research interests
I’m broadly interested in statistics, machine learning, and optimization. I’m also interested in lots of applications: to finance, operations research, public policy, social good, sustainability, epidemiology, healthcare, autonomous vehicles, analytics, …. A lot of my recent work has focused on building reliable and trustworthy machine learning systems, by taking a close look at the (many) statistical and computational issues that arise after a machine learning model has been deployed into real-world systems and scientific applications.
On a more technical level, I’m interested in – and have recently worked on projects related to – the following areas:
- distribution shift, robust optimization, subpopulation-level performance, responsible AI
- conformal inference, distribution-free uncertainty quantification
- weak supervision
- tuning parameter-free stochastic optimization
- implicit regularization
- sparse regression
- large-scale multiple testing
- risk estimation, model selection
representative publications
Here are a few (representative) publications related to the above topics:
all publications
- The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and RefittingJournal of Machine Learning Research (under review), 2023
#subpopulation-level performance
#responsible AI
#conformal inference
#weak supervision
#large-scale multiple testing
- Predictive Inference with Weak SupervisionJournal of Machine Learning Research (under review), 2022
#conformal inference
#weak supervision
#responsible AI
- A Comment and Erratum on "Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE?"Journal of the American Statistical Association (under review), 2022
#risk estimation
#model selection
- Minimum-Distortion EmbeddingFoundations and Trends in Machine Learning, 2021
#representation learning
#convex optimization
- Minimizing Oracle-Structured Composite FunctionsOptimization and Engineering, 2021
#stochastic optimization
- Accelerated Gradient Flow: Risk, Stability, and Implicit Regularization2021
#implicit regularization
#convex optimization
- Computationally Efficient Posterior Inference With Langevin Monte Carlo and Early Stopping2021
#implicit regularization
#Markov chain Monte Carlo
- Robust Validation: Confident Predictions Even When Distributions ShiftJournal of the American Statistical Association, 2021
#distribution shift
#robust optimization
#conformal inference
#responsible AI
- The Implicit Regularization of Stochastic Gradient Flow for Least SquaresInternational Conference on Machine Learning (ICML), 2020
#implicit regularization
#stochastic optimization
- Confidence Bands for a Log-Concave DensityJournal of Computational and Graphical Statistics, 2020
#uncertainty quantification
#convex optimization
- A Continuous-Time View of Early Stopping for Least SquaresInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019
#implicit regularization
#convex optimization
- The Generalized Lasso Problem and UniquenessElectronic Journal of Statistics, 2019
#sparse regression
#convex optimization
- Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance EstimationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2018
#sparse inverse covariance estimation
#convex optimization
#sparse regression
- A Semismooth Newton Method for Fast, Generic Convex ProgrammingInternational Conference on Machine Learning (ICML), 2017
#convex optimization
- Generalized Pseudolikelihood Methods for Inverse Covariance EstimationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2017
#sparse inverse covariance estimation
#convex optimization
#sparse regression
- The Multiple Quantile Graphical ModelAdvances in Neural Information Processing Systems 29 (NeurIPS), 2016
#sparse inverse covariance estimation
#convex optimization
#sparse regression
- Disciplined Convex Stochastic Programming: A New Framework for Stochastic OptimizationProceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015
#stochastic optimization
- Active Learning With Model SelectionAAAI Conference on Artificial Intelligence (AAAI), 2014
#model selection
- Experiments With Kemeny Ranking: What Works When?Mathematical Social Sciences, 2012
#ranking
- Learning Lexicon Models from Search Logs for Query ExpansionJoint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 2012
#ranking
- Preferences in College Applications: A Nonparametric Bayesian Analysis of Top-10 RankingsNeural Information Processing Systems (NeurIPS) Workshop on Computational Social Science, 2010
#ranking
#Markov chain Monte Carlo