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Vincent Yan Fu Tan

University of Wisconsin, Madison Electrical and Computer Engineering 3533 Engineering Hall, 1415 Engineering Drive Madison, WI, 53706 Citizenship: Singapore Web: http://homepages.cae.wisc.edu/~vtan/ 183 3rd St Apt 4 Cambridge, MA, 02141 Email: [email protected] Tel: 1-617-913-4213 Birth: Jul 11, 1981

Education:

2007 ­ 2010 Stochastic Systems Group, Laboratory of Information and Decision Systems, Massachusetts Institute of Technology Ph.D. in Electrical Engineering and Computer Science (EECS) Thesis Title: Large-Deviations Analysis and Applications of Learning TreeStructured Graphical Models (Jin-Au Kong Outstanding Thesis Prize) Research Advisor: Prof. Alan S. Willsky Massachusetts Institute of Technology (EECS) Exchange Student under the Cambridge-MIT Institute, GPA 5.0/5.0 Training in Statistical Signal Processing Sidney Sussex College, University of Cambridge B.A. (First-Class Honors) and M.Eng. (Distinction) in the Electrical and Information Sciences Tripos (EIST); Top student in EIST (Charles Lamb Prize) Master's Thesis Title: Blind Audio Source Separation Research Advisor: Dr. Cédric Févotte

2003 ­ 2004

2001 ­ 2003 2004 ­ 2005

Experience:

2010 ­ present Electrical and Computer Engineering, University of Wisconsin-Madison Post-Doctoral Researcher Hosts: Prof. Stark Draper and Prof. Robert Nowak Performed research in coding and information theory Massachusetts Institute of Technology (EECS) Research Assistant for Prof. Alan S. Willsky Performed research in statistical signal processing and machine learning HP Labs, Palo Alto, CA Visiting Scientist hosted by Dr. Majid Fozunbal and Dr. Mitch Trott Applied estimation-theoretic techniques to infer fading channel parameters

2007 ­ 2010

Jan 2010

Summer 2009

E-Science Research Group, Microsoft Research Los Angeles, CA Researcher under Dr. David Heckerman and Dr. Jonathan Carlson Applied machine learning techniques to infer the structure and parameters of evolutionary trees CNRS Telecom ParisTech, Paris, France Visiting Scientist hosted by Dr. Cédric Févotte Performed research on model selection for nonnegative matrix factorization Machine Learning and Perception Group, Microsoft Research Cambridge, UK Researcher under Prof. Christopher Bishop and Dr. John Winn Applied advanced Bayesian graphical modeling techniques to categorize childhood asthma classes automatically Data Mining Dept, Institute for Infocomm Research (I2R), A*STAR, Singapore Research Engineer under Prof. See-Kiong Ng Performed extensive research on randomization techniques for privacypreserving data mining Defense Medical Environmental Research Institute (DMERI), DSO, Singapore Software Engineer: Designed visualization techniques for military use Signal Processing Lab, Engineering Department, Cambridge University Master's Thesis Supervisor: Dr. Cédric Févotte Researched on effect of sparsity on audio source separation performance Applied and Computational Mathematics (ACM) Department, California Institute of Technology, CA Summer Undergraduate Research Fellow under Prof. Oscar Bruno Designed numerical algorithms for modeling surface scattering

Summer 2008

Summer 2008

2006 ­ 2007

2005 ­ 2006 2004 ­ 2005

Summer 2004

Research:

General research interests: Design and analysis of graphical model (Markov random field) learning algorithms, approximate inference, statistical analysis of clinical and biological data, statistical and digital signal processing, information theory, convex optimization, network information theory, coding theory Current research interests: Analysis of rank-minimization algorithms over finite fields and connections to coding theory such as rank-metric codes, network information theory, large alphabets, graphical models

Technical Skills:

Multiscale signal processing (wavelets and compression), Statistical signal processing (detection and estimation theory), Machine learning (graphical models and information geometry), Information theory (method of types), Mathematical analysis (measure theory, manifolds, differential analysis), Theoretical statistics (high-dimensional statistics), Convex optimization Programming Languages: Java, C++/C#, Matlab Extensive experience in TeX for typesetting Languages: English, Mandarin Chinese

Teaching:

