Kim
Awarded: $14,926
Lay Abstract: N/A

PI: Krim Lacey
Awarded: $9,160
Lay Abstract: N/A

Team: Gengxin Li,  Jian Hu & Zhi "Elena" Zhang
Awarded: $29,865
Lay Abstract: N/A

Team: Christopher Pannier, Krisanu Bandyopadhyay & Pravansu Mohanty
Awarded: $30,000
Lay Abstract: N/A

PI: John Abramyan
Awarded: $24,167
Lay Abstract: "In most vertebrates, the embryonic mouth begins as a shallow cavity in the developing face, which eventually connects to the digestive tract. In contrast, the developing salamander mouth is filled with a cellular mass, effectively plugging its opening, and clearing only after the animal hatches from the egg. The relationship between these two modes of development, as well as the underlying molecular mechanisms, remains poorly understood. Based on preliminary data from our laboratory, a working hypothesis proposes that the cells filling the mouth in the salamander embryo arise from the developing gut and eventually migrate away in order to cavitate the mouth. This project seeks to apply molecular methods to characterize the formation of the embryonic mouth in the Mexican axolotl (Ambystoma mexicanum), an aquatic salamander species that is commonly used in laboratory research. Three aims will be accomplished with this work: (1) identification of the various cell/tissue types comprising the developing salamander mouth, (2) characterization of the cellular and genetic processes that lead to the breakdown of the cellular plug in the developing mouth, (3) labeling and tracking the cells within the plug in order to ascertain their eventual fate once they break away from the embryonic mouth. The overarching intellectual merit of this project is an increased understanding of the evolutionary changes in the embryonic development of the vertebrate mouth. This project will also introduce a new research directions to the UM-Dearborn campus, bringing with it new tools, skill sets, as well as opportunities for students and faculty."

PI: Francine Dolins
Awarded: $24,999
Lay Abstract: N/A

PI: Fred Feng
Awarded: $24,984
Lay Abstract: "The main aim of this project is to develop an open-source and scalable platform for analyzing and publishing bicycle traffic data, which offers a community-engaged, data-driven approach to sustainable transportation planning and benchmarking. Specifically, we will (1) collect preliminary bike traffic data at multiple locations with a variety of bicycle infrastructures (e.g., protected bike lanes) in March-November 2023, (2) develop a free, open-source, and scalable bike traffic data platform (based on client-side JavaScript and data visualization library D3js) that enables local communities to easily publish their own bike counting data, (3) engage with local communities (e.g., Bike Dearborn) and use iterative design to support their own bike traffic data needs, we will also engage with local stakeholders and city planners/officials (e.g., Ann Arbor Downtown Development Authority) to support data-driven decision making in bicycling infrastructure justification, planning, and benchmarking at local levels, and (4) connect with other U-M researchers with complementary expertise, such as traffic optimization, civil engineering, and urban planning, and discuss novel and competitive project ideas for large external grants. If awarded, we expect this project will help to place the PI in a strong position to pursue large external grants on the topics related to sustainable and equitable transportation modes."

PI: Alireza Mohammadi
Co-PI: Hafiz Malik
Awarded: $23,354
Lay Abstract: "Industrial robots, which form the backbone of the modern manufacturing systems, are networked devices which can be specifically targeted for cyber-attacks. The cyber-attackers targeting industrial robotic systems might have various motivations ranging from stealing intellectual properties to causing interruptions in the production line. Successful cyber-attacks against industrial robotic systems can impose long-lasting negative impact on the manufactured items and/or create safety issues in the modern factories where humans and robots work in proximity to each other. This project aims at developing cyber-physical attack generation and identification tools/algorithms for protecting industrial robotic systems against an ever-increasing horizon of emerging cyber-threats, which form the backbone of the manufacturing industry for various applications ranging from welding, pick-and-place tasks, painting, to assembly. We will devise a variety of algorithms that leverage the available tools in control systems design for robotic systems, the theory of unknown input observers, and physical fingerprinting techniques for cyber-physical systems. The developed algorithms will be used to scan a given robotic system for cyber-vulnerabilities, to understand the physical impacts of a successful cyber-attack on the manufactured items and the robotic system itself, and to devise real-time monitoring algorithms for protecting the networked robotic devices in industrial and manufacturing settings. All of the developed algorithms will be tested on digital twins of the robots as well as miniature replicas of industrial robots."

PI: Zheng Song
Awarded: $24,971
Lay Abstract: N/A

PI: Sonia Tiquia-Arashiro
Co-PI: Xiaohua (Shannon) Li
Awarded: $15,000
Lay Abstract: "Metal contamination is one of the global environmental concerns as they pose risks to human health. Because metal ions are non-degradable and remain in the environment. The remediation of heavy-metal-contaminated water is essential as heavy metals persist and do not degrade in the environment. Microorganisms provide good option for remediation of metals. However, identification of microbial strains with high metal uptake capacity and specificity is a key aspect and therefore the isolation of autochthonous microorganisms from contaminated sites such as Saint Clair River in Michigan is an interesting option to obtain metal-resistant strains. Moreover, key knowledge gaps and practical challenges also need to be addressed to advance the remediation technology. This proposal will evaluate the potential of metal-tolerant bacterial strains isolated from Saint Clair River to remove metals from water contaminated with metals. These bacterial strains will be characterized for their attributes with significance for metal bioremediation, namely biosorption characteristics, biosorption capacity and mechanical stability. Additionally, potential mechanisms of biosorption will be studied by Fourier transform infrared spectroscopy (FT-IR) spectroscopy and advanced bioimaging studies using Scanning electron microscopy energy dispersive X-ray microscopy (SEM-EDX) and transmission electron microscopy energy dispersive X-ray microscopy (TEM-EDX). to understand and the impact of metals of structural and compositional changes in the cell and cell morphology and the locations of the absorbed metals.

The research described in this proposal is particularly new to the PI and will open a new area of programmatic research that has potential to significantly enhance the PIs research program at UM-Dearborn. This proposal will expand the PIs expertise to include advanced biological imaging and will allow her to answer important biological questions crucial in linking molecular-scale information to whole-cell, systems-level understanding. The data obtained in this study will be useful for the development of bioremediation agents to treat water contaminated with metals and the advancement of remediation technology with microorganisms. This grant will provide an opportunity for the PI generate preliminary data for external funding to support her research ambition. The proposal has potential fit for the United States Environmental Protection Agency (USEPA) Grants under several research areas including, Ecosystems Grants, Water research Grants, and Health Research Grants, all of which support the establishment of a new research direction or an innovative expansion of current work."

PI: Besa Xhabija
Awarded: $25,000
Lay Abstract: "Cancer is the leading cause of death globally and the second most common cause of death in the US. A substantial downside of the current disease treatment interventions are the associated treatment-related side effects, off-target drug effects, or drug resistance. As a result, the modern researchers are working to develop new, effective therapies with low or no toxicity in normal cells.

Natural products (NPs) are naturally occurring compounds that have biological activity. An abundance of the therapeutic drugs that are currently used clinically today to treat cancer are derived from natural resources and it is estimated NPs have been responsible for the discovery, development, and approval of more than a quarter of the newly approved anti-cancer drugs in the past 20 years. Moreover, NPs have potential synergistic role when combined with the current therapeutic treatment. So, it is important to further explore the antitumor activity of NPs which may contribute to the future of drug discovery and design.

