Speaker: Dr. Wasim Huleihel
The computational aspect in the field of statistics has drawn much attention recently. Prompted by the ever-growing size and dimensionality of modern data sets, new approaches to statistical inference are needed for high-dimensional problems, in which the dimensionality of the data is comparable to the sample size. These problems are inherently underdetermined, typically resulting in trivial rates of detection/estimation. Nonetheless, if the underlying signal is known to have a certain structure then this issue disappears. In this talk, several timely models in statistics and signal processing that exhibit phase transitions will be presented. In the first part of the talk, I will discuss various problems with planted sparse structure, focusing on the wellstudied community detection problem in large scale/big data networks. For these problems, I will show the underlying phase diagrams and discuss the inherent statistical-computational gaps. The second part of this talk will be devoted to the group testing problem, where the goal is to recover a small subset of defective items from a larger population, while efficiently reducing the total number of required tests. The fundamental, as well as the computational limits of the group testing problem, are well-understood under the assumption that the input-output statistical relationship is known to the recovery algorithm. Practical considerations, however, render this assumption inapplicable, and “blind” recovery/estimation procedures, independent of the inputoutput statistics, are desired. Here, I will present the fundamental limits of a general noisy group testing problem when this relationship is unknown both from practical and statistical point of views.
Wasim Huleihel received the B.Sc. and M.Sc. degrees in 2012 and 2013 from Ben-Gurion University, respectively, and received his PhD degree in 2017 from the Technion, all in electrical engineering. He is currently a postdoctoral fellow in the Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT). His research interests include information theory, high dimensional statistics, and statistical signal processing. Wasim is the recipient of the MIT – Technion Postdoctoral Fellowship, Viterbi fellowship, the Vatat postdoctoral scholarship, Vatat fellowship for excellent PhD students, the Advanced Communication Center (ACC) Feder Family Award for outstanding research work in the field of communication technologies (first prize), B.Sc. and M.Sc. graduations with honor, M.Sc. Rector’s List Award, and Technion excellent tutor awards.