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Abstract: Access to even basic medical resources is greatly influenced by factors like an individual’s birth country and zip code. In this talk, I will present my work on designing intelligent mobile systems for equitable healthcare. I will showcase three systems that are not only interesting from a computational standpoint but are also having real-world medical impact. The first system can det...

SHPE changes lives by empowering the Latinx/Hispanic community to realize its fullest potential and to impact the world through STEM awareness, access, support and development. Learn more at shpe.rice.edu/

In collaboration with the Rice Office of Multicultural Affairs this Black History Month, the Department of Electrical and Computer Engineering (ECE) at Rice is hosed guest speaker Dr. Sandra K. Johnson, Rice alumna, and first African American woman to earn a Ph.D. in ECE. Title: Life as a Technology Trailblazer and FinTech Startup Founder Abstract: The life of a technology trailblazer is an int...

Abstract: With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice, flawed models in healthcare, and black box loan decisions in finance. Interpretability of machine learning models is critical in high stakes decisions. In this talk, I will focus on...

Abstract: In this talk, I will present some of our recent attempts at bridging the theory-practice divide between optimization theory and machine learning practice. While we have witnessed rapid advances in optimization theory and its application machine learning (optimal algorithms, minimax rates, tight complexity bounds), many of these advances have failed to advance machine learning practice...

Abstract: This talk is a brief summary of a journey in video conferencing industry. It starts with some background information about video compression. Then it briefly goes through the video conferencing product development on software, DSP and hardware and show some key algorithms for each platform. In this talk, we will try to explain: why the video encoder is so complex? Where does the compl...

Abstract: Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. An exciting opportunity in this regard stems from the growing availability of perturbation / intervention data (for example in genomics, advertisement, education, etc.). In order to obtain mechanistic insights from such data, a major challenge is the developmen...

Abstract: In the first part of the talk, we focus on the gradient noise (GN) in the stochastic gradient descent (SGD) algorithm for training deep neural networks (DNNs). In this setting, GN is often considered to be Gaussian in the large data regime by assuming that the classical central limit theorem (CLT) kicks in. This assumption is often made for mathematical convenience, since it enables S...

Abstract: Counterfactual estimation using synthetic controls is one of the most successful recent methodological developments in causal inference. Despite its popularity, the current description only considers time series aligned across units and synthetic controls expressed as linear combinations of observed control units. We propose a continuous-time alternative that models the latent counter...

Abstract: This talk will introduce a framework for processing electron microscopy data obtained using energy dispersive X-ray spectroscopy (EDX). The specimen is observed from different directions in order to obtain a tomographic reconstruction. The physics of the process suggests considering the spectrum as a realization of a linear combination of a few random variables representing the chemic...

Abstract: Unobserved confounding is a fundamental obstacle to establishing valid causal conclusions from observational data. Two complementary types of approaches have been developed to address this obstacle. An extensive line of work is based on taking advantage of fortuitous external aids (such as the presence of an instrumental variable or other proxy), along with additional assumptions to e...

Abstract: Image denoising - removal of white additive Gaussian noise from an image - is one of the oldest and most studied problems in image processing. An extensive work over several decades has led to thousands of papers on this subject, and to many well-performing algorithms for this task. As expected, the era of deep learning has brought yet another revolution to this subfield, and took the...

Prof. Saptarshi Das Associate Professor, Department of Engineering Science and Mechanics Materials Research Institute Pennsylvania State University Title: A Bio-inspired Era of Sensing, Computing, Storage, and Security based on 2D Materials and Devices Abstract: My group is developing a new paradigm of sensing, computing, storage, and hardware security inspired by the neurobiological architectu...

Dr. Ranveer Chandra CTO, Agri-Food, Managing Director, Research for Industry Leads Networking Research Microsoft Research Abstract: Data-driven techniques help boost agricultural productivity by increasing yields, reducing losses and cutting down input costs. However, these techniques have seen sparse adoption owing to high costs of manual data collection and limited connectivity solutions. In ...

Prof. Howard Lee -Extreme Light-matter Interaction in Zero-index Materials and “Meta”-Optical Fibers

Prof. Mahmoud Hussein -Large-scale vibron-phonon couplings: A new strategy in thermoelectricity

Prof. Luca Mottola -Mobile Drone Computing

Get to know MECE Webinar, March 2021

Dr. Alan Swann -Temporal Architecture of Suicidal Behavior and Risk

Prof. Mihaela van der Schaar -Why medicine is creating exciting new frontiers for machine learning

Prof. Jean Jacques Greffet -Quantum plasmonics

Prof. Christoph Studer -Low-Resolution to the Rescue of All-Digital Massive MIMO

Prof. Mercedeh Khajavikhan -Non-Hermitian and Topological Photonics: Optics at an Exceptional Point

Prof. Jiasi Chen - Networked Augmented and Virtual Reality for Mobile Devices

Prof. Daniel Bliss - DASH: Enabling the Next Generation of Advanced Software-Defined RF Systems

Prof. Kaushik Chowdhury - Experiments with Learning, Sensing and Communications in UAVs

Prof. Olgica Milenkovic - Submodular Hypergraph Partitioning with Applications

Prof. Urbashi Mitra - Data-driven Decision Making and Estimation in CPS/IoT Networks

Prof. Hai “Helen” Li - Efficient Deep Learning at Scale