# Biography

I am a Research Scientist (Career) at the Lawrence Berkeley National Laboratory. I also lead the Robust Deep Learning Group at the International Computer Science Institute (ICSI), an affiliated institute of UC Berkeley. Prior to this role, I was an Assistant Professor (Tenure-Track) for Data-driven Modeling and Artificial Intelligence in the Department of Mechanical Engineering and Materials Science at the University of Pittsburgh, from September 2021 to December 2022. Before joining Pitt, I was a postdoctoral researcher in the Department of Statistics at UC Berkeley, where I worked with Michael Mahoney. I was also part of the RISELab in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley. Before that, I was a postdoc in the Department of Applied Mathematics at the University of Washington (UW), working with Nathan Kutz and Steven Brunton. I earned my PhD in Statistics at the University of St Andrews in December 2017. My MSc. in Applied Statistics is also from the University of St Andrews.

I am broadly interested in the intersection of deep learning, dynamical systems, and robustness—how can we build robust and intelligent dynamical systems that are computationally efficient and expressive? I am also interested in leveraging tools from randomized numerical linear algebra to build modern algorithms for data-intensive applications such as fluid flows and climate science. Projects in my group span the space between the development of robust and dynamical systems-inspired neural network architectures, efficient training strategies for extracting knowledge from limited data, and data-driven modeling of scientific data.

# News

- Two papers accepted in ICLR 2024 (one as spotlight)
Robustifying State-space Models for Long Sequences via Approximate Diagonalization (preprint).

Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs (preprint).

- One paper accepted in AISTATS 2024
Boosting model robustness to common corruptions (preprint).

- One paper accepted in AISTATS 2023 (as oral presentation)
Error Estimation for Random Fourier Features (preprint).

- Two papers accepted in ICLR 2022 (one as spotlight)
Noisy Feature Mixup (preprint) .

Long Expressive Memory for Sequence Modeling (preprint).

# Current Group Members

Krti Tallam, joint Postdoc

with Michael Mahoney

Kareem Hegazy, joint Postdoc

with Michael Mahoney

Pu Ren, joint Postdoc

with Michael Mahoney

Dongwei Lyu, Research Intern

# Alumni

Junyi Guo (Graduate researcher 2022-24; now PhD student at University of Notre Dame).

Jialin Song (Graduate researcher 2021-24; now PhD student at Simon Fraser University).

Yixiao Kang (Graduate researcher 2023-24; now ML Engineer at Meta).

Daniel Barron (Graduate researcher 2023-24; now Software Engineer at Amazon).

Olina Mukherjee (High School student researcher, 2021-22; now undergraduate student at CMU).

Ziang Cao (Undergraduate research intern 2020-21; now graduate student at Stanford).

Francisco Utrera (Graduate researcher 2019-22; now Senior ML Engineer at Erithmitic) .

Evan Kravitz (Graduate researcher 2019-20; now Software Engineer at Amazon) .

Vanessa Lin (Undergraduate research intern 2018-19; now at Google).

Qixuan Wu (Undergraduate research intern 2018-19; now at Goldman Sachs).