We are delighted to have with us as distinguished plenary speakers:
Jennifer Tour Chayes, The Power of Locality for Network Algorithms Abstract. Given the massive size of many networks, conventional algorithms which scale as polynomials in the network size are woefully inadequate. In the first part of this talk, we consider how to use locality to deliver much more efficient algorithms (quasilinear or even sublinear in the network size) for quantities and questions like pagerank, coverage, diffusion, and determining the most influential nodes. In the second part of this talk, we consider another aspect of locality, namely the question of local data access. Many large networks are encoded locally, e.g., with adjacency lists. How does the nature of the data access impact the efficiency of algorithms? Surprisingly, we show that small differences in data access can lead to very large differences in efficiency and approximability of network algorithms. As an example, we consider a covering problem which arises naturally for recruiters on social networks, and show how small differences between the information on neighbors in LinkedIn and Facebook lead to large differences in their utility to recruiters. (Shortened) Biography. Jennifer Tour Chayes is Distinguished
Scientist, Managing Director and Cofounder of Microsoft Research New England and
Microsoft Research New York City. Before joining Microsoft in 1997, Chayes was for
many years Professor of Mathematics at UCLA. Chayes is the author of over 125
academic papers and the inventor of over 30 patents. Her research areas include
phase transitions in discrete mathematics and computer science, structural and
dynamical properties of selfengineered networks, graph theory, graph algorithms,
algorithmic game theory, and computational biology.


Ramesh Johari, Can I Take a Peek? Continuous Monitoring of Online A/B Tests Abstract. A/B testing is a hallmark of Internet services:
from ecommerce sites to social networks to marketplaces, nearly all online
services use randomized experiments as a mechanism to make better business decisions.
Such tests are generally analyzed using classical frequentist statistical measures:
pvalues and confidence intervals. Despite their ubiquity, these reported values are
computed under the assumption that the experimenter will not continuously monitor their
testin other words, there should be no repeated “peeking” at the results that affects
the decision of whether to continue the test. On the other hand, one of the greatest
benefits of advances in information technology, computational power, and visualization
is precisely the fact that experimenters can watch experiments in progress, with greater
granularity and insight over time than ever before.
Biography. Ramesh Johari is an Associate Professor at Stanford University and the Cisco Faculty Scholar in the School of Engineering, with a fulltime appointment in the Department of Management Science and Engineering (MS&E), and courtesy appointments in the Departments of Computer Science (CS) and Electrical Engineering (EE). He is a member of the Operations Research group in MS&E, the Information Systems Laboratory in EE, and the Institute for Computational and Mathematical Engineering. He received an A.B. in Mathematics from Harvard (1998), a Certificate of Advanced Study in Mathematics from Cambridge (1999), and a Ph.D. in Electrical Engineering and Computer Science from MIT (2004). 

Sridevi V. Sarma, On the Therapeutic Mechanisms of Deep Brain Stimulation for Parkinson’s disease: Why High Frequency? Abstract. Deep brain stimulation (DBS) is clinically recognized
to treat movement disorders in Parkinson’s disease (PD), but its therapeutic mechanisms
remain elusive. One thing is clear though: high frequency periodic DBS (130180Hz) is
therapeutic, while low frequency DBS is not therapeutic and may even worsen symptoms.
So, what is so special about high frequency? In this talk, we address this question by
discussing our viewpoint supported by recent results from our key studies of the
thalamocorticalbasal ganglia motor network.
Biography. Sridevi V. Sarma (M’04) received the B.S. degree in electrical engineering from Cornell University, Ithaca NY, in 1994; and an M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in, Cambridge MA, in 1997 and 2006, respectively. She was a Postdoctoral Fellow in the Brain and Cognitive Sciences Department at the Massachusetts Institute of Technology, Cambridge, from 20062009. She is now an assistant professor in the Institute for Computational Medicine, Department of Biomedical Engineering, at Johns Hopkins University, Baltimore MD. Her research interests include modeling, estimation and control of neural systems using electrical stimulation. She is a recipient of the GE faculty for the future scholarship, a National Science Foundation graduate research fellow, a L’Oreal For Women in Science fellow, the Burroughs Wellcome Fund Careers at the Scientific Interface Award, the Krishna Kumar New Investigator Award from the North American Neuromodulation Society, and a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE). 

Martin Wainwright, Statistics meets Computation: Some vignettes from the interface Abstract. In the modern era of massive data sets, computational considerations have become increasingly important in statistics. In this talk, we discuss some problems that lie at the interface between statistics and algorithms, including rigorous guarantees for nonconvex optimization problems in statistics, and various forms of optimality in randomized sketching methods Biography. Martin Wainwright joined the faculty at University of California at Berkeley in Fall 2004, and is currently a Professor with a joint appointment between the Department of Statistics and the Department of Electrical Engineering and Computer Sciences. He received his Bachelor's degree in Mathematics from University of Waterloo, Canada, and his Ph.D. degree in Electrical Engineering and Computer Science (EECS) from Massachusetts Institute of Technology (MIT), for which he was awarded the George M. Sprowls Prize from the MIT EECS department in 2002. He is interested in highdimensional statistics, information theory and statistics, and statistical machine learning. He has received an Alfred P. Sloan Foundation Research Fellowship (2005), IEEE Best Paper Awards from the Signal Processing Society (2008) and Communications Society (2010); the Joint Paper Award from IEEE Information Theory and Communication Societies (2012); a Medallion Lecturer (2013) of the Institute for Mathematical Statistics; a Section Lecturer at the International Congress of Mathematicians (2014); and the COPSS Presidents' Award in Statistics (2014). He is currently serving as an Associate Editor for the Annals of Statistics, Journal of Machine Learning Research, Journal of the American Statistical Association, and Journal of Information and Inference. 