Kareem Hegazy

Kareem Hegazy

Postdoctoral Researcher

Posts by Collection

portfolio

publications

Imaging CF3I Conical Intersection and Photodissociation Dynamics with Ultrafast Electron Diffraction

Published in Science, 361, 64 (2018), 2018

MeV ultrafast electron diffraction images the photodissociation of CF3I through a conical intersection — the first real-space mapping of a coherent nuclear wave packet bifurcating onto two potential energy surfaces as it passes through the intersection.

Recommended citation: J. Yang, X. Zhu, T. J. A. Wolf, et al. (2018). "Imaging CF3I Conical Intersection and Photodissociation Dynamics with Ultrafast Electron Diffraction." Science, 361(6397), 64-67. https://www.science.org/doi/10.1126/science.aat0049

The Photochemical Ring-Opening of 1,3-Cyclohexadiene Imaged by Ultrafast Electron Diffraction

Published in Nature Chemistry, 11, 504 (2019), 2019

Femtosecond, sub-ångström imaging of 1,3-cyclohexadiene’s photochemical ring-opening with MeV ultrafast electron diffraction — a textbook electrocyclic reaction and model for photobiological reactions such as vitamin D synthesis.

Recommended citation: T. J. A. Wolf, D. M. Sanchez, J. Yang, et al. (2019). "The Photochemical Ring-Opening of 1,3-Cyclohexadiene Imaged by Ultrafast Electron Diffraction." Nature Chemistry, 11(6), 504-509. https://doi.org/10.1038/s41557-019-0252-7

Conformer-Specific Photochemistry Imaged in Real Space and Time

Published in Science, 374, 178 (2021), 2021

Directly images conformer-specific electrocyclic ring-opening of α-phellandrene with MeV ultrafast electron diffraction, confirming Woodward–Hoffmann rules in real space and time — a new tool for resolving conformer-dependent reaction dynamics.

Recommended citation: E. G. Champenois, D. M. Sanchez, J. Yang, et al. (2021). "Conformer-Specific Photochemistry Imaged in Real Space and Time." Science, 374(6564), 178-182. https://www.science.org/doi/abs/10.1126/science.abk3132

Applying Bayesian Inference and Deterministic Anisotropy to Retrieve the Molecular Structure from Gas-Phase Diffraction Experiments

Published in Communications Physics, 6, 325 (2023), 2023

Novel Bayesian statistical framework that transforms gas-phase diffraction into 3D molecular images — 100× resolution improvement, no simulation bottleneck.

Recommended citation: K. Hegazy, V. Makhija, et al. (2023). "Applying Bayesian Inference and Deterministic Anisotropy to Retrieve the Molecular Structure from Gas-Phase Diffraction Experiments." Communications Physics (Nature), 6, 325. https://www.nature.com/articles/s42005-023-01448-x

Neural Network Path Optimization for Finding Transition States on a Machine Learned Potential

Published in Submitted to Nature Computational Science, 2025

A double-ended neural-network framework for chemical transition state search on ML interatomic potentials — outperforms NEB / GSM at high-throughput scale.

Recommended citation: K. Hegazy, E. Yuan, et al. (2025). "Neural Network Path Optimization for Finding Transition States on a Machine Learned Potential." Submitted to Nature Computational Science.

Foundation Models for Discovery and Exploration in Chemical Space

Published in Submitted to Nature, 2025

A foundation-model approach to discovery and exploration in chemical space — unified molecular representations that generalize across diverse downstream tasks in chemistry and materials discovery.

Recommended citation: A. Wadell, A. Bhutani, V. Azumah, et al. (2025). "Foundation Models for Discovery and Exploration in Chemical Space." Submitted to Nature. arXiv:2510.18900.

The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators

Published in ICLR 2026, 2026

Machine-learned operators cannot actually do zero-shot super-resolution — they’re brittle and susceptible to aliasing off the training grid. A simple, data-driven multi-resolution training protocol restores accurate cross-resolution inference.

Recommended citation: M. Sakarvadia, K. Hegazy, et al. (2026). "The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators." ICLR. https://openreview.net/forum?id=hkF7ZM7fEp&referrer=%5Bthe%20profile%20of%20Kareem%20Hegazy%5D(%2Fprofile%3Fid%3D~Kareem_Hegazy1)

Recency Biased Causal Attention for Time-series Forecasting

Published in AISTATS 2026, 2026

Introduces a recency-biased causal attention mechanism that reweights Transformer attention with a smooth heavy-tailed decay — strengthens temporally local dependencies and achieves competitive or superior performance on time-series forecasting benchmarks.

Recommended citation: K. Hegazy, M. W. Mahoney, N. B. Erichson. (2026). "Recency Biased Causal Attention for Time-series Forecasting." AISTATS. https://virtual.aistats.org/virtual/2026/poster/13795

Neural Equilibria for Long-Term Prediction of Nonlinear Conservation Laws

Published in Under review at ICML, 2026

NeurDE — a ‘neural twin’ of Boltzmann-BGK kinetic theory that pairs exact lattice-Boltzmann transport with a learned equilibrium map — delivers data-efficient, stable long-horizon forecasts of shock propagation and complex compressible dynamics.

Recommended citation: J. Benitez, K. Hegazy, et al. (2026). "Neural Equilibria for Long-Term Prediction of Nonlinear Conservation Laws." Under review at ICML.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.