Research Interests

  • Statistical machine learning
  • Scalable algorithms and data structures

Preprints

  • Matrix Concentration Inequalities via the Method of Exchangeable Pairs. (preprint)
    Lester Mackey, Michael I. Jordan, Richard Y. Chen, Brendan Farrell, and Joel A. Tropp.

  • Combinatorial clustering and the beta negative binomial process. (preprint)
    Tamara Broderick, Lester Mackey, John Paisley, and Michael I. Jordan.

Publications

  • Divide-and-Conquer Matrix Factorization. (pdf, website, code, bib)
    Lester Mackey, Ameet Talwalkar, and Michael I. Jordan.
    Advances in Neural Information Processing Systems (NIPS). December 2011.

  • Visually Relating Gene Expression and in vivo DNA Binding Data. (pdf, bib)
    Min-Yu Huang, Lester Mackey, Soile Keranen, Gunther Weber, Michael Jordan, David Knowles, Mark Biggin, and Bernd Hamann.
    IEEE International Conference on Bioinformatics and Biomedicine (BIBM). November 2011.

  • Mixed Membership Matrix Factorization. (pdf, supp info, slides, code, bib)
    Lester Mackey, David Weiss, and Michael I. Jordan.
    International Conference on Machine Learning (ICML). June 2010.

  • On the Consistency of Ranking Algorithms. (pdf, slides, bib)
    John Duchi, Lester Mackey, and Michael I. Jordan.
    International Conference on Machine Learning (ICML). June 2010.
    • Winner of the ICML 2010 Best Student Paper Award.

  • Deflation Methods for Sparse PCA. (pdf, poster, code, bib)
    Lester Mackey.
    Advances in Neural Information Processing Systems (NIPS). December 2008.

  • Fault-tolerant Typed Assembly Language. (pdf, bib)
    Frances Perry, Lester Mackey, George A. Reis, Jay Ligatti, David I. August, and David Walker.
    ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI). June 2007.
  • Static Typing for a Faulty Lambda Calculus. (pdf, bib)
    David Walker, Lester Mackey, Jay Ligatti, George Reis, and David August.
    ACM SIGPLAN International Conference on Functional Programming (ICFP). September 2006.

  • Participatory Design with Proxies: Developing a Desktop-PDA System to Support People with Aphasia. (pdf, bib)
    Jordan Boyd-Graber, Sonya Nikolova, Karyn Moffatt, Kenrick Kin, Joshua Lee, Lester Mackey, Marilyn Tremaine, and Maria Klawe.
    SIGCHI Conference on Human Factors in Computing Systems (CHI). April 2006.

Invited Talks

  • Matrix Completion and Matrix Concentration. (slides)
    Statistics Seminar, Stanford University, Jan. 2012.

  • Divide-and-Conquer Matrix Factorization. (slides)
    • Statistical Science Seminar Series, Duke University, Jan. 2012.
    • CMS Seminar, Caltech, Jan. 2012.
    • San Francisco Bay Area Machine Learning Meetup, San Francisco, CA, Nov. 2011.

  • The Story of the Netflix Prize: An Ensembler's Tale. (slides)
    National Academies' Seminar, Washington, DC, Nov. 2011.

  • Mixed Membership Matrix Factorization. (slides)
    Joint Statistical Meetings, Miami Beach, FL, July 2011.

  • False Event Identification and Beyond: A Machine Learning Approach.
    Presented with Ariel Kleiner.
    Comprehensive Test Ban Treaty Organization Technical Meeting on Data Mining, Vienna, Austria, Nov. 2009.

  • The Dinosaur Planet Approach to the Netflix Prize.
    • LIDS Seminar Series, MIT, Nov. 2008, presented with David Weiss.
    • Guest Lecture, Stat 157, U.C. Berkeley, Sept. 2008.
    • Process Driven Trading Group, Morgan Stanley, April 2008, presented with David Lin and David Weiss.

Miscellany

  • Sparse Representation and Low-Rank Approximation. (website)
    Organized with Francis Bach, Michael Davies, Rémi Gribonval, Michael Mahoney, Mehryar Mohri, Guillaume Obozinski, and Ameet Talwalkar.
    Neural Information Processing Systems (NIPS) 2011 workshop. December 2011.

  • Anomaly Detection for Asynchronous and Incomplete Data.
    Joint work with John Duchi and Fabian Wauthier.
    Advanced Topics in Computer Systems (CS 262A, E. Brewer). December 2008.

  • Scalable Dyadic Kernel Machines. (pdf)
    Advanced Topics in Learning and Decision Making (CS 281B, P. Bartlett). May 2008.

  • Latent Dirichlet Markov Random Fields for Semi-supervised Image Segmentation and Object Recognition. (pdf)
    Statistical Learning Theory (CS 281A, M. Jordan) and Computer Vision (CS 280, J. Malik). December 2007.