Spinosin A (SPA) is a natural substance isolated from Saccharopolyspora spinosa bacterium. A cancer drug candidate screening of NPs discovered SPA to have strong anti-tumor properties by significantly inhibiting breast cancer cell proliferation. SPA does so by binding and activating the tumor suppressor, arginosuccinate synthase (ASS1). Since, a downregulation of ASS1 is a common feature of in many human cancers, especially in melanoma cancers, targeting it and increasing its activity utilizing SPA, may provide the first evidence that SPA can serve a potential therapeutic drug for melanoma cancers and could offer direct translational relevance in cancer research."

PI: Xuan Zhou
Co-PIs: Lei Chen & Ya Sha (Alex) Yi
Awarded: $25,000
Lay Abstract: "Batteries are one of the key technologies in modern society. They have found ubiquitous applications in various aspects of our daily lives from consumer electronics, electric vehicles, renewable energy storage and even to the aviation system. The pursuit of higher energy density and safety has driven batteries from using the traditional liquid organic electrolytes to all solid-state ones. Lithium, the lightest metal and the strongest reducing agent in the world, is considered as the ultimate negative material for li-ion batteries. Nonetheless, the formation of lithium dendrites, which are metallic microstructures that form on the surface of lithium negative electrode during the charging process because of unevenly distributed current, could shorten the positive and negative electrode and cause catastrophic consequences. Solid electrolytes are expected to prevent the penetration of the dendrites. However, the micron or sub-micron sized cracks formed during the battery assembly or induced by battery long time cycling, make this issue still unignorable. To tackle the above-mentioned problems, the objectives of this project are set to (1) fabricate strong lithium oxides based thin film solid electrolytes by using physical vapor deposition (PVD) technique, (2) characterize the properties of the fabricated solid electrolytes including the ionic conductivity, hardness, microstructure etc., (3) measure the battery properties such as capacity, lifespan through battery assembly and testing, (4) build a physics based electrochemical model to simulate the nucleation and growth of the dendrite and study its formation mechanism and (5) utilize the generated data through this project as preliminary results to pursue external funding."

PI: Jorge Gonzalez del Pozo
Awarded: $8,392
Lay Abstract: "It is now more than ever essential to revisit colonial and post-colonial relationships through cultural representations of identity. As national literatures evolve, there is an increasing interest in the Spanish intertwined history with the Philippines which is seeing a revival in the contemporary cultural scene. Philippine literature written in Spanish is a historical seminal part for the Philippines and the need to preserve it, as well as the current attention to these manifestations, has not been widely studied. One of the main goals of this research project is to understand better this reality and disseminate Hispano-Philippine literature in Spain, its main audience outside the Pacific. This project is based on a solid historical and panoramic study stemming from the archival work, so the resurging contemporary Hispano-Philippine literature can be analyzed and interpreted.

My project aims to analyze Spanish literature in the Philippines through the texts by looking at its multiple dimensions beyond the fiction to unpack the symbolism and representation of these distant relationships. By analyzing these works of art, I aim to achieve two main overarching goals: (1) to explore specifically how Spanish literature created in the Philippines is in direct dialogue with both national identities, and (2) to broaden the scope of Hispanic studies by examining how the Pacific archipelago practices and features in their fiction operate as a vehicle for expressing social, political, and cultural concerns. Particularly, this study will allow me to develop: a) 2 articles on the topic and, more robustly, b) an edited volume of essays on the topic."

PI: Nadja Rottner
Awarded: $14,999
Lay Abstract: "The artists under discussion in Geometry Unbound, namely Diego Barbosa, Milton Becerra, Sigfredo Chacón, Eugenio Espinosa, Héctor Fuenmajor, Yeni & Nan, Claudio Perna, Antonieta Sosa, Pedro Terán, Luis Villamizar, and Alfred Wenemoser situate their ephemeral geometries on malleable fabric, nets, rocks, earth, streets, grass, trees, or rivers. By integrating abstraction into different public spaces such as traffic, parks, busy plazas or natural reservoirs, they programmatically break away from established modernist traditions of geometric abstraction when painting and sculpture were bound to a single material support, conceived of as a limited and definite stable, autonomous and universal surface separate from life. These artists refuse to be tied into a governmental cooption of Venezuelan abstract and kinetic art conceived of as universalist art of modernist internationalism, abstraction becomes local, performative, and contingent. Instead, the placement of the line, circle, triangle, or the grid in different local contexts acts as a signifier that offers profound insights into the official hegemony of culture and a mismanaged state between 1969 and 1981. Geometry Unbound demonstrates how the gendered, socially stratified, and indigenous body enters into abstraction as a personal instrument of corrective choice. In doing so, these artists do not conform to well-worn modes of art as political protest, institutional critique, or radical subversion—after all, Venezuela, unlike Brasil, Argentina, or Chile, was not under dictatorship in the 1970s—and yet there is criticality and agency to be found in an analysis of art in-between dictatorial regimes."

PI: Adam Sekular
Awarded: $15,000
Lay Abstract: "Over two weeks, enthusiasts and new players of Old Time Music gather in Clifftop, West Virginia to reconvene a perennial community, The Clifftop Old-time Music Festival, in an evocation to the music’s trance-like, mystical power. This immersive documentary looks at this gathering from the inside, embedding with a small group of participants over the course of the entire festival."

Team: Pam McAuslan, Sridhar Lakshmanan & Marie Waung
Awarded: $30,000
Lay Abstract: "This research fits at the intersections of the fields of psychology and engineering. Using observational, survey, and experimental research applied to vehicle design we will examine a variety of design elements and their impact on trust and perceptions of the vehicle’s intentions. The application of this research is wide ranging from assisting hearing impaired pedestrians to adding protection to delivery vehicles. This work may be applied to vehicles with varying levels of autonomy. Findings on human reactions to design features and signals emitted from the vehicle may be used to design vehicles that in some cases facilitate driver and pedestrian safety and in other cases repel humans from the vehicle (e.g., offer protection for delivery or military vehicles). The proposed work will allow the principal investigator and her multi-disciplinary collaborators to build on their existing research collaboration, and package the results into three proposals. These proposals will be submitted to three different sponsors, each with a differing viewpoint on the underlying research."

Team: Jacob Napieralski, Natalie Sampson, Ulrich Kamp, & Stuart Batterman
Awarded: $29,764
Lay Abstract: "This proposed planning project would engage faculty and students across disciplines in new and sustained community-academic partnerships with the Southwest Detroit Community Benefits Coalition (CBC) to examine an overlooked environmental health issue: fugitive dust. Common in communities near industry, materials handling facilities and transportation routes, fugitive dust is unconfined airborne particles that can contain toxic pollutants such as lead, cadmium, and PCBs. When inhaled, fugitive dust can aggravate and cause serious illness. Our team of community leaders with faculty and student scholars from public health, engineering, environmental sciences, and geography will measure the migration of fugitive dust using X-Ray Fluorescence (XRF) instruments occurring near a local waste recycling/processing facility in Detroit’s Delray and Carbon Works neighborhoods. Findings will be shared with multiple audiences in plain language English and Spanish to inform ongoing discussions with local decision-makers related to zoning and related ordinances. Given the success of the CBC and researchers on our team, we are confident this preliminary work would prepare us well for pursuing additional funding through the U.S. Environmental Protection Agency and/or foundations to: 1) develop more detailed longitudinal research studies to understand the health implications of industrial fugitive dust, 2) build community capacity to identify exposure patterns and mitigation strategies to improve environmental health and justice with policymakers and industry, and 3) build campus capacity with new opportunities for students in action-oriented research related to campus priorities of Diversity, Equity and Inclusion; Sustainability; and Mobility and Infrastructure."

