School of Engineering
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David Thomas
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2017
Masters Student in Computational and Mathematical Engineering, admitted Autumn 2016BioAfter graduating from MIT with computer science and mathematics degrees, I spent three years 'open sourcing' global derivatives markets. I came to graduate school to 'open source' the universe. I work on the Large Synoptic Survey Telescope, a giant telescope that will produce the widest and deepest map of our universe and help resolve the mysteries of dark matter and dark energy. The algorithms and methods I develop have applicability outside of physics and cosmology and I welcome opportunities to collaborate with research groups in industry.

Johan Ugander
Assistant Professor of Management Science and Engineering
BioUgander's research develops algorithmic and statistical frameworks for analyzing social networks, social systems, and other largescale datarich contexts. He is particularly interested in the challenges of causal inference and experimentation in these complex domains. His work commonly falls at the intersections of graph theory, statistics, optimization, and algorithm design.

Jeremy Utley
Adjunct Professor, Hasso Plattner Institute of Design
BioJeremy currently leads the d.school's work with organizations as Director of Executive Education. In this role, he advises professionals and organizations on how to imbed the tools of design thinking and cultivate an innovative organizational culture. He also teaches the celebrated d.leadership and LaunchPad classes, advanced d.school courses focused on creating realworld impact with the tools of design.
Jeremy never expected to be a designer. On his 10th birthday, his father asked him what he wanted to be when he grew up. Jeremy replied,”I want to be one of the people who carry boxes with handles.” A little over a decade later, Jeremy became a briefcasecarrying management consultant focusing on economic development. Then, in 2008, the d.school derailed him completely. His time as a student and a fellow at the d.school showed him that “how” he worked was more important than “what” he did. Today, Jeremy is dedicated to helping others along the same path to becoming a designer. He helps people change their deeplyengrained behaviors and discover, as he did, that it is possible for them to make a difference. He does this through teaching as well as through growing alongside his students to become better in his own life and work every day.
Jeremy is the Director of Executive Education at the d.school. He is a graduate of The University of Texas at Austin’s Red McComb’s School of Business (2005) and the Stanford University Graduate School of Business (2009). 
Benjamin Van Roy
Professor of Electrical Engineering, of Management Science and Engineering and, by courtesy, of Computer Science
BioBenjamin Van Roy is a Professor of Electrical Engineering, Management Science and Engineering, and, by courtesy, Computer Science, at Stanford University, where he has served on the faculty since 1998. His research focuses on understanding how an agent interacting with a poorly understood environment can learn over time to make effective decisions. He is interested in questions concerning what is possible or impossible as well as how to design efficient learning algorithms that achieve the possible. His research contributes to the fields of reinforcement learning, online optimization, and approximate dynamic programming, and offers means to addressing central problems of artificial intelligence.
He has graduated fifteen doctoral students, published over forty articles in peerreviewed journals, and been listed as an inventor in over a dozen patents. He has served on the editorial boards of Machine Learning, Mathematics of Operations Research, and Operations Research, for which he has also served as editor of the Financial Engineering Area. He has also founded and/or led research programs at several technology companies, including Unica (acquired by IBM), Enuvis (acquired by SiRF), and Morgan Stanley.
He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master's Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, and the Management Science and Engineering Department's Graduate Teaching Award. He is an INFORMS Fellow and has been a Frederick E. Terman Fellow and a David Morgenthaler II Faculty Scholar. He has held visiting positions as the Wolfgang and Helga Gaul Visiting Professor at the University of Karlsruhe and as the Chin Sophonpanich Foundation Professor and the InTouch Professor at Chulalongkorn University. 
Varun Vasudevan
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2016
Current Research and Scholarly InterestsMaster's Thesis
Title: Best rank1 approximations without orthogonal invariance for the 1norm.
Abstract: Data measured in the realworld is often composed of both a true signal, such as an image or experimental response, and a perturbation, such as noise or weak secondary effects. Lowrank matrix approximation is one commonly used technique to extract the true signal from the data. Given a matrix representation of the data, this method seeks the nearest lowrank matrix where the distance is measured using a matrix norm.
The classic EckartYoungMirsky theorem tells us how to use the Singular Value Decomposition (SVD) to compute a best lowrank approximation of a matrix for any orthogonally invariant norm. This leaves as an open question how to compute a best lowrank approximation for norms that are not orthogonally invariant, like the 1norm.
In this thesis, we present how to calculate the best rank1 approximations for 2byn and nby2 matrices in the 1norm. We consider both the operator induced 1norm (maximum column 1norm) and the Frobenius 1norm (sum of absolute values over the matrix). We present some thoughts on how to extend the arguments to larger matrices.