Welcome to the Signal and Information Processing Seminar Series at Rutgers!
The SIP Seminar Series at Rutgers University–New Brunswick brings together a diverse group of researchers, both from within and outside Rutgers, on (approximately) a biweekly basis to discuss recent advances in signal and information processing. The term of “Signal and Information Processing” used within the SIP Seminars is rather broad in nature and subsumes signal processing, image processing, statistical inference, machine learning, computer vision, control theory, harmonic analysis, information theory, etc.
Seminar Mailing List: You can subscribe to the SIP Seminars mailing list by sending an email to ECE_SIPemail@example.com with SUBSCRIBE in the subject of the email.
Spring 2019 Seminar Schedule: The SIP Seminars in Spring 2019 will take place on the following dates (typically, Wednesdays) at 2 pm in Room 240 of the Electrical Engineering Building on Busch Campus of Rutgers University–New Brunswick: Feb 6, 20; Mar 6, 27; Apr 3, 10, 24.
Spring 2019 SIP Seminars
Dr. Rafael D’Oliveira
Title: GASP Codes for Secure Distributed Matrix Multiplication
Abstract: We consider the problem of secure distributed matrix multiplication (SDMM) in which a user wishes to compute the product of two matrices with the assistance of honest but curious servers. We construct polynomial codes for SDMM by studying a combinatorial problem on a special type of addition table, which we call the degree table. The codes are based on arithmetic progressions, and are thus named GASP (Gap Additive Secure Polynomial) Codes. GASP Codes are shown to outperform all previously known polynomial codes for secure distributed matrix multiplication in terms of download rate.
Biography: Rafael G.L. D’Oliveira is a postdoctoral research assistant in the Department of Electrical Engineering at Rutgers University. He received his Ph.D. degree in applied mathematics from the University of Campinas in Brazil. As a Ph.D. student he did a one year research internship at Telecom ParisTech in France. His research interests lie in the areas of information theory, coding theory and their applications to privacy in distributed systems.
Title: Outage Analysis for the Open Area Mm-wave Device-to-Device Environment
Abstract: A significant portion of the 5th generation of wireless networks will operate in the mm-wave bands. One of the several challenges associated with mm-wave propagation is to overcome shadowing due to signal blockage caused by environmental objects. Particularly susceptible are nodes in a device-to-device network that typically operate at low power and in a blockage prone environment such as crowded open areas. In this talk, we provide an insight into the effect of blockages on the signal quality for an open area device-to-device scenario. We propose a blockage model based on a fundamental stochastic geometry process: the homogeneous Poisson Point Process. The model provides the average signal attenuation as a soft metric that quantifies the extent of blockage. This not only indicates whether the signal is blocked but also measures how much the signal is attenuated due to one or more blockers. The analytical results are confirmed with the help of Monte Carlo simulations for real-world blocker placement in the environment. Further, outage performance of under line-of-sight and non-line-of-sight mm-wave environments is analyzed.
Biography: Swapnil Mhaske is a Ph.D. student under the supervision of Prof. Predrag Spasojevic in the Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA. He received the B.E. degree in electronics engineering from the University of Pune, India and the M.S. degree in electrical and computer engineering from Rutgers University, New Brunswick, NJ, USA. His research interests broadly lie in the area of wireless communications, with focus on the development of efficient hardware architectures for error control coding systems and channel modeling for mm-wave communications.
Prof. Yuxin Chen
Title: Random initialization and implicit regularization in nonconvex statistical estimation
Abstract: Recent years have seen a flurry of activities in designing provably efficient nonconvex procedures for solving statistical estimation / learning problems. Due to the highly nonconvex nature of the empirical loss, state-of-the-art procedures often require suitable initialization and proper regularization (e.g. trimming, regularized cost, projection) in order to guarantee fast convergence. For vanilla procedures such as gradient descent, however, prior theory is often either far from optimal or completely lacks theoretical guarantees. This talk is concerned with a striking phenomenon arising in two nonconvex problems (i.e. phase retrieval and matrix completion): even in the absence of careful initialization, proper saddle escaping, and/or explicit regularization, gradient descent converges to the optimal solution within a logarithmic number of iterations, thus achieving near-optimal statistical and computational guarantees at once. All of this is achieved by exploiting the statistical models in analyzing optimization algorithms, via a leave-one-out approach that enables the decoupling of certain statistical dependency between the gradient descent iterates and the data. As a byproduct, for noisy matrix completion, we demonstrate that gradient descent achieves near-optimal entrywise error control. (This is joint work with Cong Ma, Kaizheng Wang, Yuejie Chi, and Jianqing Fan.)
Biography: Yuxin Chen is currently an assistant professor in the Department of Electrical Engineering at Princeton University. Prior to joining Princeton, he was a postdoctoral scholar in the Department of Statistics at Stanford University, and he completed his Ph.D. in Electrical Engineering at Stanford University. His research interests include high-dimensional statistics, convex and nonconvex optimization, statistical learning, and information theory. He received the 2019 AFOSR Young Investigator Award.
Prof. Xiugang Wu
Prof. Jayadev Acharya
Prof. Robert Babak Faryabi
Prof. Philippe Ciblat