Team: DeLean Tolbert Smith, Christopher Burke, Georges Ayoub, Sarah Nesbitt
Awarded: $29,906
Lay Abstract: "Providing high-quality teacher development in engineering design is a diversity, equity, and inclusion concern. We know that students of color in urban school districts are often missing out on key courses that act as gatekeepers to later STEAM careers. Many school districts, especially, those serving Black and Latinx communities, struggle to recruit and retain STEAM competent and confident teachers. Our local community and society at large sorely miss out on the talent and innovation that could come from these students if they had access to high-quality STEAM curricula and teachers. The Human-Centered Engineering Design and Department of Education are working with Detroit Highschools who have a curricular emphasis on Engineering and Engineering design. To organize summer professional development workshops. The purpose of these workshops is to train educators in using design thinking/human-centered design tools to prepare high school students for university engineering education. Furthermore, the workshop will be used to create a database of pre-college course materials and potential engineering education and design education research questions and research study designs. We are seeking funding that could support the faculty's summer effort and related fees for a Summer 2022 series of workshops. Through an existing relationship with The School at Marygrove, we identified an expressed need. The school's leaders and teachers desire and have requested access to resources and training to increase the quality of their instruction and delivery of engineering design-related topics. After this pilot year, we anticipate securing external funding to sustain this program for the years to come."

PI: Alan Argento
Co-PIs: Gargi Ghosh, Wonsuk Kim
Awarded: $25,000
Lay Abstract: N/A

PI: Jie Fan
Awarded: $22,856
Lay Abstract: "Metastasis is the major cause of cancer morbidity and mortality. It happens when the pre-malignant cancer cells detach from the primary tumor, permeate into the blood circulation, and spread to secondary tissues. It has been challenging to stop this process because it is unclear about the mechanism of cancerous cell detachment and its interaction with the vascular cells. The cell has been recently discovered processing intrinsic handedness, termed as cell chirality. For instance, we have found vascular cells are strongly right-handed (or clockwise), which greatly contributes to the integrity of the blood vessel wall, preventing cancer cells from permeating through. More interestingly, we found the breast cancer epithelial cells with pre-malignant oncogenes overexpression, process an opposite cell chirality compared to the normal cells. However, very little has been known about the roles of either cancer or endothelial cell chirality in the critical steps of metastasis. With the unique bioengineering tools developed to assess cell chirality in our lab, we will first investigate the detachment of pre-malignant cancer cells caused by its reversed cell chirality; secondly, we will develop a co-cultured model to study the cell chirality induced leakage of vascular barrier with and without the physical interaction of cancer. If we succeed, we can provide novel preventive and therapeutic insights to treat metastasis by regulating the chirality of cells."

PI: Zhen Hu
Co-PI: Youngki Kim
Awarded: $21,530
Lay Abstract: N/A

PI: Bochen Jia
Awarded: $14,718
Lay Abstract: N/A

PI: Ulrich Kamp
Co-PI: Samir Rawashdeh
Awarded: $24,985
Lay Abstract: "Climate change is accelerated in mountains worldwide, and most glaciers are in recession resulting in socio-ecological challenges for both highland and lowland communities. Monitoring glaciers helps in understanding recession characteristics. Unmanned aerial vehicles (UAVs, or ‘drones’) collect important high-resolution information on a glacier, for example, surface elevation and morphology. This project aims to fly drones over glaciers in the Peruvian Andes and compare data with heritage aerial photographs and digital elevation models (DEMs) to estimate glacier extent and volume changes. As flying drones in mountains is challenging, we first test a commercial drone and a hybrid airship, built by our partner at Ohio State University, in the M-Air outdoor flying lab at the UM-AA campus. We make necessary modifications and 3D-print parts that improve flight performance. We expect to find significant glacial changes during the 90+ years between 1931 and 2022. Results provide new, more precise information on glacial change, which is valuable to local communities and regional authorities in water resources management. In addition to our research studies, we intend to establish an ‘Office for Drones and GIS’ (ODAG) in Dearborn that serves our campus and wider community, for example, through offering courses in interdisciplinary drone studies and in piloting. The office would also train Peruvian students who intern with the U.S. Geological Service Volcano Disaster Assistance Program."

PI: Taehyung Kim
Awarded: $15,083
Lay Abstract: "This project seeks to develop a new switching vector transition control to increase the efficiency of onboard permanent magnet (PM) AC motor drives in HEVs (hybrid electric vehicles) and PHEVs (plug-in hybrid electric vehicles), which in turn will improve a vehicle's fuel economy. The conventional three-phase PMAC motor drive activates all three phases, operating three switches in a six switch inverter. In this project, a switching vector transition control, activating two phases and two switches in a speed range from zero to medium, is proposed to decrease switching power losses in the PMAC motor drives. With the advantage of less switching power losses, a smooth transition between two modes and a commutation torque ripple minimization scheme will be investigated in order not to sacrifice performance of the drive. Through this project, the switching power losses of PMAC motor drives can be reduced significantly. The proposed technique can be applied to all on-board three-phase PMAC motor drives which use the field oriented control including a traction motor drive. Only a control software algorithm modification is required without any additional hardware component or structure modification to implement the proposed scheme (the proposed feature can be added at no extra cost). The reduced switching power loss will enhance the efficiency of each electric motor drive (up to 1.5%) which, in turn, will contribute to improve a vehicle's fuel economy."

PI: Suvranta Tripathy
Co-PI: Kalyan Kondapalli
Awarded: $24,478
Lay Abstract: "Motor proteins are molecules that transport cargos within the cells along tracks or highways of the cell known as microtubules. Transport of cargos to correct destinations within the cells at the appropriate times is critical for cellular function. A major problem at the cellular level in neurodegenerative diseases such as Alzheimer’s is defective functioning of these motor proteins resulting in severe traffic jams and accumulation of toxic products. While multiple studies have tried to understand how these motor protein’s function, the majority of these studies were conducted in artificial environments that tried to mimic the cellular environment. However, the cellular milieu is complex and crowded with various macromolecules, which potentially regulate various aspects of the transport by the motor proteins. Current theoretical models can’t therefore fully explain the transport observed in cells, which contain a dense and highly diverse pool of macromolecules (a phenomenon known as macromolecular crowding).  Lack of information about how macromolecular crowding regulates motor transport function on highways in the cell is a major gap in our field. The proposed research involves experimentally determining the biophysical properties such as force generation of a single motor protein kinesin in a physiologically relevant crowding medium using single molecule force spectroscopy technique of “Optical Trapping/Tweezers”. The outcomes of this work will be used to develop theoretical models and will be applied to better understanding of the mechanisms of cellular processes related to Alzheimer disease."

PI: Zhi "Elena" Zhang
Co-PI: Jie Fan
Awarded: $25,000
Lay Abstract: N/A

PI: Francine Banner
Awarded: $14,792
Lay Abstract: "In January 2021, as years of incendiary rhetoric culminated in a dramatic insurrection at the U.S. Capitol, questions of accountability abounded. As the former President faced impeachment, the list of others who might have enabled or encouraged insurrection was long. Some questions raised were legal in nature. Legal questions, however, were intertwined with moral ones: How much encouragement must a person have provided to have facilitated the downfall of American democracy? Were those who knowingly engaged in violence more responsible than those who passively went along? Was it possible to redeem oneself after years of ignoring a festering crisis? As the public pondered these questions, complicity was the lingua franca.

