Yang Cai is currently finishing his Ph.D. at MIT under the supervision of Constantinos Daskalakis. He received his Bachelor degree in Computer Science from Peking University. His research interests include Algorithmic Game Theory, Online Algorithms and Logic

Christopher Pal is an assistant professor in the department of « génie informatique et génie logiciel » at the École Polytechnique Montréal. Prior to arriving in Montréal, he was an assistant professor in the department of Computer Science at the University of Rochester. He has been a research scientist with the University of Massachusetts Amherst at the Center for Intelligent Information Retrieval and Information Extraction and Synthesis Lab. He has also been affiliated with Microsoft Research's Interactive Visual Media Group and their Machine Learning and Adaptive Systems group. He has four patents, there are over 1100 citations to his work on Google Scholar and he has been the recipient of a Google research award. He earned his M. Math and Ph.D. from the University of Waterloo

For those of us who have an empirical component in our research, there are ample of opportunities to present experimental results in one graphical form or another. In this tutorial, I will present some examples of graphs I have produced for a paper. Bring your laptop and you can produce some graphs too.

I will be in MC103 at 11h30 to assist anyone who wants to set up R in their laptop. The SOCS machines also have R installed if you want to VNC in.

If you know R already, please contact me if you can help. It'd be great if we have a few people at 11h30 to help others with the installation and during the tutorial.

The difficulty of solving combinatorial optimization problems efficiently has led to the design of approximation algorithms. Unfortunately, approximation algorithms are often quite complicated and specialized, which limits their applicability. In this talk, we discuss a fundamental combinatorial optimization problem, monotone submodular maximization over a matroid constraint (MSMM). This problem generalizes classical problems such as Maximum Coverage and Maximum Satisfiability. The simple greedy algorithm achieves the best possible approximation ratio for Maximum Coverage. However, for the more general MSMM problem, achieving the best approximation ratio required the sophisticated continuous greedy algorithm. We present an elementary non-oblivious local search algorithm, which is also theoretically optimal. Our algorithm is much simpler than the existing continuous greedy algorithm, and moreover it may generalize to new situations in which the continuous greedy approach fails.

Over the past few years, Twitter has emerged as an increasingly influential platform for real-time information distribution and discovery. However, it has been taking a bit of dark turn into adult content, which could significantly hinder the further popularity of Twitter and/or cause serious legal issues. Existing techniques for adult content detection are ill-suited for detecting adult accounts in Twitter.

To tackle this problem, we propose iterative social based classification (ISC)}, an effective solution for adult account detection for online social networks. ISC consisting of three key components: (1) collective interest, a novel social link based feature for effective discrimination of adult accounts; (2) a tag based label propagation algorithm which explores the tagged content embedded in tweets to further boost the detection accuracy; and (3) a social based linear classifier that integrates social consistency based on the collective interest feature and maximal tag scores computed from tag based label propagation. Evaluations using large-scale real-world Twitter data demonstrate that our ISC solution significantly outperforms existing methods in detecting adult accounts. It is able to identify adult accounts accurately among $1.07$ million accounts using only $100$ labeled adult accounts.

Vida Dujmovic is an Assistant Professor at the School of Mathematics and Statistics & Department of Systems and Computer Engineering of Carleton University. She has obtained her PhD and MSc from McGill University and her BEng from University of Zagreb in Croatia. She is a recipient of NSERC Doctoral Prize and, more recently, Faculty Research Achievement Award of Carleton University.

Edith Law is a CRCS postdoctoral fellow at the School of Engineering and Applied Sciences at Harvard University. She graduated from Carnegie Mellon University in 2012 with Ph.D. in Machine Learning, where she studied human computation systems that harness the joint efforts of machines and humans. She is a Microsoft Graduate Research Fellow, co-authored the book "Human Computation" in the Morgan & Claypool Synthesis Lectures on Artificial Intelligence and Machine Learning, co-organized the Human Computation Workshop (HCOMP) Series at KDD and AAAI from 2009 to 2012, and helped create the first AAAI Conference on Human Computation and Crowdsourcing. Her work on games with a purpose and large-scale collaborative planning has received best paper honorable mentions at CHI.

