* I will be an Area Chair for ALT 2025.

* I will be an Area Chair for NeurIPS 2024.

* I will be an Area Chair for the ICML 2024 workshop Theoretical Foundations of Foundation Models.

* I will be a long-term participant at the Simons Institute's program on Modern Paradigms in Generalization in Fall 2024.

* Paper on interpolation under distribution shift was accepted at ICML 2024.

* New preprint on minimum-norm interpolation under distribution shift with Neil Mallinar, Austin Zane, and Bin Yu.

* Paper on transformers and in-context learning accepted for publication at JMLR.

* I will give a talk at the UC Davis Mathematics of Data and Decisions Seminar on May 21.

* I will give a talk at the UCLA Department of Statistics & Data Science Seminar on May 16.

* I will give a talk at the Sorbonne Université-Paris Diderot University Statistics seminar on April 30.

* I will give a talk at INRIA SIERRA/École normale supérieure on April 29.

* Paper on benign overfitting and grokking in ReLU networks was accepted at ICLR 2024.

* I will present the paper Random Feature Amplification at ICLR 2024 as a part of the journal-to-conference track.

## Older news (click to expand)

__2023__* Two papers accepted at NeurIPS 2023 workshops: one at Robustness of Foundation Models (R0-FoMo), another at Mathematics of Modern Machine Learning (M3L).

* I will give a talk at Apple Machine Learning Research, Cupertino on November 29.

* New preprint on benign overfitting and grokking with Zhiwei Xu, Yutong Wang, Gal Vardi, and Wei Hu.

* The Double-Edged Sword of Implicit Bias was accepted at NeurIPS 2023.

* Random Feature Amplification was accepted for publication at JMLR.

* I will present the tutorial on "Reconsidering Overfitting in the Age of Overparameterized Models" with Vidya Muthukumar and Fanny Yang at NeurIPS 2023.

* I will give a talk at the University of Basel Department of Mathematics and Computer Science seminar on November 9.

* I will give a talk at the University of Cambridge Machine Learning Group on October 25.

* I will give a talk at Google DeepMind London on October 18.

* I will give a talk at the Imperial College London AI+X Seminar on October 17.

* I will give a talk at the University of Oxford Computational Statistics and Machine Learning Seminar on October 13.

* I will give a talk at Stanford University (Tengyu Ma's group) on September 11.

* I will give a talk at the Google Research in-context learning reading group on August 11th.

* New preprint with Nikhil Ghosh, Wooseok Ha, and Bin Yu on learning a single-neuron autoencoder with SGD.

* I will be a member of the senior program committee for ALT 2024.

* New preprint with Ruiqi Zhang and Peter Bartlett on in-context learning of linear models with transformers.

* Paper on benign overfitting in neural networks was accepted at COLT 2023.

* I will be an Area Chair for NeurIPS 2023 in New Orleans.

* I will be speaking at the Youth in High Dimensions workshop in Trieste, Italy, from May 29-June 2. Registration is available here.

* I will be joining UC Davis as an Assistant Professor of Statistics in the fall.

* Two new preprints with Gal Vardi, Peter Bartlett, and Nati Srebro: one on benign overfitting, another on adversarial robustness.

* I am on the program committee for COLT 2023.

* Paper on implicit bias in neural nets trained on high-dimensional data was accepted for publication at ICLR 2023 as a spotlight presentation.

__2022__* I will be speaking at the Symposium on Frontiers of Machine Learning and Artificial Intelligence at the University of Southern California on November 10th.

* New preprint with Gal Vardi, Peter Bartlett, Nati Srebro, and Wei Hu on implicit bias in neural networks trained on high-dimensional data.

* I have been selected as a 2022 Rising Star in Machine Learning by the University of Maryland.

* I am giving a talk at the University of Alberta Statistics Seminar on October 26th.

* I am giving a talk at the EPFL Fundamentals of Learning and Artificial Intelligence Seminar on September 30th.

* I am a visiting scientist at EPFL in September and October, hosted by Emmanuel Abbe.

* I am giving a talk at the Joint Statistical Meetings about benign overfitting without linearity.

* Benign overfitting without linearity was accepted at COLT 2022.

* I am an organizer for the Deep Learning Theory Summer School and Workshop, to be held this summer at the Simons Institute.

* I will be speaking at the ETH Zurich Data, Algorithms, Combinatorics, and Optimization Seminar on June 7th.

* I will be a keynote speaker at the University of Toronto Statistics Research Day on May 25th.

* I am giving a talk at Harvard University's Probabilitas Seminar on May 6th.

* Two recent works accepted at the Theory of Overparameterized Machine Learning 2022 workshop, including one as a contributed talk.

* I am giving a talk at the Microsoft Research ML Foundations Seminar on April 28th.

* I am giving a talk at the University of British Columbia (Christos Thrampoulidis's group) on April 8th.

* I am giving a talk at Columbia University (Daniel Hsu's group) on April 4th.

* I am giving a talk at Oxford University (Yee Whye Teh's group) on March 23rd.

* I am giving a talk at the NSF/Simons Mathematics of Deep Learning seminar on March 10th.

* I am giving a talk at the Google Algorithms Seminar on March 8th.

* I'm reviewing for the Theory of Overparameterized Machine Learning 2022 workshop.

* Two new preprints with Niladri Chatterji and Peter Bartlett: Benign Overfitting without Linearity and Random Feature Amplification.

* Recent work on sample complexity of a self-training algorithm accepted at AISTATS 2022.

__2021__* I am speaking at the Deep Learning Theory Symposium at the Simons Institute on December 6th.

* My paper on proxy convexity as a framework for neural network optimization was accepted at NeurIPS 2021.

* Two new preprints on arxiv: (1) Proxy convexity: a unified framework for the analysis of neural networks trained by gradient descent, and (2) Self training converts weak learners to strong learners in mixture models.

* I am reviewing for the ICML 2021 workshop Overparameterization: Pitfalls and Opportunities (ICMLOPPO2021).

* Three recent papers accepted at ICML, including one as a long talk.

* New preprint on provable robustness of adversarial training for learning halfspaces with noise.

* I will be presenting recent work at TOPML2021 as a lightning talk, and at the SoCal ML Symposium as a spotlight talk.

* I'm giving a talk at the ETH Zurich Young Data Science Researcher Seminar on April 16th.

* I'm giving a talk at the Johns Hopkins University Machine Learning Seminar on April 2nd.

* I'm reviewing for the Theory of Overparameterized Machine Learning Workshop.

* I'm giving a talk at the Max-Planck-Insitute (MPI) MiS Machine Learning Seminar on March 11th.

* New preprint showing SGD-trained neural networks of any width generalize in the presence of adversarial label noise.

__2020__* New preprint on agnostic learning of halfspaces using gradient descent is now on arXiv.

* My single neuron paper was accepted at NeurIPS 2020.

* I will be attending the IDEAL Special Quarter on the Theory of Deep Learning hosted by TTIC/Northwestern for the fall quarter.

* I've been awarded a Dissertation Year Fellowship by UCLA's Graduate Division.

* New preprint on agnostic PAC learning of a single neuron using gradient descent is now on arXiv.

* New paper accepted at

*Brain Structure and Function*from work with researchers at UCLA School of Medicine.

* I'll be (remotely) working at Amazon's Alexa AI group for the summer as a research intern, working on natural language understanding.

*2019** My paper with Yuan Cao and Quanquan Gu, "Algorithm-dependent Generalization Bounds for Overparameterized Deep Residual Networks", was accepted at NeurIPS 2019 (arXiv version, NeurIPS version).