I’m a Machine Learning Researcher passionate about unifying data-driven and physics-informed learning.
My work focuses on representation learning, Bayesian inference, kernel-based generalization, and large language models (LLMs) for structured and unstructured data.

🔹 Eigenspectrum analysis for feature-space generalization

🔹 Differentiable symmetry-aware ML packages

🔹 Large Language Models for scientific embeddings

🔹 Fourier Neural Operators (FNOs) for non-smooth data assimilation

Languages: Python, R, C/C++ , MATLAB

Core Expertise: Large Language Models (LLMs), Representation Learning, Bayesian Inference, Optimization, Federated Learning

An end-to-end differentiable package for permutationally invariant polynomials in Python and Rust.
🧾 Published in J. Chem. Phys. (2025) — Editor’s Pick

When richer features don’t always generalize better.
📘 Submitted to ICLR 2026

Meta-learning for Hessian inversion and enhanced variational data assimilation.
📙 NeurIPS 2025 Workshop

M.Sc. in Computational Science & Engineering — McMaster University (2023–2025)
Ph.D. in Physical Chemistry — University of Tehran (2014–2019)
M.Sc. in Physical Chemistry — University of Tehran (2010–2013)