Invited Talks

  • Stein's Method, Learning, and Inference. (slides, video)
    • American Mathematical Society von Neumann Lecture, Joint Mathematics Meetings, Jan. 2025.
    • Plenary - SIAM Conference on Mathematics of Data Science (MDS), Sep. 2022.
    • Tutorial - Stein's Method: The Golden Anniversary, IMS, Jun. 2022.
    • Beyond iid Learning Seminar, ETH Zurich, Dec. 2021.
    • Your Model Is Wrong Workshop, NeurIPS, Dec. 2021.
    • Plenary - International Conference on Monte Carlo Methods and Applications (MCM), August 2021.
    • ISI World Statistics Conference, July 2021.
    • Probability for Machine Learning Seminar, University of Oxford, Dec. 2020.
    • Econometrics and Statistics Colloquium Workshop, U. Chicago, Nov. 2020.
    • Joint Statistics and Machine Learning Seminar, Carnegie Mellon University, Nov. 2020.
    • Statistics Colloquium, Penn State, Sep. 2020.
    • Oberwolfach Uncertainty Quantification Workshop, Oberwolfach, Germany, Mar. 2019.
    • Keynote - Data, Learning, and Inference Workshop (DALI), George, South Africa, Jan. 2019.
    • Charles Stein Memorial Session, Joint Statistical Meetings, Vancouver, Canada, July 2018.

  • Advances in Distribution Compression / Kernel Thinning and Stein Thinning. (slides)
    • Statistics Seminar, Harvard University, October 2024.
    • Stochastics and Statistics Seminar, MIT, December 2023.
    • IMSI Workshop on Permutations and Causal Inference, Chicago, August 2023.
    • COPSS Leadership Academy Seminar, Joint Statistical Meetings, Toronto, August 2023.
    • Ethel Newbold Prize Seminar, World Statistics Congress, Ottawa, July 2023.
    • Semi-plenary - Journees de Statistique, French Statistical Society, Brussels, July 2023.
    • GRAMSIA: Graphical Models, Statistical Inference, and Algorithms Workshop, Harvard University, May 2023.
    • Probability and Statistics Seminar, Boston University, Mar. 2023.
    • Information Systems Laboratory Colloquium, Stanford University, Mar. 2023.
    • Statistics Seminar, UW Madison, Dec. 2022.
    • Data Science Seminar, London School of Economics, May 2022.
    • StatML CDT, Imperial College London and University of Oxford, March 2022.
    • Symposium on Advances in Approximate Bayesian Inference, February 2022.
    • Statistics Seminar, University of Michigan, December 2021.
    • Statistics and Data Science Seminar, Yale University, November 2021.
    • ML Advances and Applications Seminar, Vector Institute & Fields Institute, November 2021.
    • ML Theory Seminar, Harvard University, November 2021.
    • Computational Bayesian Statistics Journal Club, Flatiron Institute, November 2021.
    • Applied Math Colloquium, Cornell University, October 2021.
    • Bayesian Data Analysis Webinar, October 2021.
    • Statistics Seminar, Cambridge University, October 2021.
    • Statistics Colloquium, Columbia University, September 2021.

  • Knowledge Distillation as Semiparametric Inference. (slides, video)
    • Bridging the Gap Between Practice and Theory in Deep Learning Workshop, ICLR, May 2024.
    • Directions in ML Speaker Series, Microsoft Research, Apr. 2021.

  • Doing Some Good with Machine Learning. (slides, video)
    • AI for Social Impact Seminar, Harvard University, Apr. 2024.
    • AI and ML Community Seminar, Microsoft, Feb. 2024.
    • Data Science for Social Change Seminar, Cornell University, Feb. 2023.
    • Computer Science Tea, Carleton College, Feb. 2023.
    • Keynote - Data Science Week, Purdue University Fort Wayne, Nov. 2022.
    • Mathematical Sciences Colloquium, Appalachian State University, Sep. 2022.
    • Research Science Institute, Distinguished Guest Lecture, July 2022.
    • CIFAR Deep Learning+Reinforcement Learning Summer School, July 2021.
    • Data Science for Social Good, Stanford University, July 2021.
    • Statistics Seminar, Luxembourg Statistical Society, June 2021.
    • AI Seminar, University of Alberta, May 2021.
    • Neyman Seminar, U.C. Berkeley, Dec. 2020.
    • Keynote - International Conference on Machine Learning (ICML), July 2020.

  • Adaptive Bias Correction for Improved Subseasonal Forecasting. (slides)
    • Workshop on AI-Assisted Decision-Making for Conservation, Harvard CRCS, Oct. 2022.

  • Cross-validation Confidence Intervals for Test Error. (slides)
    • Inference and Interpretability in a Model-Free Setting, Joint Statistical Meetings, Aug. 2021.

  • Online Learning with Optimism and Delay. (slides, video)
    • ICML Time Series Workshop, July 2021.

  • Improving Subseasonal Forecasting in the Western U.S. with Machine Learning. (slides, video)
    • ICLR Climate Change AI Workshop, Apr. 2020.
    • Keynote - Open Data Science Conference, Apr. 2020.
    • NeurIPS Workshop on Tackling Climate Change with Machine Learning, Dec. 2019.
    • Statistics & Data Science Conference (SDSCon), MIT, Apr. 2019.
    • Computer Science Colloquium, Cornell University, Nov. 2018.

