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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Kareem Hegazy
Postdoctoral researcher at UC Berkeley / Berkeley Lab / ICSI. Physics PhD from Stanford.
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Posts
Welcome to the Blog (Gibberish Edition)
Published:
A short lorem-ipsum note standing in for a real welcome post — meant only to exercise the theme’s typography, MathJax, and the collapsible derivation component.
A Technical Gibberish Post: Lorem Gradients and Ipsum Solvers
Published:
Mid-length filler post exercising inline and display math, aligned derivations inside collapsible toggles, code blocks, and a small table — so the theme’s technical-post rendering can be inspected end to end.
A Longer Gibberish Essay: Ipsum Flows on Lorem Manifolds
Published:
Longer placeholder essay that stress-tests the theme on a multi-screen technical post: display math, aligned derivations, several collapsible proof-sketches, and a closing table.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
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
Tracking Dissociation Pathways of Nitrobenzene via MeV Ultrafast Electron Diffraction
Published in J. Phys. B, 57, 195101 (2024), 2024
Resolving photoinduced dissociation pathways of nitrobenzene using MeV ultrafast electron diffraction.
Recommended citation: K. Hegazy, et al. (2024). "Tracking Dissociation Pathways of Nitrobenzene via MeV Ultrafast Electron Diffraction." J. Phys. B, 57, 195101. https://iopscience.iop.org/article/10.1088/1361-6455/ad7431/ampdf
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
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
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.