Vincent Yan Fu Tan
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Graduate Student, EECS Dept., MIT. Phone:
(617) 253-3816
Email:
vtan at mit dot edu
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After our honeymoon in
Brief Biography
Vincent Tan is a second-year graduate student at Laboratory
for Information and Decision Systems (LIDS)
in the Department of EECS at MIT.
His research interests are in the broad areas of statistical signal processing,
graphical models and convex optimization. He is affiliated to the Stochastic
Systems Group (SSG) led by Prof. Alan Willsky.
Vincent was an
undergraduate in Electrical and Information Sciences (EIST) at Sidney Sussex College in Cambridge University. He worked on his Masters
project at the Signal Processing
Laboratory in the Engineering Department (CUED)
under Dr. Cédric
Févotte and received the Charles Lamb Prize for being the top
student in EIST in 2005. He spent his junior year at the Massachusetts
Institute of Technology (MIT) on the
Cambridge-MIT (CMI) Undergraduate
Exchange Program.
·
In
the summer of 2008, Vincent was an intern at the Machine Learning and
Perception (MLP) group at
Microsoft Research Cambridge (MSR-C).
·
From
2006 to 2007, Vincent worked at the Institute for Infocomm Research (I2R), a research institute under A*STAR.
·
From
2005 to 2006, Vincent worked as a research engineer at the Defence Science
Organisation (DSO) National Laboratories
in
·
In
2004, he spent a summer at Caltech under
the Summer Undergraduate Research Fellowship (SURF).
Vincent’s research is
funded by the Agency of Science, Technology and Research (A*STAR),
Vincent is a Student Member
of the IEEE. Vincent is married to a
wonderful woman, Huili, who is a graduate student in biology at the Whitehead
Institute.
Research
My research lies in the broad areas of statistical signal
processing, convex optimization and machine learning.
Current Research
I have been working on the use of
convex optimization and information theory to learn probabilistic graphical
models for the specific purpose of hypothesis testing/classification (SSP
2007). This work has been extended to sequentially and jointly learn increasingly complex probability models defined on
graphical models for discriminating between two hypotheses (ICASSP 2008, ITA
2008). I am also interested in frame representations, sampling theory and
signals with finite rate of innovation (TSP 2008).
Previous Research
Previously, I was involved in
developing new algorithms for privacy-preserving data mining. I examined the
use of various and devised novel algorithms for the reconstruction of a
distribution after a generic randomization process (MLDM 2007). I examined the
accuracy and utility of using Kernel Density Resampling methods for privacy
preservation (PinKDD 2007) in the context of distributed classification.
During my
final year at
Publications
- 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, 2008, Accepted!
- Vincent Y. F. Tan, John W. Fisher
III, Alan S. Willsky, “Learning Max-Weight Discriminative Forests,”
2008 IEEE International Conference
on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas,
Nevada, Mar 30 – April 4, 2008, Link
- John
W. Fisher III, Vincent Y. F. Tan,
Alan S. Willsky, “Learning Max-Weight Discriminative Forests,”
2008 Information Theory and Applications Workshop (ITA),
La Jolla, California, Jan 27 – Feb 1, 2008. (JWF Invited)
- Sujay Sanghavi, Vincent Y. F. Tan and Alan S.
Willsky, “Learning Graphical Models for Hypothesis Testing”,
In IEEE Statistical Signal Processing
(SSP) Workshop (2007), Madison, WI, Aug 26 - 29, 2007. Link
- Vincent Y. F. Tan and See Kiong Ng, “Privacy-Preserving Sharing of
Horizontally-Distributed Private Data for Constructing Accurate
Classifiers”, Proceedings
of the First SIGKDD International Workshop on Privacy, Security, and Trust
in KDD (PinKDD'07), Lecture Notes in Computer Science (LNCS), Volume
4890, Pages 116-137, Springer, 2008. SpringerLink
- Vincent Y. F. Tan and See Kiong Ng, “Privacy-Preserving Sharing of
Horizontally-Distributed Private Data for Constructing Accurate
Classifiers”, accepted by the
First ACM SIGKDD International Workshop on Privacy, Security, and Trust in
KDD (PinKDD 2007 held in conjunction with SIGKDD), San Jose,
California, August 12-15, 2007. pdf Link
- Vincent Y. F. Tan and See Kiong Ng, “Generic Probability Density Function
Reconstruction for Randomization in Privacy-Preserving Data Mining”,
In: P. Perner (Ed.): Proceedings of the 5th International Conference on
Machine Learning and Data Mining (MLDM-07), (LNAI 4571), pp. 76-90,
- Vincent Y. F. Tan and Cédric Févotte, “A Study of the Effect of
Source Sparsity for Various Transforms on Blind Audio Source Separation
Performance”. In Proceedings Workshop on Signal
Processing with Adaptive Sparse Structured Representations
(SPARS’05),
Reports and Thesis
- Vincent Y. F. Tan, “Blind Audio
Source Separation”. M.Eng. Final Report, Signal Processing Laboratory,
Cambridge University Engineering Department, Jun. 2005. pdf
- Vincent Y. F. Tan, “An
Algorithm for Finding Equivalent Sources For a Wave Scattering
Problem”. Summer Undergraduate Research Fellowship (SURF) Final
Report, Applied and Computational Mathematics, Caltech, Aug. 2004.
