Advancing Computer Science Careers through Enhanced Networking and Training
ASCENT Fellows
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Yossi Oren is a Postdoctoral Research Scholar in the Department of Computer Science at Columbia University in the City of New York and a member of the Network Security Lab, led by Professor Angelos Keromytis. He holds a Ph.D. in Electrical Engineering from Tel-Aviv University and an M.Sc. in Computer Science from the Weizmann Institute of Science. His main research interests are hardware and architectural security (low-resource cryptographic constructions, power analysis and other hardware attacks and countermeasures) and Network Security (cyber-physical system security, consumer and voter privacy, web application security). In his free time he likes to get lost in the NYC subway.
Hossein Hojjat is a Postdoctoral Researcher in the Department of Computer Science at Cornell University. His reasearch focuses on analysis and verification of software and hardware systems. He currently works with Professor Nate Foster. He finished his Ph.D. in under the supervision of Professor Viktor Kuncak at EPFL. He received his Master's degree in Software Engineering from Tehran University in 2007 while working with Professor Marjan Sirjani (Reykjavík University) and Professor Mohammad Reza Mousavi (TU/e Eindhoven). He received his Bachelor's degree in Software Engineering from Tehran University.
Mingxing Tan is a Postdoctoral Associate in the School of Electrical and Computer Engineering at Cornell University. His research interests are in computer architecture and computer-aided design, especially in the interaction between programming language, compiler, microarchitecture and high-level synthesis for energy-efficient and reconfigurable computing. He currently works in the Computer Systems Lab (CSL) and collaborates with Professor Zhiru Zhang. Prior to joining Cornell, he worked as a research assistant and lead the compiler group in the Microprocessor Research and Development Center at Peking University, where he received his Ph.D. in Computer Science with the highest honors in 2013.
Narges Razavian is a Postdoctoral Associate in the Machine Learning Group at the Courant Institute of Mathematical Sciences at New York University. She works on probabilistic graphical models applied to bio-medical data. In September 2013, she received her Ph.D. in the field of Computational Biology from Carnegie Mellon University, with a focus on probabilistic graphical models (sparse structure learning, parameter estimation and inference) and Kernel space models for continuous, time varying models of protein structure. Her specialties are graphical models, Kernel embedding of multivariate distributions, regression models, learning sparse models, continuous multivariate models, time varying continuous multivariate models, expectation propagation, nonparametric Bayesian models, Gibbs sampling, iPhone application development, and natural language processing.
Dong Liu is a Postdoctoral Research Scientist in the Digital Video | Multimedia Lab at Columbia University, supervised by Professor Shih-Fu Chang. His research interests are large-scale multimedia retrieval, computer vision, and novel machine learning techniques for enabling “Next-Generation Visual Search Engines." He obtained his Ph.D. degree in the School of Computer Science and Technology at the Harbin Institute of Technology, advised by Dr. Hong-Jiang Zhang. Previously, he investigated new machine learning and information retrieval theoretical approaches for solving the real-world challenges in visual information retrieval problems. In particular, he has research interests in three major domains: (1) content-based tag processing for Internet social images; (2) multimedia event detection in consumer videos; and, (3) novel machine learning techniques for visual understanding.
Alec Jacobson is a Postdoctoral Researcher in the Columbia Computer Graphics Group (C2G2) at Columbia University. He received his B.A. and M.A. in Mathematics and Computer Science from the Courant Institute of Mathematical Sciences at New York University, and his Ph.D. in Computer Science from ETH Zurich. His thesis on real-time deformation techniques for 2D and 3D shapes was awarded the ETH Medal and the Eurographics Best PhD award. His research incorporates finite-element methods and constrained optimization to design automatic weighting functions for skinning. He is also interested in novel input devices for character animation and robust meshing techniques for 3D volumes. He has published several papers in the proceedings of SIGGRAPH.
Uri Shalit will be a Postdoctoral Associate at New York University beginning in January 2015. He is currently a Ph.D. student at the Interdisciplinary Center for Neural Computation and at the School of Computer Science and Engineering at the Hebrew University of Jerusalem. His advisors are Gal Chechik and Daphna Weinshall. His research is currently focused on two different themes: (1) understanding the role of influence and innovation in contemporary music using computational tools; and (2) using matrix manifold optimization tools to build better and faster machine learning algorithms. Previously, he has collaborated with neuroscientists and computational biologists in develpoing machine learning algorithms for interpreting the complex data arising from cutting edge brain research. Since 2011, he has been a recipient of Google's European Fellowship in Machine Learning.