  • Orthogonal Machine Learning: Power and Limitations. (slides, video)
    • Robust and High-Dimensional Statistics Workshop, Simons Institute for the Theory of Computing, Oct. 2018.

  • Measuring Sample Quality with Kernels. (slides)
    • Bayes, Machine Learning, and Deep Learning Invited Session, International Society for Bayesian Analysis (ISBA) World Meeting, June 2018.
    • Harvard / MIT Econometrics Workshop, MIT, Mar. 2018.
    • SAMSI Workshop on Trends and Advances in Monte Carlo Sampling Algorithms, Duke University, Dec. 2017.
    • SAMSI Workshop on Quasi-Monte Carlo and High-Dimensional Sampling Methods, Duke University, Aug. 2017.
    • Borchard Colloquium on Concentration Inequalities, High Dimensional Statistics, and Stein's Method, Missilac, France, July 2017.
    • New England Machine Learning Day, Cambridge, MA, May 2017.
    • Machine Learning Seminar, MIT, Mar. 2017.

  • Measuring Sample Quality with Stein's Method. (slides)
    • Gatsby Unit Seminar, University College London, Oct. 2016.
    • Seminar, University of Liege, Sep. 2016.
    • Quetelet Seminar, Ghent University, Sep. 2016.
    • International Conference on Monte Carlo and Quasi-Monte Carlo Methods (MCQMC), Stanford, CA, Aug. 2016.
    • Statistics Seminar, Columbia University, Feb. 2016.
    • Quasi-Monte Carlo Invited Session, IMS-ISBA Joint Meeting (MCMSki V), Jan. 2016.
    • Wharton Statistics Seminar, University of Pennsylvania, Dec. 2015.
    • Neyman Seminar, UC Berkeley, Sep. 2015.
    • IMS-Microsoft Research Workshop: Foundations of Data Science, Cambridge, MA, June 2015.
    • Stochastics and Statistics Seminar, MIT, May 2015.
    • Statistics Seminar, Stanford University, May 2015.

  • Statistics for Social Good
    • AI Now Symposium on the Social and Economic Impact of Artificial Intelligence Technologies, MIT, July 2017.
    • Data Science @ Stanford Seminar, Stanford, June 2016.

  • Matrix Completion and Matrix Concentration. (slides)
    • IDSS Special Seminar, MIT, Feb. 2016.
    • Statistics Seminar, Harvard University, Nov. 2014.
    • Blackwell-Tapia Conference, Los Angeles, CA, Nov. 2014.
    • Information Systems Laboratory Colloquium, Stanford University, April 2013.
    • Statistics Seminar, Yale University, April 2013.
    • Statistics Seminar, Columbia University, April 2013.
    • Computer Science Seminar, University of Southern California, May 2012.
    • Statistics Seminar, Stanford University, Jan. 2012.

  • Divide-and-Conquer Matrix Factorization. (slides)
    • CS Department Colloquium, Princeton University, Dec. 2015.
    • Workshop on Big Data: Theoretical and Practical Challenges, Paris, France, May 2013.
    • Kaggle, San Francisco, CA, Feb. 2013.
    • 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.

  • Predicting ALS Disease Progression with Bayesian Additive Regression Trees. (slides)
    • Big Data in Biomedicine Conference, Stanford University, May 2015.
    • Guest Lecture, Stats 202, Stanford University, Nov. 2013.
    • Statistics Seminar, Stanford University, April 2013.
    • RECOMB Conference on Regulatory and Systems Genomics, San Francisco, CA, Nov. 2012.

  • Weighted Classification Cascades for Optimizing Discovery Significance. (slides)
    • NeurIPS Workshop on High-energy particle physics, machine learning, and the HiggsML data challenge (HEPML), December 2014.

  • Ranking, Aggregation, and You. (slides)
    • Statistics Seminar, University of Chicago, Oct. 2014
    • Yale MacMillan-CSAP Workshop on Quantitative Research Methods, Yale University, Sep. 2014.
    • Wharton Statistics Seminar, University of Pennsylvania, Sep. 2014.
    • Statistics Seminar, Carnegie Mellon University, Sep. 2014.
    • Western Section Meeting, American Mathematical Society, Nov. 2013.
    • Statistics Seminar, Stanford University, Sep. 2013.
    • Stanford Statistics/Machine Learning Reading Group, Stanford University, Nov. 2012.

  • Dividing, Conquering, and Mixing Matrix Factorizations. (slides)
    • Technicolor, Palo Alto, CA, June 2013.

  • Stein's Method for Matrix Concentration. (slides)
    • Institut National de Recherche en Informatique et en Automatique (INRIA), Dec. 2012.
    • Berkeley Probability Seminar, University of California, Berkeley, May 2012.

  • Build a Better Netflix, Win a Million Dollars?
    • SPARC Camp, Aug. 2014. (slides)
    • USA Science and Engineering Festival, Washington, DC, Apr. 2012. (slides)

  • The Story of the Netflix Prize: An Ensembler's Tale. (slides, video)
    • 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.
    • Comprehensive Test Ban Treaty Organization Technical Meeting on Data Mining, Vienna, Austria, Nov. 2009, presented with Ariel Kleiner.

  • 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.