My new manuscript, Complicit, argues that we are in a “complicity moment,” when economic, social, and political factors have coalesced to make us more aware—and more fearful—of imminent crises and increasingly suspicious of one another. Accomplice charges are common in law, applied to defendants who assist primary perpetrators. The term, also, is ubiquitous in society. In 2017, complicity was the Dictionary.com Word of the Year, joining gaslighting and xenophobia among the most searched terms online. Today, there is no neutral. Even staying silent can make a person complicit. Engaging legal cases and examples from the pandemic, climate change, racism, and sexual assault and harassment, this book examines why we are so eager to turn on one another and makes a few suggestions as to how we might shift our focus away from blame and toward systemic, structural change."

PI: Yi-Su Chen
Awarded: $15,000
Lay Abstract: "Supply chain disruption or supply chain shortage has become a buzz word ever since the outbreak of the Covid-19 pandemic. Many sectors have experienced shortage, one after another. For example, there was shortage of toiletry products, personal protective equipment (PPE) in the early onset of the pandemic, then lumbers for building houses and canned or processed foods as the pandemic persists, and now chips, to name a few. Widely used in various industries, chip shortages have impacted everyone in multiple aspects: from the obvious such as computers and electronic devices that are proved to be next to essential for education and work continuance, to seemingly unrelated such as agriculture industry where chips are used in farm equipment and machineries, further exacerbating the food shortage problem. Chip shortages cause negative impacts not only on individuals but the greater society and environment such as delayed production of electronic vehicles. Worse, some shortages are caused by hoarding, resulting in the bullwhip effect, a phenomenon that describes the information distortion in the downstream customers gets traveled and amplified through the supply chain. Such phenomenon can be dampened by collaborating and sharing information with business partners.

This research project examines two types of factors that influence cooperating and collaborating behaviors between partners. The first one is how the decision makers’ individual attachment styles affect their cooperating vs. competition behaviors. The other factor regards whether the aforementioned effect is context contingent. This research offers theoretical, managerial, and public policy implications as detailed in the project description."

PI: Danielle DeFauw
Awarded: $9,750
Lay Abstract: "Children’s book author and publisher, Dr. Fatma Al-Lawati and Dr. Danielle L. DeFauw, Associate Professor of Reading and Language Arts, have partnered together to explore the Etisalat Award for Arabic Children’s Literature. The Etisalat Award was established in 2009 by the United Arab Emirates Council on Books for Young People, the national branch of the International Council on Books for Young People. Restructured in 2013 to honor the best text and best-illustrated books created for children, birth to 14 years old, the Etisalat Award highlights the artistic productions created by writers, illustrators, and publishers. Designed to promote Arabic books to children and families, creators are incentivized through the award process to publish high-quality children’s books. Because Al-Lawati and DeFauw value the book award process, they plan to (a) explore how the Etisalat Award helps the writers, illustrators, designers, publishers, and all stakeholders to meet the international standards in producing high-quality children's books; (b) through content analysis, study how the awarded children’s books met the award criteria; and (c) compare and contrast the criteria to other book awards' processes (e.g., Caldecott, Newbery). Although Al-Lawati could translate the 22 children’s literature books purchased June 2021, it is important to have these books professionally translated. The Translator Coordinator of the Society of Children’s Book Writers and Illustrators (SCBWI), Avery Fischer Udagawa, recommended the ArabKitLitNow collective group. Pending grant support, translators from the ArabKidLitNow group have agreed to translate the books. Upon completion of the translations, the research process may proceed and evolve."

PI: Dara Hill
Awarded: $15,000
Lay Abstract: "The project is a documentary short that aims to document the perspectives of Detroit parents, whose school choice decisions were informed by their participation in a social network. The focal parents originated in 2013 as a response to a bewildering educational landscape in Detroit that was plagued by a succession of school closures and proliferation of for profit charter schools. They organized meetings, daytime school visits of Detroit's top schools, and shared information to guide their selection of schools. The project builds on a documentary short I produced, entitled "My Child's K-5 School Journey in Detroit: Voices from the Best Classroom Project," which is being featured in local and international film festivals and at the University of Michigan-Detroit Center in support of Detroit families. The seminal work captured the experiences of four families who represent the broader group who enrolled in 15 Detroit public, private and charter schools while sustaining residency in Detroit and contributing time and resources to promote educational equality in Detroit schools. More specifically, the project aims to document the middle school experiences of the same four focal students, including cross cultural relationships, academic rigor, support of their racialized identities and their agency to promote racial equality in school and society."

PI: Pamela Aronson
Awarded: $11,257
Lay Abstract: "We are in the midst of a gender revolution that is transforming women’s power in their relationships with men and in the political sphere. This study is leading to a book manuscript about recent transformations in gender and politics and the #MeToo movement that are reshaping everyday life. Women and non-binary interviewees are not only redefining their experiences, but, with some men, are creating new expectations of sexual consent. Many women, and some men, no longer tolerate men’s abuses of power in politics or in their interpersonal relationships. Additionally, women seized political power in the 2018 and 2020 elections in unprecedented ways. The people who support these transformations are voting for women candidates, engaging on social media, and making their interpersonal relationships more equal. They are also generating a new language that reflects women’s agency and sense of empowerment. This study thus documents widespread cultural transformations that have taken place in both politics and people’s everyday lives."

PI: John Chenoweth
Awarded: $3,280
Lay Abstract: N/A

PI: David Hill
Co-PI: Stein Brunvand
Awarded: $15,000
Lay Abstract: N/A

PI: Peter Oelkers
Awarded: $6,000
Lay Abstract: N/A

PI: Krisanu Bandyopadhyay
Awarded: $10,000
Lay Abstract: N/A

PI: Marilee Benore
Co-PIs: Amanda Esquivel, Simona Marincean
Awarded: $9,970
Lay Abstract: "The lack of diversity in the STEM workforce and in education is an ongoing problem in spite of attempts to be more inclusive. Studies document the negative economic impact and lack of research progress created when a disturbingly large number of capable individuals are shut out or lost from opportunity. Statistics from international sources paint a dismal picture of the number of women leaving STEM careers. Similarly, the dearth of minority professionals in STEM, while most profound in the United States due to systemic inequities in education and policies, is at a crisis point.

Our hypothesis is that the COVID crisis has been significantly more devastating to the careers of women and young professionals.  The “pipeline loss” is a trend consistent across all women in STEM. This crisis will exacerbate the loss of STEM researchers due to support decrease, lack of childcare, inability to continue research, increased family obligations, and loss of networking opportunities.

The purpose of this study is to create, distribute and analyze a survey asking the questions that will clarify the negative impact of COVID on the careers of women (and men) in STEM. The results will bring insight to UM-Dearborn, aid in developing solutions, and retain faculty and students who need to guide our campus, the community and state to better futures."

PI: Sang-Hwan Kim
Co-PI: Kyungwon Lee
Awarded: $9,928
Lay Abstract:  "Urban Air Mobility (UAM) is on-demand air transportation within core urban areas using new, electric-powered, vertical takeoff and landing (eVTOL) aircraft, like a "flying car." These vehicles are one of the most exciting emerging technologies today because they can create new urban transportation options and change how people travel to and around cities. For this reason, many research organizations and industries have started developing UAM and its environment. However, there is a lack of studies on the UAM in terms of human-centered design and in the Detroit area, which is called "Motor City.”

