School of Engineering
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Kasper van der Vaart
Postdoctoral Research Fellow, Civil and Environmental Engineering
BioKasper van der Vaart studied physics and astronomy at the University of Amsterdam, where his bachelor thesis, supervised by Tom Gregorkiewicz and Wieteke de Boer, was on the topic of photoluminescence of silicon nanocrystals. Afterwards he went to the University of Utrecht to obtain a M.Sc. in Nanomaterials, focussing mostly on nanophotonics and soft matter. Following a project back in Amsterdam on the rheology of colloidal glasses (supervised by Peter Schall) he sought to apply his new found knowledge on rheology to a more everyday material. Thus he ventured in to the field of food technology and performed his master project on chocolate flow behaviour at the laboratory of Food Technology and Engineering at Ghent University, under supervision of Koen Dewettinck. Wanting to visualize the actual particle motion in a soft material, Kasper went to the EPFL in Switzerland. There he investigated particlesize segregation in granular avalanches through both experiments and simulations, in the lab of Christophe Ancey. During this work Kasper collaborated closely with Nico Gray and Anthony Thornton. His current work at Stanford focusses on the collective behavior and emergent properties of midge swarms, in order to determine what constitutes collective behavior, how it can be quantified and how we can compare different collectively behaving organisms.

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.