Manuscripts

. High-Dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models. arXiv:2204.04476, 2022.

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. Reversible Gromov-Monge Sampler for Simulation-Based Inference. arXiv:2109.14090, 2021.

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. Interpolating Classifiers Make Few Mistakes. arXiv:2101.11815, 2021.

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. Deep Learning for Individual Heterogeneity: An Automatic Inference Framework. arXiv:2010.14694, 2020.

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Publications

. Online Learning to Transport via the Minimal Selection Principle. Conference on Learning Theory (COLT), 2022.

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. Universal Prediction Band via Semi-Definite Programming. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022.

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. How Well Generative Adversarial Networks Learn Distributions. Journal of Machine Learning Research, 2021.

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. Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks. Journal of the American Statistical Association (Theory and Methods), 2021.

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. Deep Neural Networks for Estimation and Inference. Econometrica, 2021.

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. Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits. Journal of the American Statistical Association (Theory and Methods), 2021.

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. On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. Conference on Learning Theory (COLT), 2020.

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. Just Interpolate: Kernel ''Ridgeless'' Regression Can Generalize. Annals of Statistics, 2020.

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. Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information. Journal of Machine Learning Research, 2020.

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. Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2019.

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. Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

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. Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

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. Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. Conference on Learning Theory (COLT), 2018.

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. Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. International Conference on Machine Learning (ICML), 2017.

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. Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix. Annals of Statistics, 2017.

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. On Detection and Structural Reconstruction of Small-World Random Networks. IEEE Transactions on Network Science and Engineering, 2017.

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. Geometric Inference for General High-Dimensional Linear Inverse Problems. Annals of Statistics, 2016.

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. Learning with Square Loss: Localization through Offset Rademacher Complexity. Conference on Learning Theory (COLT), 2015.

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. Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. Conference on Learning Theory (COLT), 2015.

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Teaching

Booth:

Wharton:

  • 622 (MBA): Advanced Quantitative Modeling: Spring 15, Spring 14

Awards

  • NSF CAREER Award, 2021-2026

  • David G. Booth Faculty Fellow, William S. Fishman Faculty Scholar, 2021-2022

  • George C. Tiao Faculty Fellowship, 2017-2021
    for research in computational and data science

  • J. Parker Memorial Bursk Award, 2016
    for excellence in research

  • US Junior Oberwolfach Fellow, 2015

  • Winkelman Fellowship, 2014-2017
    the highest honorific fellowship awarded by the Wharton School

Professional Service

Workshops & Talks

More Talks

UPenn
Nov 30, 2022
Princeton
Nov 28, 2022
UW Madison
Nov 16, 2022
UCL
May 24, 2022
UBC
Oct 15, 2021

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