In this regard, the current study is to examine: the design features of UAM in terms of human factors and user experience; usage models and trust/acceptance models; and research collaboration models within the automotive industry in the Detroit area. The research efforts and findings of the current study will serve as a basis for further expanded research, along with preparing external grant proposals.

Based on high research demands and the proposers' expertise, it is expected that the proposed collaborative study can be completed successfully, yielding insightful results and further research opportunities. It is also expected that the study ultimately contributes to Metro Detroit's being "Rotor City, "leading UAM businesses as the city of next-generation transportation."

PI: Kalyan Kondapalli
Awarded: $10,000
Lay Abstract: N/A

PI: Christopher Pannier 
Co-PIs: Paul Draus, Jacob Napieralski
Awarded: $9,996
Lay Abstract: N/A

PI: Carmel Price
Co-PIs: Sally Howell, Fred Feng, Sara Gleicher
Awarded: $9,968
Lay Abstract: "We envision a transdisciplinary space at UM-Dearborn that will use an innovative approach to mobilize researchers, students, policy makers, community members, faith leaders, business and civic representatives, to investigate and then plan, implement, and evaluate actions that reduce disparities to improve health. Through unique community-academic partnerships, this space will focus on addressing structural and racial inequities to make access to healthy environments more equitable, communities more engaged, and, health practices and polices more culturally responsive and effective to eliminate health disparities and achieve health equity in Dearborn, Detroit, and beyond. Because health inequity is a complex problem that requires careful investigation from multiple perspectives, participants will use a multi-faceted approach that integrates research, community participation, education, training, policy and social action. A commitment to health equity and values such as inclusion, respect, integrity, collaboration, rigor, innovation, and social justice, will guide all activities. Community and academic partners will work from shared guidelines to ensure all research is community-centered, equitable, ethical, and action oriented. This project fits the Urban Futures area with secondary areas in Ending Structural Racism and the COVID-19 Pandemic."

PI: Rose Wellman 
Co-PIs: Carmel Price, Matthew Stiffler
Awarded: $9,984
Lay Abstract: N/A

PI: Oleg Zikanov
Co-PI: Cheol Lee
Awarded: $9,992
Lay Abstract: N/A

PI: Georges Ayoub
Co-PI: Christopher Pannier
Awarded: $24,958
Lay Abstract: "Most of the plastic materials that has been produced to date are not biodegradable and, therefore, discards that escape waste-management will persist for centuries and are already causing ecological disruption. These challenges motivate the concept of a circular economy in which polymers can be recycled multiple times and maintain their valuable structural material properties. Therefore, understanding the deterioration of polymers’ mechanical properties in each step of a recycling process is of high priority to guide the design of polymeric materials, their manufacturing processes, and their recycling processes. The advanced manufacturing process Fused Filament Fabrication (FFF) 3D printing achieves material saving by avoiding off-cuts when compared to machining and 3D printing also offer design freedom not found in the alternative process of plastic injection molding. Through design freedom, lighter-weight parts can be produced for the same structural application. The goal of this proposal is to give a second life for the non-biodegradable polymers our society disposes of every year. To tackle this important environmental issue, the research objective is to investigate the effect of repeated recycling on the static mechanical properties of a semi-crystalline polymer (PLA). A polymer recycling research capability will be initiated at UM-Dearborn. Building upon the knowledge of degradation mechanisms, this project will test multiple-times recycled PLA in the injection molding and FFF processes and build a database on the mechanical behavior of multiple times recycled semi-crystalline polymers (PLA) by varying the number of recycling cycles (up to 3 times)."

PI: Marilee Benore
Awarded: $5,900
Lay Abstract: "This proposal aims to solve a problem of significance to health—allowing for inexpensive, accurate and rapid determination of riboflavin in the presence of human samples or other sources. Globally, little is known about riboflavin deficiency or its impact, specifically on women and children who are at risk. We are synthesizing an analogue, which will be detectable by standard assay methods utilizing the biotin avidin system and our protein Riboflavin Binding Protein."

PI: Lei Chen
Awarded: $10,000
Lay Abstract: "The objective of the proposed research is to fundamentally predict and understand the lithium (Li) dendrite growth in the solid-state batteries (SSBs), which offer the potential for improved safety and energy density compared with traditional flammable liquid electrolytes-based batteries. Unfortunately, numerous experimental observations have evidenced Li dendrite penetration inside mechanically hard solid-state electrolytes (SSEs) in particular at higher current density (> 1 mA/cm−2) and the resultant short-circuits severely limit the applicable current density. However, the fundamental mechanism of Li dendrite growth in the SSEs is not well understood. In general, mechanical stress will manifest more acutely in the hard SSEs compared with conventional liquid electrolyte-based batteries. Owing to a low melting temperature of 453K, the deformation of Li metal has been characterized by strain rate-dependent creep flow behaviors. We thus hypothesize that Li metal behave like an “incompressible viscous fluid” when contacting the mechanically hard ceramic SSEs, and the built Li stress further drives the crack growth in the SSEs. To test the hypothesis, we will develop an atomically-informed physics-based microstructure evolution model that explores the Li dendrite nucleation and growth in the SSEs. In-situ and ex-situ experiments will be used for validating the model and physical interpretations. The outcome of this work will advance our fundamental knowledge on Li dendrite growth in the SSE systems. The proposed research will facilitate the development and commercialization of high energy density Li-ion batteries (LIBs) for energy storage applications with improved safety, especially in electric vehicles (EVs)."

PI: Junaid Farooq
Awarded: $10,000
Lay Abstract: "Communication networks are becoming increasingly critical in of our daily lives. Any disruption in wireless and internet connectivity due to natural disasters, failures, or malicious attacks may result in interruption of public services, perturbation of public order, or jeopardy of enterprise operation causing losses to businesses, etc. This research aims to maintain coverage and connectivity in such scenarios using aerial networks that can rapidly be deployed in the air using unmanned aerial vehicles (UAVs) and other flying platforms. Proper deployment of the UAVs is important since there is no backhaul network in the sky. Hence a web of UAVs is required to create a localized internetwork that can also be connected to the global internet from one end. It is challenging to create such networks since the UAVs need to consider a multitude of challenges such as covering ground users adequately, remaining in proximity to other UAVs, and other physical challenges such as battery limitations. This research proposes to use biologically inspired approaches such as flocking of birds, animal foraging, and predator-prey dynamics to design formations in the air that can serve the purpose of guiding the UAVs to independently determine how to position themselves. The project will focus on macro-scale strategies for creating formations in the air that are tailored to users on the ground. These will be designed for various scenarios where the ground users are spread out in the form of communities or are geographically scattered."

PI: Kalyan Kondapalli
Awarded: $17,544
Lay Abstract: N/A

PI: Samir Rawashdeh
Awarded: $14,595
Lay Abstract: "Patients that end up in the ICU for severe lung conditions, whether caused by the Coronavirus Disease (COVID-19), COPD, or others conditions, are discharged as soon as they are stable. However, they require substantial monitoring and care for their condition to continue to improve and to prevent re-hospitalization. We are developing a breathing assessment mask for post-ICU monitoring and care. The mask would be worn for one minute approximately four times per day to assess clinically relevant breathing parameters such as breathing rate, utilized lung volume, CO2 concentration, and volatile organic compounds. These measurements can potentially help clinicians recommend early interventions and outpatient visits before the conditions worsen to the point of requiring emergency room services."

