Computing in Engineering Forum 2020

Biography

Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago. Her research is focused on machine learning, signal processing, and large-scale data science. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018. Willett received the National Science Foundation CAREER Award in 2007, is a member of the DARPA Computer Science Study Group, and received an Air Force Office of Scientific Research Young Investigator Program award in 2010. Willett has also held visiting researcher or faculty positions at the University of Nice in 2015, the Institute for Pure and Applied Mathematics at UCLA in 2004, the University of Wisconsin-Madison 2003-2005, the French National Institute for Research in Computer Science and Control (INRIA) in 2003, and the Applied Science Research and Development Laboratory at GE Healthcare in 2002.

Research

My research interests include signal processing, machine learning, and large-scale data science. In particular, I have studied methods to leverage low-dimensional models in a variety of contexts, including when data are high-dimensional, contain missing entries, are subject to constrained sensing or communication resources, correspond to point processes, or arise in ill-conditioned inverse problems. This work lies at the intersection of high-dimensional statistics, inverse problems in imaging and network science (including compressed sensing), learning theory, algebraic geometry, optical engineering, nonlinear approximation theory, statistical signal processing, and optimization theory. My group has made contributions both in the mathematical foundations of signal processing and machine learning and in their application to a variety of real-world problems. I have active collaborations with researchers in astronomy, materials science, microscopy, electronic health record analysis, cognitive neuroscience, precision agriculture, biochemistry, and atmospheric science.

Since 2000, Russell Allgor, Chief Scientist for Amazon.com, has led a team of mathematical modeling experts in Amazon’s Worldwide Operations organization. This talented group focuses on using data, mathematical modeling, simulation, and optimization methods to improve the efficiency of Amazon’s operations. They focus on problems including network design and facility location, inventory planning, order assignment, equipment and process design, routing, and process control within and across facilities. Ideas and algorithms developed by Russell and his team have returned hundreds of millions of dollars to Amazon’s bottom line.

Prior to joining Amazon.com, Russell worked in the applied research and development department for Bayer AG in Leverkusen, Germany working on the design and optimization of batch and continuous chemical processes, including the color red for Legos. Prior to that he was with Air Products and Chemicals, working in process simulation and design. He received a PhD in chemical engineering from Massachusetts Institute of Technology (MIT), where his research focused on modeling and optimization of discrete continuous dynamic systems. He holds a Bachelor of Science in chemical engineering from Princeton University.

He received the Martin K. Starr Award in 2016 which recognizes contributions made to the field of Production and Operations Management (POM) by POM practitioners. He also serves as the Vice President of Industry for POMS.

Russell is originally from Ocean, New Jersey and currently resides in Seattle, Washington.

Dr. Kevin Eliceiri is the Walter H. Helmerich Professor of Medical Physics and Biomedical Engineering and Vilas Associate at the University of Wisconsin-Madison. He is an investigator in the Morgridge Institute for Research and member of the Carbone Cancer Center and McPherson Eye Research Institute. His research group, the Laboratory for Optical and Computational Instrumentation (LOCI), is a computational optics laboratory dedicated to the development and application of optical and computational technologies for cell studies.

The Eliceiri lab is the lead developer of several open-source imaging packages including FIJI, ImageJ and MicroManager. He also is co-lead of the newly formed NIH P41 center: “Center for Open Bioimage Analysis” (COBA). His instrumentation efforts involve novel forms of polarization, laser scanning and multiscale imaging.  Dr. Eliceiri has authored more than 200 scientific papers on various aspects of optical imaging, image analysis, cancer and live cell imaging.