Fall 2011 University of Wisconsin-Madison (ECE) Instructor for graduate class ECE 901: Multiterminal Information Theory Designed course, taught 9 lectures, designed problem sets and solutions Massachusetts Institute of Technology (EECS) Teaching assistant for graduate class 6.437 Inference and Information Conducted recitations and office hours, designed exams and problem sets Rating: 6.4/7.0 (22 students) Massachusetts Institute of Technology (EECS) Teaching assistant for graduate class 6.241 Dynamical Systems and Control Conducted recitations and office hours, designed exams and problem sets Rating 6.5/7.0 (20 students) Massachusetts Institute of Technology (EECS) Co-instructor for Independent Activities Period (IAP) course 6.097 Review of Signals and Systems Designed syllabus, taught lectures, designed problem sets National University of Singapore (ECE) Tutor for course ECE 2012 Analytical Techniques in ECE Conducted weekly tutorials Massachusetts Institute of Technology (EECS) Tutor to three undergraduate students in EECS for the undergraduate class 6.011 Introduction to Signal Processing, Control and Communications

Spring 2010

Fall 2008

Jan 2010 Jan 2009 Jan 2008 Fall 2006

Spring 2004

Services:

Jan 2009 Co-chair of the 14th annual Laboratory for Information and Decision Systems (LIDS) student conference Invited four distinguished speakers from industry and academia to the conference Reviewer for various international journals such as Journal for Machine Learning Research, Signal Processing, IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Signal Processing Letters Reviewer for various international conferences such as the International Symposium on Information Theory (ISIT) and the Neural Information Processing Systems (NIPS)

Membership:

2007 - present Member, IEEE (Institute of Electrical and Electronics Engineers) Information Theory Society

Awards and Grants:

2011 2011 2009 2006 2005 2004 2003 ­ 2004 2001 Philip Yeo Prize for Outstanding Achievement in Research EECS Jin-Au Kong Outstanding Doctoral Thesis Prize Student Travel Award, International Symposium on Information Theory (ISIT) in Seoul, Korea A*STAR National Science Scholarship (PhD) Full funding for PhD studies at MIT Charles Lamb Prize Top student in Electrical Engineering in Cambridge University Summer Undergraduate Research Fellowship, Caltech Grant from Cambridge-MIT Institute (CMI) for exchange year at MIT Overseas Merit Scholarship, Public Service Commission (PSC) Full funding for undergraduate studies in Cambridge University

Book Chapters:

Vincent Y. F. Tan and See-Kiong Ng "Privacy-Preserving Sharing of Horizontally-Distributed Private Data for Constructing Accurate Classifiers" Lecture Notes in Computer Science, 2008, Volume 4890, Privacy, Security and Trust in KDD

Journal Publications:

Vincent Y. F. Tan and Cédric Févotte "Automatic Relevance Determination in Nonnegative Matrix Factorization with the -Divergence" Submitted to the IEEE Transactions on Pattern Analysis and Machine Intelligence, Nov 2011 Jonathan M. Carlson, Jennifer Listgarten, Nico Pfeifer, Vincent Y. F. Tan, Carl Kadie, Bruce D. Walker, Thumbi Ndung'u, Roger Shapiro, John Frater, Zabrina L. Brumme, Philip J. R. Goulder, David Heckerman, "Widespread Impact of HLA Restriction on Immune Control and Escape Pathways in HIV1" Submitted to Journal of Virology, Nov 2011 Tzu-Han Chou, Vincent Y. F. Tan, and Stark C. Draper "The Sender-Excited Secret Key Agreement Model: Capacity and Error Exponents" Submitted to the IEEE Transactions on Information Theory, Jul 2011 Animashree Anandkumar, Vincent Y. F. Tan and Alan S. Willsky "HighDimensional Structure Estimation in Ising Models: Tractable Graph Families" Submitted to the Annals of Statistics, Jul 2011 Animashree Anandkumar, Vincent Y. F. Tan and Alan S. Willsky "HighDimensional Gaussian Graphical Model Selection: Tractable Graph Families" Submitted to the Journal of Machine Learning Research, Jul 2011 Vincent Y. F. Tan, Laura Balzano and Stark C. Draper "Rank Minimization over Finite Fields: Fundamental Limits and Coding-Theoretic Interpretations" IEEE Transactions on Information Theory, 2011 (In press) Myung Jin Choi, Vincent Y. F. Tan, Animashree Anandkumar and Alan S. Willsky "Learning Latent Tree Graphical Models" Journal of Machine Learning Research, May 2011 Vincent Y. F. Tan, Animashree Anandkumar and Alan S. Willsky "Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates" Journal of Machine Learning Research, May 2011