PI: Feng Zhou
Awarded: $27,878
Lay Abstract: "Trust in automation has affective and cognitive components, but affect is the primary determinant of trusting behavior [1]. However, studies of affect on trust in automation are scarce. Therefore, it is critical to examine the role of affect in trust in automated vehicles (AVs). The overall objective of the proposal is to explore the role of affect in trust-based interaction in automated driving, with two aims, i.e., 1) investigate the relations between affect and trust in automated vehicles, and 2) calibrate trust in AVs using affect heuristics. The central hypotheses are that as an affective and cognitive response, affect can foster trust-based interaction to improve the joint performance of the human-AV team. The rationale is that, once the relations between the latent structure of affect in trust-based interaction and trust in AVs are identified, affect can effectively help calibrate driver trust in AVs using affect heuristics.  I am well-prepared to undertake the proposed research due to the successful track records in human factors, affective computing, and driver behavior modeling in automated driving. By linking to the local automotive industry, this is critical to build my research program. It is expected that affect-based trust calibration will significantly improve trust-based bidirectional interaction in automated driving and have positive impacts on the development of AVs and the automotive industry in Michigan and beyond.The proposal is creative and original because it is converging to a holistic approach (i.e., both affect and cognition) to examine the dynamic and complex concept of trust in human-AV interaction."

PI: Pamela Aronson
Awarded: $6,000
Lay Abstract: "We are in the midst of a gender revolution that is transforming women’s power in their relationships with men and in the political sphere. This study is leading to a book manuscript about recent transformations in gender and politics and the #MeToo movement that are reshaping everyday life. Women and non-binary interviewees are not only redefining their experiences, but, with some men, are creating new expectations of sexual consent. Many women, and some men, no longer tolerate men’s abuses of power in politics or in their interpersonal relationships. Additionally, women seized political power in the 2018 and 2020 elections in unprecedented ways. The people who support these transformations are voting for women candidates, engaging on social media, and making their interpersonal relationships more equal. They are also generating a new language that reflects women’s agency and sense of empowerment. This study thus documents widespread cultural transformations that have taken place in both politics and people’s everyday lives."

PI: Doohyun Kim
Awarded: $15,000
Lay Abstract: "In the foreseeable future, internal combustion engines fueled by various types of conventional and renewable fuels will be the primary power source for medium- and heavy-duty commercial vehicles. To comply with upcoming regulations for pollutants and greenhouse gases, major advancement in combustion strategies and aftertreatment system is mandatory. Leveraging the newly built engine testcell in ELB, I propose to develop a comprehensive research program at UM-Dearborn that conducts fundamental investigation on advanced combustion strategies that achieve higher efficiency and cleaner emissions than conventional diesel engine. To prepare the current testcell to perform full emissions measurement and detailed combustion analysis, several critical tasks should be completed, which include building exhaust system with sampling ports for emissions analyzers, installing PM emissions analyzer, setting up a high speed data acquisition system, and building intake air system. The major outcome of this project will be a fully functioning engine testcell with capability to conduct high level research on advanced combustion strategies, which is critical to initiate collaborations with external institutions and to pursue external research projects. I will put particular emphasis on building long-term relationships with automotive industry partners in the field of internal combustion engine, which will strengthen UM-Dearborn students’ career opportunities in the automotive industry. These collaborations will also benefit other powertrain research groups in the College of Engineering."

PI: Krim Lacey
Awarded: $6,000
Lay Abstract: "Intimate partner violence (IPV) remains a public health concern that continues to affect the lives of many individuals regardless of race/ethnicity, sexual orientations, national origins, and age, particularly within college and university settings. Studies show that the rate of intimate partner victimization tends to be higher among young adults, and these cases have gained increased visibility on US college and university campuses. Research demonstrates that victimized individuals are prone to injuries, as well as other health-related outcomes. Documented consequences of intimate partner violence include substance use and misuse, PTSD, depression, suicide, broken bones, back and gynecological problems. Notwithstanding the growing number of research identifying the risks and consequences of IPV, additional studies are necessary to understand intimate partner violence and its impact on college students, a cohort shown to be at increased risk for intimate victimization. The proposed research project seeks to: (1) explore the association between intimate partner violence (emotional, sexual, and physical abuse) and risk/stressors (e.g., discrimination, stress) among college students, and how they might differ by racial/ethnic groups (e.g., White, Black, Hispanic, Asian/Pacific Islander, Other); (2) understand the interrelationship between emotional, sexual and physical abuse among college students in a representative sample; (3) examine group (racial/ethnic) differences in association with mental and physical health conditions and intimate partner violence among college students; and (4) test a mediation model between stressors, intimate partner violence and mental and physical health outcomes using large scale national data (N=67,972) collected on college students across 129 institutions in the United States."

PI: Carmel Price
Co-PI: Natalie Sampson
Awarded: $5,875
Manuscript
Lay Abstract: "Planning and permitting decisions have long perpetuated patterns of environmental racism in the U.S., leading to social, economic, and health inequities. For overburdened communities, this means constant demands on residents to mobilize and engage in agency decision-making to protect community health. Plain language cannot eliminate these patterns and demands, but it is a prerequisite for meaningful participation towards environmental justice (EJ). 

In 2020, the Environmental Health Research-to-Action (EHRA) team joined with the Clear Language Lab (CLL) to identify opportunities for increasing plain language in environmental decision-making processes with the Michigan Department of Environment, Great Lakes, and Energy (EGLE). This resulted in a 45 page, publicly available report based on: 1) CLL’s content review of sample EGLE documents (e.g., public notices, project summaries), 2) EHRA-led focus groups with EJ leaders and legal advocates, and 3) CLL-led focus groups with adult learners. Findings confirmed that documents are perceived as biased, intentionally inaccessible, and unnecessarily technical, without clear directions on how, why, and where the general public can participate. 

At a time when EJ is gaining state- and federal-level attention, findings and sample documents generated through this work are increasingly relevant to policy changes underway. As such, we plan to extend this work nationally. In particular, this project will provide paid mentorship for a student who will learn to conduct applied research with community and agency partners.Through scholarly and public-facing dissemination, we aim to help agencies improve transparency and accessibility towards meaningful public participation, particularly in communities overburdened by environmental exposures."

PI: Charu Chandra
Awarded: $6,000
Lay Abstract: "The proposed research in Augmented Analytics is grounded in two broad areas of data science and business analytics: developing data science and business analytics methodology, models and tools, and the innovative application of proposed methods in any research area. Augmented Analytics invokes multi-disciplinary research in broad areas of machine learning and natural language processing. Implementation of this methodology has the promise of enormous improvements in the quality, relevance and timeliness of complex business decision-making in image, sound, and video data formats. We propose to harness features and capabilities of both Machine Learning and Natural Language Processing to offer an integrated toolbox that enables executing search-based analytics and conversation-based analytics in unison. 

The proposed deliverables are a framework and methodology to formalize augmented analytics for data mining applications in business, engineering, and medical fields. Additionally, the methodology will be applied to the study of such applications in a Capstone Course, where students investigate a real problem with an industry partner. The nature of the proposed research lends itself to STEM classification and it is our intention to incorporate its features in data science and business analytics curriculum at the University of Michigan-Dearborn campus. Our future direction is in-depth research of the role of Deep Learning in Machine Learning Algorithms, especially Neural Networks and Artificial Intelligence in Augmented Analytics."