Assaf Glazer is a Postdoctorate in the Runway Program at the Jacobs Institute at Cornell Tech. He completed his PhD at the Technion - Israel Institute of Technology in 2013, specializing in the fields of machine learning and computer vision. Assaf has always strived to understand the boundaries between the academic and practical worlds. In 2001, Assaf started his career at Wales Ltd. as an operational researcher, providing solutions for defense systems while applying a variety of professional and academic skills. In 2004, Assaf joined Applied Materials Inc. as an algorithm researcher, dealing with the development of image classification methods for process control in the semiconductor industry. In addition, he coordinated joint research projects of the Academia, Industry and Chief Scientist Bureau (Israel). During his stay at Cornell Tech, Assaf is engaging in research of an intelligence vision system for the healthcare market, based on his expertise in the fields of machine learning, computer vision, and process control.
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Alp Kucukelbir is a Postdoctoral Research Scientist at the Institute for Data Sciences and Engineering and the department of Computer Science at Columbia University. He works with David Blei on probabilistic programming methods to develop scalable and generic machine learning algorithms. He applies his work in structural biology and proteomics. Alp received his Ph.D. from Yale University, where he was awarded the Becton Prize for best thesis in engineering. He holds a B.A.Sc. from the University of Toronto. Before joining Columbia University, Alp had started his postdoc with David Blei at Princeton University.
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Konstantin Bauman is a Postdoctoral Research Scholar in the Information Operations Mamangement Sysytems Department at NYU's Stern School of Business. He works with Alexander Tuzhilin on the Recommender Systems project. Konstantin received an M.A. degree in Mathematics from Moscow State Lomonosov University in 2008, an M.A. degree in Data Mining from Moscow Institute of Physics and Technology in 2009, and he defended his dissertation in Geometry and Topology at the Mathematical Institute Russian Academy of Science in 2012. Before joining NYU, Konstantin worked for five years with a research group at Yandex. His current research interests include machine learning, recommender systems, technology enhanced learning, and natural language processing.
Stephan Mandt is a Postdoctoral Researcher with David Blei at the Institute for Data Sciences and Engineering at Columbia University. He was a PCCM fellow in theoretical physics at Princeton University. His Ph.D. supervisor was Achim Rosch at the Institute for Theoretical Physics at the University of Cologne (Germany). He was also a fellow of the German National Academic Foundation. He received a diploma (M.Sc. equivalent) in physics from the University of Cologne, supervised by Martin Zirnbauer. Stephan works in the field of statistical machine learning. In particular, he is interested in scalable Bayesian methods that map statistical inference to stochastic optimization. He is also interested in statistical physics and quantum many-body dynamics. His research interests include approximate Bayesian inference, variational methods, simulated annealing, stochastic optimization, statistical mechanics and computational physics, non-equilibrium systems, and ultracold atoms.
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Ozgur Ozkan is currently a Postdoc Research Assistant Professor in the Computer Science and Engineering Department at New York University Polytechnic School of Engineering. His research interests lie in Theoretical Computer Science, especially the design and analysis of algorithms and data structures. Oz received his Ph.D. from NYU:Poly (advised by John Iacono). Before coming to NYU:Poly, he was a member of the Algorithms Lab Seminar Group at the New Jersey Institute of Technology under Dr. Artur Czumaj, where he finished his undergraduate studies in Computer Science.
Jason Fuller is a Postdoctoral Fellow at New York University's Clayspace-Curtis Lab in the Department of Psychology. His research focuses on cognition and perception and the neural mechanisms of motor learning. He received his Ph.D. from McGill University and his M.Sc. from the University of Guelph.
Veeresh Thummadi is a Postdoctoral Research Scientist at the New York University Stern School of Business, where he focuses on statistics, econometrics, data science, visualization and computing. He received his Ph.D. in Management and Information Systems in 2013 and his M.Eng. in 2009 from Case Western Reserve University. He worked previously as a systems engineer. In his free time, he volunteers with New York Cares and Asha for Education.