CISS 2018

Plenary Speakers

Bin Yu

Bin Yu University of California at Berkeley

Three principles of data science: predictability, stability, and computability
Wednesday, March 21, 11:45am

In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions.

Making prediction as its central task and embracing computation as its core, machine learning has enabled wide-ranging data-driven successes. Prediction is a useful way to check with reality. Good prediction implicitly assumes stability between past and future. Stability (relative to data and model perturbations) is also a minimum requirement for interpretability and reproducibility of data driven results (cf. Yu, 2013). It is closely related to uncertainty assessment. Obviously, both prediction and stability principles can not be employed without feasible computational algorithms, hence the importance of computability.

The three principles will be demonstrated in the context of two neuroscience projects and through analytical connections. In particular, the first project adds stability to predictive modeling used for reconstruction of movies from fMRI brain signals to gain interpretability of the predictive model. The second project uses predictive transfer learning that combines AlexNet, GoogleNet and VGG with single V4 neuron data for state-of-the-art prediction performance. It provides stable function characterization of neurons via (manifold) deep dream images from the predictive models in the difficult primate visual cortex V4. Our V4 results lend support, to a certain extent, to the resemblance of these CNNs to a primate brain.

Bio: Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California at Berkeley. Her current research interests focus on statistics and machine learning theory, methodologies, and algorithms for solving high-dimensional data problems. Her lab is engaged in interdisciplinary research with scientists from genomics, neuroscience, precision medicine and political science. She obtained her B.S. degree in Mathematics from Peking University in 1984, her M.A. and Ph.D. degrees in Statistics from the University of California at Berkeley in 1987 and 1990, respectively. She held faculty positions at the University of Wisconsin-Madison and Yale University and was a Member of Technical Staff at Bell Labs, Lucent. She was Chair of Department of Statistics at UC Berkeley from 2009 to 2012, and is a founding co-director of the Microsoft Lab on Statistics and Information Technology at Peking University, China, and Chair of the Scientific Advisory Committee of the Statistical Science Center at Peking University. She is Member of the U.S. National Academy of Sciences and Fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, an Invited Speaker at ICIAM in 2011, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 2013-2014 and the Rietz Lecturer of IMS in 2016. She is a Fellow of IMS, ASA, AAAS and IEEE. She served on the Board of Mathematics Sciences and Applications (BMSA) of NAS and as co-chair of SAMSI advisory committee, and on the Board of Trustees at ICERM and Scientific Advisory Board of IPAM. She has served or is serving on many editorial boards, including Journal of Machine Learning Research (JMLR), Annals of Statistics and American Statistical Association (JASA).


Promod Viswanath

Pramod Viswanath University of Illinois at Urbana Champaign

Thursday, March 22, 11:45am

Bio: Pramod Viswanath received the Ph.D. degree in electrical engineering and computer science from University of California at Berkeley in 2000. From 2000 to 2001, he was a member of research staff at Flarion technologies, NJ. Since 2001, he is on the faculty at University of Illinois at Urbana Champaign in Electrical and Computer Engineering, where he currently is a professor. He received the Eliahu Jury Award from the EECS department of UC Berkeley in 2000, the Bernard Friedman Prize from the mathematics department of UC Berkeley in 2000, a NSF CAREER award in 2002, the Xerox faculty research award from the college of engineering of UIUC in 2010 and the Best Paper Award at the Sigmetrics conference in 2015. He has worked extensively on wireless communication: co-designer of the first OFDM cellular system at Flarion technologies and has coauthored a popular textbook on wireless communication. With wireless technology fairly mature, he has explored a variety of research topics: his current research interests include cryptocurrencies and natural language processing.

Alexandros Dimakis University of Texas at Austin

Friday, March 23, 11:45am

Bio: Prof. Dimakis received his Ph.D. in 2008 and M.S. degree in 2005 in electrical engineering and computer sciences from UC Berkeley and the Diploma degree from the National Technical University of Athens in 2003. During 2009 he was a CMI postdoctoral scholar at Caltech. He received an NSF Career award in 2011, a Google faculty research award in 2012 and the Eli Jury dissertation award in 2008. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012. He is currently serving as an associate editor for IEEE Signal Processing letters. He served as chair of the Data Storage track at Globecom and was a keynote speaker at the Int. Symposium on Network Coding (NetCod). His research interests include information theory, coding theory, signal processing, and networking, with a current focus on distributed storage, network coding, distributed inference and message passing algorithms.