PI: Lei Chen
Awarded: $6,000
Lay Abstract: "The overarching goal of the proposed research is to develop a data-driven approach that enables efficient and effective microstructure optimization of additively manufactured (AM) piezoelectric composites. Piezocomposites, which consist of a piezoelectric-ceramic phase and an elastic-polymer phase, are emerging flexible piezoelectric materials with high efficiency in absorbing and converting multi-directional mechanical stimuli into electrical signals. The piezoelectric properties are dictated by the microstructure, namely, the morphology of each phase. Conceptually, computational microstructure optimization of piezocomposites involves iterative searches to achieve the desired combination of properties demanded by a selected application. Traditional analytical-based optimization methods suffer from the searching efficiency and result optimality. Moreover, it is challenging to include AM constraints, e.g., the minimum feature size and the porosity in as-built components, in the optimization formations. This research aims to tackle above challenges by developing a novel data-driven approach for microstructure optimization of AM piezoelectric composites. The proposed data-driven approach leverages (1) extensive physics-based simulation data and (2) limited amount of measurement data from AM process. The use of data-driven model for microstructure optimization is an innovative concept that will enable to reduce the dimensionality of the problem space, and dramatically accelerate the microstructure optimization of AM piezocomposites. If funded, the “initiation” grant will help PI get this project off the ground and generate preliminary data that will support the development of competitive research proposals. In the long run, the outcomes will also lead to technological advances in numerous areas, including energy harvesting devices, flexible electronics, wearable sensors, strain-tolerant hydrophone, etc."

PI: Anne Danielson-Francois
Awarded: $6,000
Lay Abstract: "Recent evidence has shown a dramatic decline in insect populations over the last 30 years from Germany, the United Kingdom, and Puerto Rico. Insects are vanishing at an unprecedented rate, even in the most pristine national forests where pesticide use is forbidden. Researchers have been alarmed by the significant declines, calling this phenomenon an insect “Armageddon.” Despite the significance of such a global decline on essential ecosystem services such as pollination, insect biodiversity and abundance remain poorly documented in most areas. The combination of climate change, habitat loss, and pesticide use may all contribute to these losses in arthropod abundance and biomass. Here we plan to examine the impact of habitat loss and climate change on a suburban portion of the lower Rouge River in southeast Michigan. We will monitor the removal of surrounding forest patches along the river and measure its impact through satellite imagery. We will also record precipitation and temperature to assess their impacts on insect emergence. We plan to use insect traps at two locations along the lower Rouge River to quantify the biodiversity and abundance of insects over the summers of 2020 - 2022. The results of this study should spur concern and conservation efforts for these ecologically vital insect species."

PI: Francine Dolins
Awarded: $14,966
Lay Abstract: "Cooperative behaviors are ubiquitous: ant colonies build complex nests; wolf packs capture large prey. Yet, even children cooperate with sophistication exceeding evidence for nonhuman animals; they spontaneously help others complete tasks and negotiate fair divisions of labor. These unprecedented levels of cooperation underlie human social contracts, and epitomize intersections of biological and cultural evolution. What are the origins and key cultural components that may distinguish humans from other animals? Are some or all present in our closest living primate relatives?

While many species engage in joint action, studies suggest that only humans possess shared-intentions that flexibly support joint action. Are shared “we-intentions” key components of human sociality and culture? Do these abilities or precursors exist in other species but because traditional experiments cannot reproduce complex real-world ecologies, are not revealed? We address this by combining virtual reality games (VR) with state-of-the-art behavioral analysis based on computational models embodying theoretical assumptions about learning and cooperation. We study social problem-solving in apes, humans and artificial agents to answer questions about we-intentions, its role in the distinctiveness of human cooperation, and to characterize the nature of representations and computations across species. We aim to identify gaps between human, ape and artificial social intelligences potentially illuminating ways of bridging those disparities. Using VR, we will manipulate key aspects of environments to resolve debates about whether nonhuman ape cooperative hunting is based on pre-planned division of labor or individually-driven opportunistic strategy, or how shared knowledge of foraging sites modulates agents’ responses during cooperation and competition."

PI: Junho Hong
Co-PI: Wencong Su
Awarded: $15,000
Lay Abstract: "Cyber-attacks on critical infrastructures are evolving, and their patterns are diversifying, particularly for energy delivery systems. Electric Vehicles (EVs) penetration is increasingly an important transportation modernization trend. This has resulted in more charging facilities and cybersecurity concerns to energy delivery systems. Especially, as the demand for higher-power charging (>200kW) to reduce charging time brings forth eXtreme Fast Charging (XFC) stations, the potential impact of cyber-attacks on the power distribution grid is much higher than lower- and medium-power charging facilities. A fundamental reason for these concerns is that the XFC system could be used as an entry point for cyber intrusion to control systems of distribution grids.

The goal of this proposal is to explore a more resilient system design and develop a more cyber secure Electric Vehicle Supply Equipment (EVSE) and XFC station that can detect a cyber-attack and mitigate its impact. In this proposal, the project team will study a Machine Learning algorithms that identify abnormal behaviors of, and cyber intrusions to, the XFC station and the connected electric distribution grid. The proposed Hidden Markov Models (HMM) based anomaly correlations can predict the attackers' behaviors. Then it can identify false data injection attacks (i.e., bad data detection), and guarantee reliable XFC system operations."

PI: Taehyung Kim
Awarded: $6,000
Lay Abstract: "In this project, a new configuration for light plug-in electric vehicles (PEVs) with a cost-effective, compact dual-charging system will be investigated. The dual charging sources are 1. Solar cells for daytime slow charging and 2. Utility grid for faster charging. This dual charging feature can make PEVs more sustainable using renewable energy source. The new structure of isolated DC-DC converter will be employed in a light PEV which has a brushless permanent magnet (BPM) machine as a traction motor to lower the motor drive component cost. The proposed isolated converter has the capacity to operate effectively in all vehicular modes including charging, propulsion and regenerative braking with dual charging sources (grid and photovoltaics). During a battery charging mode by the grid or solar cells, the converter operates as an isolated SEPIC. While, in propulsion and regenerative recovery modes, it operates as a flyback converter. Therefore, the proposed converter keeps isolation in each mode, which results in better safety for the battery as well as vehicle users. In the propulsion and regenerative braking modes the converter has buck/boost conversion capability to adjust the dc bus voltage according to driving conditions for the BPM motor drive to use a cost-effective motor controller and inverter switches. As a result, the overall system becomes compact, efficient and cost-effective to make it a suitable solution for on-board charging system."

PI: Jaerock Kwon
Awarded: $15,000
Lay Abstract: "Accurate measurement of the key joints of the human body in 3D is essential in human motion study. The joint data acquisition has been conducted using a sophisticated system where markers are attached to a human subject, a motion tracking system tracks the markers in movements, and a kinematic model converts the marker positions to the joint positions. Yet, this kind of acquisition system is not practical in forensics and some clinical areas such as identifying a person, classifying an action, and detecting an anomaly of body movements. In most real-world cases, 2D videos are the only available source of human body motion. Thus, there have been mounting efforts to inferencing their corresponding 3D key joints from the 2D key joint positions. However, due to the immense resources required to acquire 3D human motion data and lack of proper approaches to make an inference framework, the inference accuracies in the currently available systems are not enough to find exact positions of joints and measure joint angular changes that are critical to acquiring salient features of human body movement. The objectives of this project are to (1) build high-resolution human movement datasets with 2D and 3D joint positions, (2) develop a research platform using a Deep Neural Network based on inference engine for 3D kinematics of human movement from 2D videos without using any markers, (3) complete a pilot study to investigate the feasibility of the inference framework, and (4) seek external fundings."

