Past Events

Abstract: Approximate probabilistic inference is a key computational task in modern machine learning, which allows us to reason with complex, structured, hierarchical (deep) probabilistic models to extract information and quantify uncertainty.

Abstract: We present a new statistical framework to quantify uncertainty (UQ) for recovering low-rank matrices from incomplete and noisy observations. We further develop a sequential active sampling approach guided by the uncertainties. The motivation comes from two related and widely studied problems, matrix completion, which aims to recover a low-rank matrix X from a partial, noisy observation of its entries, and low-rank matrix recovery, which recovers X from a set of linear combination its entries with additive noise.

Recent years have seen a flurry of activities in designing provably efficient nonconvex procedures for solving statistical estimation problems. The premise is that despite nonconvexity, the loss function may possess benign geometric properties that enable fast global convergence under carefully designed initializations, such as local strong convexity, local restricted convexity, etc.

*PLEASE NOTE CORRECTION: Seminar will take place in EER 3.646 (North Tower)This talk focuses on the statistical sample complexity and model reduction of Markov decision process (MDP).  We begin by surveying recent advances on the complexity for solving MDP, without any dimension reduction. In the first part we study the statistical state compression of general Markov processes. We propose a spectral state compression method for learning state features and aggregation structures from data.

The concept of a blockchain was invented by Satoshi Nakamoto to maintain a distributed ledger for an electronic payment system, Bitcoin. In addition to its security, important performance measures of a blockchain protocol are its transaction throughput, confirmation latency and confirmation reliability. These measures are limited by two underlying physical network attributes: communication capacity and speed-of-light propagation delay. Existing systems operate far away from these physical limits.

This year's Texas Wireless Summit, “AI and the Mobile Device,” will focus on how machine learning, artificial intelligence, and some key applications will interact with wireless technology. The Summit will examine how Artificial Intelligence and Machine Learning (AI/ML) will simultaneously enhance connectivity as well as place demands on both devices and connectivity.

Soft biomaterials such as human skin have very different mechanical properties from conventional electronics, requiring unusual materials and geometries to match the behavior of the skin.  One of the biggest challenges in stretchable electronics is the transfer of power and data signals, with physical wiring easily pulled out or damaged.  In my talk, I will be discussing all aspects of creating inductors and power circuits for wireless power transfer to stretchable systems.  I will focus on the use of room temperature liquid metals and stretchable magnetic materials to maximize

Recent years have witnessed significant progress in entropy estimation, in particular in the large alphabet regime. Concretely, there exist efficiently computable information theoretically optimal estimators whose performance with n samples is essentially that of the maximum likelihood estimator with n log(n) samples, a phenomenon termed ``effective sample size boosting''. Generalizations to processes with memory (estimation of the entropy rate) and continuous distributions (estimation of the differential entropy) have remained largely open.

The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), especially since 2010 or so, yielding new tools for signal estimation, classification, prediction, and manipulation.  Layered signal representations, nonlinear function approximation, and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size.  These are leading to significant performance gains in a variety of long standing problem domains (e.g., speech, vision), as well as providing the ability to construc

Bio-tissues are soft, curvilinear and dynamic whereas wafer-based electronics are hard, planar, and rigid. Over the past decade, stretchable high-performance inorganic electronics have emerged as a result of new structural designs and unique materials processes. Electronic tattoos (e-tattoos) represent a class of stretchable circuits, sensors, and stimulators that are ultrathin, ultrasoft and skin-conformable. This talk will first introduce stretchable serpentine structures followed by a dry and freeform “cut-and-paste” method for the rapid prototyping of e-tattoos.