Vincent Y. F. Tan, Animashree Anandkumar, Lang Tong and Alan S. Willsky "A Large-Deviation Analysis for the Maximum-Likelihood Learning of Markov Tree Structures" IEEE Transactions on Information Theory, Mar 2011 (Winner of Philip Yeo Prize for Outstanding Achievement in Research) Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher III and Alan S. Willsky "Learning Graphical Models for Hypothesis Testing and Classification" IEEE Transactions on Signal Processing, Nov 2010 Angela Simpson*, Vincent Y. F. Tan*, John Winn, Markus Svénsen, Chris Bishop, David Heckerman, Iain Buchan and Adnan Custovic "Beyond Atopy: Multiple Patterns of Sensitization in Relation to Asthma in a Birth Cohort Study" American Journal of Respiratory and Critical Care Medicine, Jun 2010 (*Co-first Authorship) Vincent Y. F. Tan, Animashree Anandkumar and Alan S. Willsky "Learning Gaussian Tree Models: Analysis of Error Exponents and Extremal Structures" IEEE Transactions on Signal Processing, May 2010 Vincent Y. F. Tan and Vivek K Goyal "Estimating Signals with Finite Rate of Innovation from Noisy Samples: A Stochastic Algorithm" IEEE Transactions on Signal Processing, Oct 2008

Conference Publications (Refereed):

Vincent Y. F. Tan and Cédric Févotte "Automatic Relevance Determination in Nonnegative Matrix Factorization with the -Divergence" NIPS Workshop on Sparse Representation and Low-rank Approximation 2011, Granada, Spain Animashree Anandkumar, Vincent Y. F. Tan and Alan S. Willsky "HighDimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions" Neural Information Processing Systems (NIPS), 2011, Granada, Spain (Oral Presentation) Vincent Y. F. Tan and Alan S. Willsky "Sample Complexity for Topology Estimation in Networks of LTI Systems" Control and Decision Conference (CDC), Orlando, FL, Dec 2011 Tzu-Han Chou, Vincent Y. F. Tan and Stark C. Draper "The Sender-Excited Secret-Key Agreement Model: Capacity Theorems" Allerton Conference Comm., Control and Computing, 2011, Monticello, IL

Mesrob I. Ohannessian, Vincent Y. F. Tan and Munther A. Dahleh "Canonical Estimation in a Rare Events Regime" Allerton Conference Comm., Control and Computing, 2011, Monticello, IL Animashree Anandkumar, Vincent Y. F. Tan and Alan S. Willsky "Structure Estimation of Graphical Models using Local Algorithms" Allerton Conference Comm., Control and Computing, 2011, Monticello, IL Donatello Materrasi and Vincent Y. F. Tan "Reconstruction of Polytree Networks of Dynamical Systems with Latent Nodes" Allerton Conference Comm., Control and Computing, 2011, Monticello, IL Vincent Y. F. Tan, Laura Balzano and Stark C. Draper "Rank Minimization over Finite Fields" International Symposium on Information Theory (ISIT), 2011, St Petersburg, Russia Vincent Y. F. Tan and Alan S. Willsky "Sample Complexity for Topology Estimation in Networks of LTI Systems" 18th International Federation of Automatic Control (IFAC) World Congress 2011, Milan, Italy Animashree Anandkumar, Vincent Y. F. Tan and Alan S. Willsky "HighDimensional Robust Structure Learning of Ising Models on Sparse Random Graphs" NIPS Workshop on Robust Statistical Learning 2010, Vancouver, BC Myung Jin Choi, Vincent Y. F. Tan, Animashree Anandkumar and Alan S. Willsky "Consistent and Efficient Reconstruction of Latent Tree Models" Allerton Conference Comm., Control and Computing, 2010, Monticello, IL Vincent Y. F. Tan, Animashree Anandkumar and Alan S. Willsky, "Scaling Laws for Learning High-Dimensional Markov Forests Distributions" Allerton Conference on Comm., Control and Computing, 2010, Monticello, IL Vincent Y. F. Tan, Matthew Johnson and Alan S. Willsky, "Necessary and Sufficient Conditions for High-Dimensional Salient Feature Subset Recovery" International Symposium on Information Theory (ISIT), 2010, Austin TX Vincent Y. F. Tan, Animashree Anandkumar and Alan S. Willsky, "Error Exponents for Composite Hypothesis Testing of Markov Forest Distributions" International Symposium on Information Theory (ISIT), 2010, Austin TX Vincent Y. F. Tan, Animashree Anandkumar and Alan S. Willsky, "How do the Structure and Parameters of Gaussian Tree Models Affect Structure Learning" Allerton Conference Comm., Control and Computing, 2009, Monticello, IL

