About Me

Thank you for visiting this website. I am a machine learning researcher at Morgan Stanley and research affiliate at Duke University. My research focuses on bridging the gap between the computational aspects of deep learning and the theoretical properties of topics in applied mathematics such as stochastic processes, differential equations, and extreme value theory. I am interested in applying these methods to describe different phenomena in biomedical, environmental, and financial applications. If you are interested in discussing some of these topics in more detail, I would love to continue the conversation over email.

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Publications

2026

How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off
Waïss Azizian, Ali Hasan
International Conference on Machine Learning (ICML), 2026
[pdf] [OpenReview]

Learning Max-Stable Representations that Extrapolate
Ali Hasan, Patrick Kuiper, Yuting Ng, Jose Blanchet, Vahid Tarokh
Uncertainty in Artificial Intelligence (UAI), 2026
[pdf] [pmlr]

2025

Elliptic Loss Regularization
Ali Hasan*, Haoming Yang*, Yuting Ng, Vahid Tarokh
International Conference on Learning Representations (ICLR), 2025
[pdf] [OpenReview]

Off-policy Predictive Control with Causal Sensitivity Analysis
Myrl G. Marmarelis, Ali Hasan, Kamyar Azizzadenesheli, R. Michael Alvarez, Anima Anandkumar
Conference on Uncertainty in Artificial Intelligence (UAI), 2025
[pdf] [pmlr]

Conditional Average Treatment Effect Estimation Under Hidden Confounders
Ahmed Aloui, Juncheng Dong, Ali Hasan, Vahid Tarokh
Conference on Uncertainty in Artificial Intelligence (UAI), 2025
[pdf] [pmlr]

Parabolic Continual Learning
Haoming Yang, Ali Hasan, Vahid Tarokh
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
[pdf] [pmlr]

2024

Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions
Patrick Kuiper*, Ali Hasan*, Wenhao Yang, Yuting Ng, Hoda Bidkhori, Jose Blanchet, Vahid Tarokh
Conference on Uncertainty in Artificial Intelligence (UAI), 2024
[pdf] [OpenReview]

Base Models for Parabolic Partial Differential Equations
Xingzi Xu*, Ali Hasan*, Jie Ding, Vahid Tarokh
Conference on Uncertainty in Artificial Intelligence (UAI), 2024
[pdf] [OpenReview]

Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Models
Haoming Yang*, Ali Hasan*, Yuting Ng, Vahid Tarokh
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
[pdf] [pmlr]

2023

Representation Learning for Extremes
Ali Hasan, Yuting Ng, Jose Blanchet, Vahid Tarokh
Conference on Neural Information Processing Systems: Workshop on Heavy Tails in ML, 2023
[pdf]

Inference and Sampling of Point Processes from Diffusion Excursions
Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdel‑Ghani, Anderson Schneider, Vahid Tarokh
Conference on Uncertainty in Artificial Intelligence (UAI) (Spotlight), 2023
[pdf] [poster]

Characteristic Neural Ordinary Differential Equations
Xingzi Xu*, Ali Hasan*, Khalil Elkhalil, Jie Ding, Vahid Tarokh
International Conference on Learning Representations (ICLR), 2023
[pdf] [presentation]

2022

Modeling Extremes with 𝑑‑max‑decreasing Neural Networks
Ali Hasan, Khalil Elkhalil, Yuting Ng, João M Pereira, Sina Farsiu, Jose Blanchet, Vahid Tarokh
Conference on Uncertainty in Artificial Intelligence (UAI) (Oral), 2022
[pdf]

Neural Extreme Value Copulas
Ali Hasan, Khalil Elkhalil, Yuting Ng, João M Pereira, Sina Farsiu, Jose Blanchet, Vahid Tarokh
SIAM Conference on Uncertainty Quantification, 2022

Inference and Sampling for Archimax Copulas
Yuting Ng*, Ali Hasan*, Vahid Tarokh
Advances in Neural Information Processing Systems (NeurIPS), 2022
[pdf]

2021

Fisher Auto‑Encoders
Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
[pdf]

Identifying Latent Stochastic Differential Equations
Ali Hasan*, João M Pereira*, Sina Farsiu, Vahid Tarokh
IEEE Transactions on Signal Processing, 2021
[link]

Generative Archimedean Copulas
Yuting Ng, Ali Hasan, Khalil Elkhalil, Vahid Tarokh
Conference on Uncertainty in Artificial Intelligence (UAI) (Oral), 2021
[pdf]

2020

Meta‑Learning Approach to Automatically Register Multivendor Retinal Images
Ali Hasan, Zengtian Deng, Jessica Loo, Dibyendu Mukherjee, Jacque L Duncan, David G Birch, Glenn J Jaffe, Sina Farsiu
Investigative Ophthalmology & Visual Science, 2020
[link]

Learning Partial Differential Equations from Data using Neural Networks
Ali Hasan, João M Pereira, Robert Ravier, Sina Farsiu, Vahid Tarokh
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
[link]

Transport and Concentration of Wealth: Modeling an Amenities‑Based‑Theory
Ali Hasan, Nancy Rodrı́guez, L Wong
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2020
[link]

2017

Image‑based Immersed Boundary Model of the Aortic Root
Ali Hasan, Ebrahim M Kolahdouz, Andinet Enquobahrie, Thomas G Caranasos, John P Vavalle, Boyce E Griffith
Medical Engineering & Physics, 2017
[link]

* equal contribution