About me
Hello! I am a 5th-year Electrical and Systems Engineering PhD student in the GRASP lab at the University of Pennsylvania. I am fortunate to be advised by Prof. Nikolai Matni.
Previously, I graduated with BSc degrees in Mathematics and in Statistics & Data Science from Yale University. I then spent a year as a researcher in the Kluger Lab at Yale University's Applied Mathematics Program. During the summer of 2023, I interned at Microsoft Research NYC.
I am interested in:
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Learning, (for) Dynamical Systems, (and) Control.
Email: ttz2 AT seas DOT upenn DOT edu.
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On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning, submitted. [arxiv]
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Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control, to appear at AAAI 2024. [arxiv]
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Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples, ICML 2024. [arxiv]
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Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data, ICLR 2024. (Spotlight presentation!). [arxiv]
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Performance-Robustness Tradeoffs in Adversarially Robust Control and Estimation, accepted to IEEE Transactions on Automatic Control. [arxiv]
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Multi-Task Imitation Learning for Linear Dynamical Systems, L4DC 2023. [arxiv]
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TaSIL: Taylor Series Imitation Learning, NeurIPS 2022. [arxiv]
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Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control, CDC 2022. [arxiv]
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Adversarially Robust Stability Certificates can be Sample-Efficient, L4DC 2022. [arxiv]
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Adversarial Tradeoffs in Robust State Estimation, ACC 2023. [arxiv]
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Biwhitening Reveals the Rank of a Count Matrix, SIAM Journal on Mathematics of Data Science. [arxiv]
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Sparse Optimization on General Atomic Sets: Greedy and Forward-Backward Algorithms, pre-print. [arxiv]
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On the Continuous Fermat-Weber Problem for a Convex Polygon using Euclidean Distance. [arxiv]
Artifacts from simpler times