Term Projects
- Vincent Y. F. Tan, “Information
Geometry Analysis of Learning Mixtures of Trees”. MIT 6.441 Information Theory, EECS,
MIT, May 2008. pdf
- Vincent Y. F. Tan, “Learning
Graphical Models using Information Criteria and Maximum Entropy
Relaxation”. MIT 6.867 Machine
Learning, EECS, MIT, Dec 2007.
pdf
- Vincent Y. F. Tan, “Estimating the
Parameters of a Signal with Finite Rate of Innovation from Noisy Samples:
Deterministic and Stochastic Algorithms”. MIT 6.342 Wavelets, Approximation and Compression,
EECS, MIT, May 2007. pdf
- Vincent Y. F. Tan, “Stochastic
Optimization of Keane's Bump Function”. CUED 5R1 Stochastic
Optimization Coursework, EIST, Jun. 2005. pdf
- Vincent Y. F. Tan, “Stochastic
Processes: The Gibbs Sampler and the Straight Line”. CUED 5R1
Stochastic Processes Coursework, EIST, Jun. 2005. pdf
- Vincent Y. F. Tan,
“Newsvendors Tackle the Newsvendors Problem”. CUED 4E9:
Quantitative Techniques in Operations Management, EIST, Jun. 2005. pdf
Presentations
- “Information Geometry and Mixtures
of Trees”. 6.441 Information Theory, May 2008. pdf
- “Boosted Graphical Model
Classifiers”. RQE Talk,
May 2008.
- “Learning Max-Weight Discriminative
Forests”. Presented at ICASSP,
Apr 2008. pdf
- “Learning Max-Weight Discriminative
Forests”. Presented at LIDS
Student Conference, Jan 2008. pdf
- “Learning Graphical Models and
Max-Weight Discriminative Forests for Hypothesis Testing” Presented at
- “Learning Graphical Models for
Hypothesis Testing”. Presented
at SSG Seminar, Oct 2007. pdf
- “Learning Graphical Models for
Hypothesis Testing”. Poster
presented at SSP 2007, Madison, Wisconsin, Aug 2007. pdf
- “Privacy-Preserving
Sharing of Horizontally-Distributed Private Data for Constructing Accurate
Classifiers”. Presented at PinKDD
2007,
- “Generic Probability Density
Function Reconstruction for Randomization in Privacy-Preserving Data
Mining “. Presented at MLDM
2007,
- “Estimating the Parameters of a
Signal with Finite Rate of Innovation from Noisy Samples: Deterministic
and Stochastic Algorithms”. 6.342 Wavelets, Approximation and
Compression, May 2007. pdf
- “Blind Audio Source
Separation”. M.Eng. Project Presentation, Jun. 2005. pdf
- “The Newsvendor Problem”. CUED
4M9: Quantitative Methods in Operations Management Final Presentation May.
2005. pdf
- “An Algorithm For Finding Equivalent
Sources For A Wave Scattering Problem”. Summer Undergraduate
Research Fellowship (SURF) Final Presentation, Caltech, Aug. 2004. pdf
Classes
Relevant MIT Classes:
- 18.950: Differential Geometry (Fall 2008)
- 6.441: Information Theory (Spring 2008)
- 18.100B: Analysis I (Spring 2008)
- 9.520: Statistical Learning Theory (Spring
2008: Listener)
- 6.867: Machine Learning (Fall 2007)
- 6.241: Dynamic Systems and Control (Fall
2007)
- 6.253: Convex Analysis (Fall 2007: Listener)
- 6.342: Wavelets, Approximation and
Compression (Spring 2007)
- 6.252: Nonlinear Programming (Spring 2007)
- 6.437: Inference and Information (Spring
2007: Listener)
- 6.341: Discrete-Time Signal Processing
(Spring 2004)
- 6.432: Stochastic Processes, Estimation
and Detection (Spring 2004)
- 6.301: Solid-State Circuits (Spring 2004)
- 6.302: Feedback Systems (Fall 2003)
- 6.011: Introduction to Signal Processing,
Communications and Control (Fall 2003)
- 6.431: Applied Probability (Fall 2003)
Relevant Cambridge Classes (All
2004/2005):
- 4F3: Nonlinear
and Predictive Control
- 4F8: Image
Processing and Image Coding
- 4F9: Medical
Imaging and 3-D Computer Graphics
- 4F10: Statistical
Pattern Processing
- 4F12: Computer
Vision and Robotics
- 4M13: Complex Analysis and Optimisation
- 5R1: Stochastic
processes and Stochastic optimisation methods
Teaching
I will be a
Teaching Assistant for Dynamic Systems and Control (6.241) in the Fall of 2008.
I gave a
short review course on 6.097 Signals and Systems in IAP 2008.
I conducted
recitations for Analytical Methods in ECE (EE2012) at the
National University of Singapore (NUS) in
the Fall of 2006.
Resume
I am no longer looking for
internships but here is my resume. pdf
Contact Information
- E-mail: vtan {at} mit {dot} edu,
- Term Address: Room 32-D570,
- Telephone: (617)-913-4213
Updated: June 2008