Academia/Industry Mixer Panel: Advances in Artificial Intelligence, Big data and Signal processing in Financial Market and Business Intelligence

Thursday, 16 November, 16:00 - 17:30, Verdun

Each expert panelist will give 6 minutes presentation. Afterwards we'll start the discussions. The panel will focus on business analytics application aspects to inform and inspire signal processing researchers.


Xiao-Ping (Steven) Zhang is Professor of Electrical and Computer Engineering and cross-appointed to the Finance Department at the Ted Rogers School of Management at Ryerson University. He received B.S. and Ph.D. degrees from Tsinghua University, both in Electronic Engineering. He holds an MBA in Finance, Economics and Entrepreneurship with Honors from the University of Chicago Booth School of Business. His research interests include signal processing, electronic systems, machine learning, big data, finance, and business analytics. He is cofounder and CEO for EidoSearch, an Ontario based company offering a content-based search and analysis engine for financial big data. He is the general co-chair for ICASSP2021. He is a tutorial speaker in ACMMM2011, ISCAS2013, ICIP2013, ICASSP2014 and IJCNN2017. He is/was Associate Editor for IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, and IEEE Signal Processing Letters.

Expert Panelists:

David Kedmey is President and co-founder of EidoSearch, a fintech and predictive analytics company that helps quantitative hedge funds to find conditions in data with forecasting power via its patented numeric search engine. He leads product and business development. He previously worked in the mergers and acquisitions group in the investment banking division at Oppenheimer & Co in New York. Before joining Oppenheimer & Co., Mr. Kedmey raised capital for hedge funds and for private placements in real estate and energy as an independent broker. He holds an M.B.A. (with Honors) in Finance, Accounting and Entrepreneurship from the University of Chicago Booth School of Business.

Ali Khanafer has been a Data Scientist at IBM since 2015. At IBM, he builds scalable machine learning systems for clients in the media, entertainment, and automotive industries. He is also a lecturer at the University of Ottawa where he teaches courses on optimization theory and control systems. He obtained his Ph.D. in 2014 from the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. His doctoral research focused on distributed optimization and control of large-scale multi-agent systems. He obtained a master's degree in Electrical Engineering from the University of Toronto in 2010 and a bachelor's degree in Electrical Engineering from the University of Ottawa in 2008. His research interests lie at the intersection of machine learning, game theory, control theory, and distributed optimization. He is recipient of an NSERC Postdoctoral Fellowship (2014), the Best Student Paper Award at the IFAC NecSys (2012), an NSERC Postgraduate Scholarship (2010), and an NSERC Alexander Graham Bell Canada Graduate Scholarship (2008).

Feng Shi is a computer vision researcher at Sportlogiq, Montreal, a company offering sport analytics. Previously, he served as a research analyst at MDA Corporation, Vancouver. He received his Ph.D. and M.Sc. in Electrical Engineering from the School of EECS at University of Ottawa. His primary research interests are computer vision and machine learning, especially in the areas of human activity recognition, video analysis, object recognition and multi-object tracking.

Zhu Liu is a Principal Inventive Scientist at AT&T Labs – Research, where he is leading research and development on large scale video content analytics systems. His 17+ year of experience in industrial laboratories produced award winning systems, best international evaluation results, 118 issued U.S. patents, and more than 70 published technical articles. His research interests include multimedia content analysis, multimedia databases, video search, machine learning, big data, and natural language understanding. Dr. Liu received the B.S. and M.S. degrees in Electronic Engineering from Tsinghua University, in 1994 and 1996, respectively, and the Ph.D. degree in Electrical Engineering from New York University in 2001. He is a senior area editor of the IEEE Signal Processing Letters, and he served as associate editor for the IEEE Transaction on Multimedia in 2008-2013. Dr. Liu was also an adjunct professor of Columbia University and New York University.

Cem Tekin is an Assistant Professor in Electrical and Electronics Engineering Department at Bilkent University, Ankara, Turkey. He received his PhD in Electrical Engineering: Systems (2013), MS in Applied Mathematics (2011) and MSE in Electrical Engineering: Systems (2010), all from the University of Michigan, Ann Arbor. From 2013 to 2015 he was a postdoctoral scholar in Electrical Engineering Department, UCLA. Cem has authored or coauthored over 35 research papers, 3 book chapters and a research monograph. His research spans the area of data science and machine learning with an emphasis on multi-agent learning, online learning and multi-armed bandit problems. He is interested in developing machine learning algorithms for finance, real-time stream mining, recommender systems, healthcare informatics and cognitive radio networks. He is the recepient of the Fred W. Ellersick award for the best paper in MILCOM 2009, and TUBITAK Career Award.