Jeff Huang is a PhD Candidate in Information Science at the University of Washington. His research in data-driven information retrieval focuses on modeling users from interaction data. He has been awarded Best Paper at SIGIR 2010, two Honorable Mentions at CHI 2011, and the Facebook Fellowship. During his graduate studies, Jeff has conducted research at the University of Washington and five research groups at Microsoft Research and Google, and has received external funding from Google and Microryza. His work appears in venues such as SIGIR, CHI, AAAI, UIST, CIKM, WSDM, as well as in the Wall Street Journal, GeekWire, and the MIT Technology Review. Jeff earned his Masters and Bachelors degrees in Computer Science at the University of Illinois.

Kevyn Collins-Thompson is a Researcher in the Context, Learning and User Experience for Search (CLUES) group at Microsoft Research. His core research is in information retrieval and text mining, including a strong interplay with statistical machine learning, mathematical optimization, economics, and natural language processing. His research focuses on models, algorithms, and evaluation methods for improved search and text understanding, especially for human learning. Kevyn received his Ph.D. in Computer Science from the Language Technologies Institute at Carnegie Mellon University and B.Math. in Computer Science from the University of Waterloo. He is a co-recipient of an ACM SIGIR’12 outstanding paper award for his work with Lidan Wang and Paul Bennett on "Robust ranking via risk-sensitive optimization".

Theo Gevers is an Associate Professor of Computer Science at the University of Amsterdam, The Netherlands and a Full Professor at the Computer Vision Center (UAB), Barcelona, Spain. At the University of Amsterdam he is a teaching director of the MSc of Artificial Intelligence. He currently holds a VICI-award (for excellent researchers) from the Dutch Organisation for Scientific Research. His main research interests are in the fundamentals of content-based image retrieval, colour image processing and computer vision specifically in the theoretical foundation of geometric and photometric invariants. He is co-chair of the Internet Imaging Conference (SPIE 2005, 2006), co-organizer of the First International Workshop on Image Databases and Multi Media Search (1996), the International Conference on Visual Information Systems (1999, 2005), the Conference on Multimedia & Expo (ICME, 2005), and the European Conference on Colour in Graphics, Imaging, and Vision (CGIV, 2012). He is guest editor of the special issue on content-based image retrieval for the International Journal of Computer Vision (IJCV 2004) and the special issue on Colour for Image Indexing and Retrieval for the journal of Computer Vision and Image Understanding (CVIU 2004). He has published over 100 papers on colour image processing, image retrieval and computer vision. He is program committee member of a various number of conferences, and an invited speaker at major conferences. He is a lecturer of post-doctoral courses given at various major conferences (CVPR, ICPR, SPIE, CGIV). He is member of the IEEE.

Dr. Sidiropoulos received his M.S. and Ph.D. in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, under the supervision of Piotr Indyk. He has been a post-doctoral fellow at the University of Toronto, and the Toyota Technological Institute at Chicago. He is currently a post-doctoral fellow in the Computer Science department at the University of Illinois at Urbana-Champaign. His main research interests are in algorithms, with an emphasis on metric embeddings, high-dimensional and low-dimensional computational geometry, graph theory, optimization, and applications of geometry and topology in computer science.

Thomas Vidick is currently a postdoctoral associate in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. His research interests are in quantum computing and complexity theory, and he has made contributions to the theory of quantum multi-prover interactive proofs, quantum cryptography, pseudo-randomness, and approximation algorithms. He completed his Ph.D. in Computer Science from UC Berkeley in Fall 2011, working with Umesh Vazirani. His thesis was awarded the Bernard Friedman Memorial Prize in applied mathematics. He is a co-recipient of the FOCS'12 best paper award for his paper with Tsuyoshi Ito on "A multi-prover interactive proof for NEXP sound against entangled provers".

MATLAB is the most popular language among scientists for solving numerically intensive problems but it does not allow scientists to take full advantage of today’s supercomputers which they have access to. We try to solve this problem by developing a MATLAB to X10 translator.

X10 is a programming language being developed by IBM research to provide a programming model that provides scalability and productivity for new generation supercomputing architectures. X10 is based on four basic principles of asynchrony, locality, atomicity, and order developed on a type-safe, class-based, object-oriented foundation.

MiX10 is a part of the McLab project at SABLE lab that provides a static backend with X10 as the target language for our MATLAB compiler. Two major challenges in translating MATLAB to X10 are 1) Mapping programming constructs of a matrix-based, dynamic and “wild” language to those of a statically typed, object-oriented, parallel programming language and 2) Providing support for a huge number of MATLAB builtin methods that are a major reason behind its success as a scientific programming language. MiX10 builds upon McLab’s Tamer analysis framework and aims for performance and readability of generated X10 code.