PI: Jin Lu
Awarded: $6,000
Lay Abstract: N/A

PI: Anna Muller
Awarded: $6,000
Lay Abstract: "I am applying for Research Initiation and Development Grant for a project related to the book that I am currently writing, which is a biography of Tonia Lechtman, a Polish Jew and a Communist. Lechtman was born in pre-war Poland. In 1935, she moved to Palestine, from where she moved with her husband to France. From France, they planned to travel to Spain to join the Spanish Civil War on behalf of the Republican forces. While her husband left for Spain, pregnancy prevented her from going. She stayed in southern France until 1942 then escaped to Switzerland with two children, born in 1937 and 1939. Her life in France is one of a refugee trying to hide from fascism and anti-Semitism. Local French and Swiss archives contain many documents regarding her life in hiding. In addition to the archival documents, I have photos that she took during that time as well as rich correspondence between her and her family in Palestine. 

I would like to be able to travel to France and Switzerland in May to visit archives and research the documents there. I am planning a second trip there in June or July this year in order to reconstruct the road she traveled and make a documentary based on her experiences. The grant would help me and a filmmaker travel to France to make a movie that combines her story with a reflection of a historian trying to reconstruct her path."

PI: John Thomas
Awarded: $1,800
Lay Abstract: N/A

PI: Rose Wellman
Awarded: $6,000
Lay Abstract: N/A

PI: Feng Zhou
Awarded: $5,994
Lay Abstract: "It is critical for drivers to have an appropriate level of trust in automated driving for them to interact with the system successfully. However, the current practice of estimating trust in auto-mated driving mainly uses subjective measures, which are not adequate to capture the dynamics in trust. Thus, it is urgent to develop real-time measurement techniques. The overall objective of the proposal is to develop personalized models to estimate trust in real time in automated driving for drivers with different profiles, especially personality. The central hypotheses are that 1) as an affective and cognitive response, trust can be inferred by behavioral and physiological measures in automated driving in real time and that 2) personality can improve the performance of the models. Therationaleis that, once relevant behavioral and physiological measures and driver personality are identified, they can be used to build prediction models to improve trust-based interaction in automated driving. In addition to his prior work and qualifications, the principal investigator (PI)is particularly well prepared to undertake the proposed research due to the successful track record in conducting research in affective computing. By linking to the local automotive industry, this is critical to build PI’s research program at the University of Michigan, Dearborn. The PI plans to attain the overall objective by pursuing two specific aims, i.e., 1) experiment design on trust and personality in automated driving, and 2) build personalized trust-prediction models incorporating driver personality."

PI: Camron Amin
Co-PI: Maureen Linker 
Award: $10,957
Lay Abstract: "This project will create a prototype for a web-based, multilingual digital tool that facilitates the study of multilingual sources that have been digitally curated (scanned, archived, and indexed). If funded, one year’s worth of newsletter produced by the Persian House of Michigan will be used to code and test this digital tool. The PHoM Newsletter is in Persian and English. It is a rich source of social, cultural and economic history for Iranian-Americans living in southeast Michigan. Making the PHoM Newsletter more available to researchers will be a major contribution to basic research in the growing field of Iranian Diaspora studies. This is because, apart from an article published in 1997 on Iranian-Americans in Iowa, the study of Iranian Americans in Michigan is the only to focus on the Midwestern United States. A search interface that could return search results in Persian and English would be a powerful research tool indeed, with potential applications beyond the PHoM Newsletter itself. Once the prototype is developed for the first year (under the auspices of this grant), it will be easy to expand the searchable database for the full run of the PHoM Newsletter. Furthermore, the lessons learned from this project will be applied to similar content for other communities in Detroit Metro. The prototype itself is a case study in two additional fields of research: Library Information Systems (multilingual indexing and searching) and Philosophy (in particular, the epistemology of bringing marginalized knowledge to the cultural center). The prototype application is called the Multilingual Indexing and Search Interface (MISI) and is tentatively scheduled for open source release in Spring 2022."

PI: Armagan Bayram 
Awarded: $5,813
Lay Abstract:N/A

PI: Kirsten Dara Hill
Awarded: $7,500
Lay Abstract: N/A

PI: Riadul Islam
Awarded: $6,000
Lay Abstract: N/A

PI: Sang-Hwan Kim
Awarded: $5,908
Lay Abstract: "As “automated vehicle” has received great degree of attention along with efforts for developing technologies, there are growing interests in research on human factors to address issues on “human drivers” who are driving or monitoring the automated vehicle. There has not been relevant studies with resources in UM-Dearborn to address the issues while the proposer of the study has experience in investigating human-automation interaction and vehicle ergonomics. Based on the research demands and capability, a study will be performed 1) to establish research environment for driver-automated vehicle interaction including driving simulator setup and; 2) to conduct an experiment using human participant in order to validate current research issues and seek further study issues. Along with research environment setup and experimental results, a series of research grant proposals for external funding will be produced."

PI: Kalyan Kondapalli
Awarded: $7,500
Lay Abstract: N/A

PI: Alireza Mohammadi
Awarded: $5,134
Lay Abstract: "This research project seeks knowledge and understanding of nonlinear control and learning algorithms to enable real-time control of soft-bodied robots such as caterpillar robots and elephant trunk robotic arms for robotic locomotion and/or manipulation. Conventionally, rigid materials are employed to fabricate industrial robots whose motion can be programmed to perform a single task efficiently. In contrast to conventional hard-bodied robots, soft robots have biologically-inspired bodies that are made out of inherently soft
and deformable materials. Body compliance enables soft-bodied robots to interact with their surroundings in a safe and efficient manner without having a precise knowledge of their ambient environments. Unlike the relatively easy task of control of conventional rigid-bodied robots, the movement of soft-bodied robots can only be described using an infinite number of degrees of freedom, which makes their control extremely challenging.

The major objective of this research project concerns developing novel methods for motion control and planning for biologically-inspired soft-bodied robotic systems. As these soft-bodied robots approach a continuum behavior similar to that of fluids, their body deformations are continuous, complex and highly compliant. The overall goals of this project are to first to understand how to determine the shape of the soft-bodied robot given a desired position and orientation of its end-effector, next to understand how to automatically determine the soft body curvatures required to place a specified point on the robot body at a desired location, and, finally, to develop nonlinear control schemes to achieve optimal dynamic maneuvers under actuation limits and high body compliance."

PI: Wencong Su
Co-PI: Zhen Hu
Awarded: $15,000
Lay Abstract: "Reliable and resilient electricity is vital to the economy and national security. U.S. DOE, DOD, NSF, and other funding agencies have been heavily investing R&D on "resilient power grid control", which is dynamic, sequential decision-making under uncertainty problems. The traditional solution techniques are inherently limited by their static problem formulation and poor scalability. Significant progress has been made in solving challenging problems in games and robotics using deep reinforcement learning (DRL). In this project, we will develop an open platform for developing, training, and benchmarking DRL algorithms for resilient power system control. It is worth noting that the PIs are aware of the funding limits and time constraints. It is not our focus to deliver commercial-grade software. The goal is rather to provide a baseline platform to support our existing research project that is one step beyond feasibility studies. The PIs and other interested UM-Dearborn users can utilize and customize this DRL platform to attract external funding."