# Students

I have been fortunate to advise and co-advise over 10 PhD student interns:

- Chaoran Cheng 2024
- PhD Candidate in Computer Science, University of Illinois Urbana-Champaign

- Utkarsh . 2024
- PhD Candidate in Computational Science and Engineering, MIT

- Luca Masserano 2024
- PhD Candidate in Statistics and Machine Learning, Carnegie Mellon University

- Matthias Karlbauer 2023-2024
- Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics,
*AI4DifferentialEquations In Science ICLR Workshop*, 2024. - PhD Candidate in Computer Science, University of Tübingen

- Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics,
- S Chandra Mouli 2023
- Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs,
*ICML*, 2024. - PhD Candidate in Computer Science, Purdue University

- Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs,
- Shikai Qiu 2023
- Transferring Knowledge from Large Foundation Models to Small Downstream Models,
*ICML*, 2024. - PhD Candidate in Computer Science, NYU Courant

- Transferring Knowledge from Large Foundation Models to Small Downstream Models,
- Derek Hansen 2022-2023
- Learning Physical Models that Can Respect Conservation Laws,
*ICML*, 2023. - PhD Candidate in Statistics and Scientific Computing, University of Michigan
- Collaborated with Professor Michael W. Mahoney, University of California, Berkeley

- Learning Physical Models that Can Respect Conservation Laws,
- Nadim Saad 2021-2022
- Guiding continuous operator learning through Physics-based boundary constraints,
*ICLR*, 2023. - Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow,
*Machine Learning and Physical Sciences NeurIPS Workshop*, 2021. - PhD Candidate in Computational and Mathematical Engineering, Stanford University

- Guiding continuous operator learning through Physics-based boundary constraints,
- Xiyuan Zhang 2022-2023
- First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting,
*All Things Attention: Bridging Different Perspectives on Attention NeurIPS Workshop*, 2022. - PhD Candidate in Computer Science and Engineering (CSE), University of California, San Diego

- First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting,
- Jiayao Zhang 2022-2023
- Towards reverse causal inference on panel data: Precise formulation and challenges,
*A Causal View on Dynamical Systems NeurIPS Workshop*, 2022. - PhD Candidate in Computer Science, University of Pennsylvania

- Towards reverse causal inference on panel data: Precise formulation and challenges,
- Mike Van Ness 2022
- Cross-Frequency Time Series Meta-Forecasting,
*Technical Report, Preprint: arXiv:2302.02077*, 2023. - PhD Candidate in Management Science and Engineering (MSE), Stanford University

- Cross-Frequency Time Series Meta-Forecasting,
- Ke Alexander Wang 2021
- GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics,
*Spotlight at the ICBINB NeurIPS Workshop, PMLR*, 2022. - PhD Candidate in Computer Science, Stanford University

- GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics,
- Kai Fung (Kelvin) Kan 2021
- Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting,
*AISTATS*, 2022. - PhD Candidate in Mathematics, Emory University

- Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting,
- Xiaoyong Jin, 2020-2022
- Domain Adaptation for Time Series Forecasting via Attention Sharing,
*ICML*, 2022. - PhD in Computer Science, University of California, Santa Barbara

- Domain Adaptation for Time Series Forecasting via Attention Sharing,
- Rui (Ray) Wang 2020
- Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems, L4DC, 2021.
- Contributed Talk, Best Paper Award at the NeurIPS, Machine Learning in Public Health NeurIPS Workshop, 2020.
- PhD Candidate in Computer Science and Engineering (CSE), University of California, San Diego
- Collaborated with Professors Rose Yu, University of California, San Diego and Christos Faloutsos, Carnegie Mellon University