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Colloquium Series

Once every week while school is in session, EECS invites a distinguished researcher or practitioner in a computer science or electrical and computer engineering-related field to present their ideas and/or work. Talks are generally targeted to electrical engineering and computer science graduate students. This colloquium series is free and open to everyone.

Upcoming Colloquia

The Quest for The Ultimate Learning Machine

Monday, October 24, 2016 -
4:00pm to 4:50pm
DEAR 118

Speaker Information

Pradeep Dubey


Traditionally, there has been a division of labor between computers and humans where all forms of number crunching and bit manipulations are left to computers; whereas, intelligent decision-making is left to us humans.  We are now at the cusp of a major transformation that can disrupt this balance. There are two triggers for this: first, trillions of connected devices (the “Internet of Things”) converting the large untapped analog world around us to a digital world, and second, (thanks to Moore’s Law) beyond-exaflop levels of compute, making a large class of structure learning and decision-making problems now computationally tractable. In this talk, I plan to discuss real challenges and amazing opportunities ahead of us for enabling a new class of applications and services, “Machine Intelligence Led Services”.  These services are distinguished by machines being in the ‘lead’ for tasks that were traditionally human-led, simply because computer-led implementations are about to reach and even surpass the quality metrics of current human-led offerings.

Speaker Bio

Pradeep Dubey is an Intel Fellow and Director of Parallel Computing Lab (PCL), part of Intel Labs. His research focus is computer architectures to efficiently handle new compute-intensive application paradigms for the future computing environment. Dubey previously worked at IBM's T.J. Watson Research Center, and Broadcom Corporation. He has made contributions to the design, architecture, and application-performance of various microprocessors, including IBM® Power PC*, Intel® i386TM, i486TM, Pentium® Xeon®, and the Xeon Phi™ line of processors. He holds over 36 patents, has published over 100 technical papers, won the Intel Achievement Award in 2012 for Breakthrough Parallel Computing Research, and was honored with Outstanding Electrical and Computer Engineer Award from Purdue University in 2014. Dr. Dubey received a PhD in electrical engineering from Purdue University. He is a Fellow of IEEE.

Algorithmic Fairness: From social good to mathematical framework

Monday, November 7, 2016 -
4:00pm to 4:50pm
DEAR 118

Speaker Information

Suresh Venkatasubramanian
University of Utah


Machine learning has taken over our world, in more ways than we realize. You might get book recommendations, or an efficient route to your destination, or even a winning strategy for a game of Go. But you might also be admitted to college, granted a loan, or hired for a job based on algorithmically enhanced decision-making. We believe machines are neutral arbiters: cold, calculating entities that always make the right decision, that can see patterns that our human minds can’t or won’t. But are they? Or is decision-making-by-algorithm a way to amplify, extend and make inscrutable the biases and discrimination that is prevalent in society?

To answer these questions, we need to go back — all the way to the original ideas of justice and fairness in society. We also need to go forward — towards a mathematical framework for talking about justice and fairness in machine learning. I will talk about the growing landscape of research in algorithmic fairness: how we can reason systematically about biases in algorithms, and how we can make our algorithms fair(er).

Speaker Bio

Suresh Venkatasubramanian is an associate professor in the School of Computing at the University of Utah. He did his Ph.D at Stanford University, and did a stint at AT&T Research before joining the U. His research interests include computational geometry, data mining and machine learning, with special interests in high dimensional geometry, large data algorithms, clustering and kernel methods. He received an NSF CAREER award in 2010. He spends much of his time now thinking about the problem of "algorithmic fairness": how we can ensure that algorithmic decision-making is fair, accountable and transparent. His work has been covered on Science Friday, NBC News, and Gizmodo, as well as in various print outlets.

Past Colloquia

Andrew Clark
Wednesday, February 19, 2014 -
8:45am to 9:45am
Roy Olsson
Monday, February 17, 2014 -
8:45am to 10:00am
Janardhan Rao (Jana) Doppa
Monday, February 10, 2014 -
4:00pm to 4:50pm
Jonathan Coker
Thursday, February 6, 2014 -
3:00pm to 4:00pm
Kristi Potter
Monday, February 3, 2014 - 4:00pm
Aaron Buchwald
Monday, January 13, 2014 -
4:00pm to 4:50pm
Bruce Shepherd
Wednesday, January 8, 2014 -
9:00am to 10:00am
Mark Crowley
Monday, November 25, 2013 -
4:00pm to 4:50pm