Seminars and Workshops
CECS welcomes speakers from a variety of engineering and computer science backgrounds to participate in on-campus seminars and workshops.
Seminars are held regularly during the Fall and Winter semesters. Students, faculty, staff, and guests are welcome to attend.
Ohio Eminent Scholar, L.W. Scott Alter Chair Professor, Univ. Distinguished Professor at the Univ. of Cincinnati, and board member and vice chairman of Hon Hai Precision (Foxconn Technology Group)
Industrial AI, Big Data Analytics, Machine Learning, and Cyber Physical Systems are changing the way we design product, manufacturing, and service systems. It is clear that as more sensors and smart analytics software are integrated in the networked industrial products and manufacturing systems, predictive technologies can further learn and autonomously optimize productivity and performance. This presentation will address the trends of Industrial AI for future smart industrial internet transformation. First, Industrial AI enabled industrial systems will be introduced. In addition, advanced predictive analytics technologies with case studies will be presented.
Prof. Jay Lee is Ohio Eminent Scholar, L.W. Scott Alter Chair Professor, and Univ. Distinguished Professor at the Univ. of Cincinnati. He also serves as a board member and vice chairman of Hon Hai Precision (Foxconn Technology Group). He is founding director of National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems (IMS). Since its inception in 2001, the Center has been supported by over 100 global companies. IMS was selected as the most economically impactful I/UCRC in the NSF Economic Impact Study Report in 2012. He was selected to be one of the 30 Visionaries in Smart Manufacturing in U.S. by SME in Jan. 2016. In addition, he is co-Founder of a number of start-up companies including Predictronics (a start-up company through NSF ICorp award in 2012). In addition, his Team has won the 1st Place PHM Data Challenges five time out of nine competitions since 2008. He is also the Foundation Director of Industrial AI.
In addition, he serves as a senior advisor to McKinsey & Company, Member of the Global Future Council of World Economic Forum (WEF), member of Board of Governors of the Manufacturing Executive Leadership Board of Frost Sullivan, etc.
Previously, he served as director for product development and manufacturing at United Technologies Research Center (UTRC) as well as program directors for a number of programs at NSF including the Engineering Research Centers Program, the Industry/University Cooperative Research Centers Program, and Materials Processing, and Manufacturing Program, etc., etc. He is a frequently invited speaker and has delivered over 260 keynote and plenary speeches at major international conferences. He is a Fellow of ASME, SME, PHM (Prognostics and Health Management), as well as a founding fellow of International Society of Engineering Asset Management (ISEAM). For Publication, see Google scholar.
He has received a number of awards including the Prognostics Innovation Award at NI Week by National Instruments in 2012, NSF Alex Schwarzkopf Technological Innovation Prize in 2014, MFPT (Machinery Failure Prevention Technology Society) Jack Frarey Award in 2014, and PICMET Medal of Excellence in 2016.
October 25th, 2019
Senior Research Scientist in the Artificial Intelligence Products Group at Intel Corporation
Kushal Datta is a Senior Research Scientist in the Artificial Intelligence Products Group at Intel Corporation. He specializes in accelerating deep learning training and inference on Intel Architecture. His noteworthy achievements include optimization of Multiscale CNN training time on Multi-Node Xeon® and INT8/VNNI quantization of the Transformer model. In his previous roles, he worked on Intel® Graph Analytics Toolkit, Genomics Analytics Toolkit-4.0 (from Broad Institute) and TileDB (a multi-dimensional array store). Dr. Datta received his PhD in ECE from University of North Carolina at Charlotte where he created a famous cycle-accurate micro-architecture simulator called Casper. His doctoral work used statistical machine learning and Casper to improve power efficiency of simultaneous multi-threading SPARCV9 very large-core microarchitectures.
September 27th, 2019