#### Invited Talks

**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.

**Advances in Distribution Compression / Kernel Thinning and Stein Thinning. (slides)**- 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.

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

**Probabilistic Inference and Learning with Stein's Method. (slides, video)**- 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.

**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)**- 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?****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.