Vincent Y. F. Tan, Animashree Anandkumar, Lang Tong and Alan S. Willsky, "A Large-Deviations Analysis for the Maximum-Likelihood Learning of Tree Structures" International Symposium on Information Theory (ISIT) 2009, Seoul, Korea Vincent Y. F. Tan and Cédric Févotte, "Automatic Relevance Determination in Nonnegative Matrix Factorization" Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS) 2009, St Malo, France Vincent Y. F. Tan, John Winn, Angela Simpson and Adnan Custovic "Immune System Modeling using Infer.NET" IEEE Conference on e-Science and Cloud Computing, 2008, Indianapolis, IN Vincent Y. F. Tan, John W. Fisher III and Alan S. Willsky, "Learning MaxWeight Discriminative Forests" International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2008, Las Vegas, NV Sujay Sanghavi, Vincent Y. F. Tan and Alan S. Willsky, "Learning Graphical Models for Hypothesis Testing" IEEE Statistical Signal Processing (SSP) Workshop, 2007, Madison WI Vincent Y. F. Tan and See-Kiong Ng, "Privacy-Preserving Sharing of Horizontally-Distributed Private Data for Constructing Accurate Classifiers" First ACM SIGKDD Workshop on Privacy, Security and Trust in Knowledge Discovery and Data Mining (KDD), 2007, San Jose, CA Vincent Y. F. Tan and See-Kiong Ng, "Generic Probability Density Function Reconstruction for Randomization in Privacy-Preserving Data Mining" Machine Learning and Data Mining (MLDM), 2007, Leipzig, Germany Vincent Y. F. Tan and Cédric Févotte, "A Study of the Effect of Source Sparsity for Various Transforms on Blind Audio Source Separation Performance" Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS), 2005, Rennes, France

Conference Publications (Non-Refereed/Invited):

Animashree Anandkumar, Vincent Y. F. Tan and Alan S. Willsky "Structure Learning of Ising Models on Sparse Random Graphs" Information Theory and Applications (ITA) Workshop at UCSD, 2011, La Jolla, CA

Vincent Y. F. Tan and Vivek K Goyal "Estimating Signals with Finite Rate of Innovation From Noisy Samples: A Stochastic Algorithm" Sampling Theory and Applications (SAMPTA), 2009, Marseille, France Vincent Y. F. Tan, John W. Fisher III and Alan S. Willsky "Learning MaxWeight Discriminative Forests" Information Theory and Applications (ITA) Workshop at UCSD, 2008, La Jolla, CA

Selected Invited Presentations:

Rank Minimization over Finite Fields: Fundamental Limits and CodingTheoretic Interpretations University of Illinois at Urbana-Champaign (UIUC), Apr 2011 Structure Learning of Ising Models on Sparse Random Graphs, ITA Workshop, University California at San-Diego (UCSD), Feb 2011 Learning Graphical Models: Large-Deviations Analysis and Extensions, University of Texas-Austin, Nov 2010 Learning Graphical Models: Large-Deviations Analysis and Latent Models, University of Wisconsin-Madison, Oct 2010 Learning Graphical Models: Large-Deviation Analysis and Applications, School of Computing, National University of Singapore (NUS), Jun 2010 A Large-Deviations Analysis for Learning Graphical Models, Nanyang Technological University (NTU), Singapore, May 2010 A Large-Deviations Analysis for Learning Graphical Models, IBM Research, T.J. Watson Research Center, May 2010 Large-Deviations for Learning Graphical Models, Boston University, Apr 2010 Large-Deviations for Learning Tree Models, Harvard University, Mar 2010 Boosted Learning of Graphical Models for Hypothesis Testing, CNRS Telecom Paristech, Aug 2008 Learning Graphical Models for Hypothesis Testing, Lincoln Labs, Lexington, MA, Jan 2008

References:

Prof. Alan S. Willsky Director, LIDS, MIT 77 Massachusetts Avenue, 32-D582 Cambridge, MA 02139 [email protected] Electrical Engineering and Computer Science, MIT 77 Massachusetts Avenue, 36-690 Cambridge, MA 02139 [email protected] Electrical Engineering and Computer Science, MIT 77 Massachusetts Avenue, 36-660 Cambridge, MA 02139 [email protected] Electrical Engineering and Computer Science, MIT 32 Vassar Street, 32-D468 Cambridge, MA 02139 [email protected] ECE, University of Wisconsin, Madison Engineering Hall #3623, 1415 Engineering Drive Madison, WI 53706 [email protected] Senior Director, Microsoft Research 1100 Glendon Ave Suite 1080, Los Angeles CA 90024 [email protected]

Prof. Vivek K Goyal

Prof. Lizhong Zheng

Dr. John Fisher III

Prof. Stark Draper

Dr. David Heckerman

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