Ping Li (A recent tutorial)

Recent Photo

Associate Professor, Rutgers University

Department of Statistics, Department of Computer Science
pingli [att] stat.rutgers [dot] edu
475 Hill Center, 404 Hill Center, Piscataway, NJ

Workshop on Theoretical Foundation of BigData, 10/16/2014. Speakers: Dean Foster, Robert Schapire, Mikkel Thorup, and Eric Xing

BigData Seminar, Hill Center 552 (Lunch provided)

Postdoc Researcher positions available in Statistics, Learning, Vision, NLP.

Selected Publications

Ping Li, Cun-hui Zhang, and Tong Zhang
Compressed Counting Meets Compressed Sensing

Ping Li, Anshumali Shrivastava, Art Owen, Cun-hui Zhang
Densified One Permutation Hashing for Efficient Near Neighbor Search

Anshumali Shrivastava and Ping Li
Asymmetric Hashing for Efficient Near Neighbor Search

Ping Li, Chris Burges, and Qiang Wu
McRank: Learning to Rank Using Multiple Classifications and Gradient Boosting,

Ping Li, Trevor Hastie, and Kenneth W. Church
Very Sparse Random Projections,

Ping Li, Christian Konig, Anshumali Shrivastava, Josh Moore, Art Owen, Cun-Hui Zhang
Hashing Algorithms for Large-Scale Search & Learning

Ping Li
Adaptive Base Class (ABC) Boosting

Ping Li and Cun-Hui Zhang
Compressed Counting

Ping Li, Gennady Samorodnitsky, and John Hopcroft
Sign Cauchy Projections

Anshumali Shrivastava, Ping Li, and Michael Mitzenmacher
Hashing for Near Neighbor Search

Ping Li, Kenneth Church, and Trevor Hastie. (Essential idea on Figure 3)
2005 sktech paper on Conditional Random Sampling (CRS) using smallest k sketches.
2006 paper generalized CRS to real-valued data.

Martin Slawski and Ping Li
b-Bit Marginal Regression

Jian Wang and Ping Li
Recovery of Sparse Signals Using Multiple Orthogonal Least Squares

Guangcan Liu and Ping Li
Recovery of Coherent Data via Low-Rank Dictionary Pursuit

Anshumali Shrivastava and Ping Li
A New Space for Comparing Graphs




Ph.D. in Statistics, Stanford University.

Research Interests

Statistical Computing, Big Data, Data Mining, Information Retrieval


Srping 2015 Lecture Notes on Logistic Regression
Srping 2012 Categorical Data
Fall 2008, Fall 2009 Theory of Probability
Spring 2009, Spring 2010, Theory of Statistics
Spring 2008, Spring 2009, Statistical Computing
Spring 2010, Fall 2012, Computationally Intensive Statistical Methods