Raj Mittra Distinguished Lecture Series: Data Reduction Challenges in Human-Machine Reasoning

This talk will present an overview of man – machine symbiosis broadly defined and some of the challenges that researchers need to overcome for applications of the technology to become an everyday reality. Specifically, it will describe data reduction and ordering methods for machine learning and machine-supported human decision making. 

We begin by presenting a computationally efficient method for data reduction and ordering to overcome human cognitive biases and enhance human decision making. We then discuss data reduction methods for faster machine learning. We present  a computationally efficient algorithm for training support vector machines (SVM). The algorithm identifies a relatively small subset of the data to train the SVM while guaranteeing a performance that is within a desired distance from that obtained by training the SVM with the entire data set. Time permitting, we discuss extensions designed to accelerate the training of neural networks.

Speaker bio: Ahmed H Tewfik received his B.Sc. degree from Cairo University, Cairo Egypt, in 1982 and his M.Sc., E.E. and Sc.D. degrees from MIT, in 1984, 1985 and 1987 respectively. He is the Cockrell Family Regents Chair in Engineering and the Chairman of the Department of Electrical and Computer Engineering at the University of Texas Austin. He was the E. F. Johnson professor of Electronic Communications with the department of Electrical Engineering at the University of Minnesota until September 2010. Dr. Tewfik worked at Alphatech, Inc. and served as a consultant to several companies. From August 1997 to August 2001, he was the President and CEO of Cognicity, Inc., an entertainment marketing software tools publisher that he co-founded, on partial leave of absence from the University of Minnesota. His current research interests are in cognitive augmentation through man-machine symbiosis and mobile computing, medical imaging and brain computing interfaces. Prof. Tewfik is a Fellow of the IEEE. He was a Distinguished Lecturer of the IEEE Signal Processing Society in 1997 - 1999. He received the IEEE third Millennium award in 2000 and the IEEE Signal Processing Society Technical Achievement Award in 2017. He was elected to the positions of President-elect of the IEEE Signal Processing Society in 2017 and VP Technical Directions of that Society in 2009. He served as VP from 2010-2012 and on the board of governors of that Society from 2006 to 2008. He has given several plenary and keynote lectures at IEEE conferences. 

 

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Media Contact: Dr. Vishal Monga

 
 

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The School of Electrical Engineering and Computer Science was created in the spring of 2015 to allow greater access to courses offered by both departments for undergraduate and graduate students in exciting collaborative research in fields.

We offer B.S. degrees in electrical engineering, computer science, computer engineering and data science and graduate degrees (master's degrees and Ph.D.'s) in electrical engineering and computer science and engineering. EECS focuses on the convergence of technologies and disciplines to meet today’s industrial demands.

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