Elyssa Hofgard

Cambridge, MA

ehofgard@mit.edu

I’m on the job market for positions in academia and industry, starting Fall 2027! Please reach out if you’d like to chat.

I’m currently a PhD student in Electrical Engineering and Computer Science (EECS) at MIT. I’m a member of the Atomic Architects Research Group, advised by Professor Tess Smidt. I also work closely with the Griffin Group in the Molecular Foundry at Lawrence Berkeley National Laboratory. I received a master’s degree in Computational and Mathematical Engineering (‘22) and a bachelor’s degree in physics with honors (‘21), both from Stanford University. I’m funded by the Department of Energy Computational Science Graduate Fellowship.

I worked with the ATLAS collaboration at CERN during undergrad and my master’s degree with Professor Lauren Tompkins. In my PhD, I pivoted to computational materials science and machine learning (ML) development. I’m passionate about applying computational tools to materials design and problems in condensed matter physics. I enjoy working on projects where I am able to collaborate across disciplines, learn about a new scientific field, and interface with experimentalists. Lately, I have been working on modeling phase transitions in exotic magnetic materials, characterizing length scales of order in amorphous materials, and developing representation learning methods for applications in powder X-ray diffraction.

I also enjoy teaching and mentoring. I’m currently a teaching assistant for MIT 18.C06 (Linear Algebra and Optimization) and serve as a mentor for MIT’s undergraduate research program. See my Google Scholar and my CV for more information.

Outside of research, I love to hike and spend time outdoors. I also am an avid swimmer and a member of the MIT Women’s Chorale.

If you’re interested in probability theory or PDEs, you may be looking for my brother Jake Hofgard. He is a PhD student in mathematics at UC Berkeley and an excellent mountaineer.

news

Oct 27, 2026	Our paper FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions, led by undergraduate researcher Kyucheol Min, received a spotlight at the NeurIPS 2026 AI4Mat Workshop (OpenReview)!
Oct 05, 2026	Our paper Learning Lattice Parameters from Powder X-Ray Diffraction Data Using Invariants has been accepted at Acta Crystallographica A!
Jun 02, 2026	Starting my second practicum at Lawrence Berkeley National Laboratory with the Griffin Group through the DOE Computational Science Graduate Fellowship — here until August 21!
Mar 16, 2026	Presenting Using Equivariant Neural Networks to Learn Multipolar Order Parameters at the APS Global Physics Summit 2026 — Altermagnetism II session, Mon. March 16, 3:30–6:18 p.m., Convention Center Room 503. Come say hi if you’re at APS!
Jan 26, 2026	Our paper To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking has been accepted at ICLR 2026!

latest posts

Oct 11, 2026	Learning Lattice Parameters from Powder X-Ray Diffraction Data Using Invariants
Mar 02, 2026	To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking

selected publications

Learning Lattice Parameters from Powder X-ray Diffraction Data Using Invariants

Elyssa Hofgard, Kyucheol Min, Nofit Segal, and 7 more authors

To appear, Acta Crystallographica Section A, 2026

arXiv Bib

@article{hofgard2026lattice,
  title = {Learning Lattice Parameters from Powder {X}-ray Diffraction Data Using Invariants},
  author = {Hofgard, Elyssa and Min, Kyucheol and Segal, Nofit and Mittan-Moreau, David W. and Tehrani, Aria Mansouri and Oklejas, Vanessa and Nigam, Jigyasa and Paley, Daniel W. and Brewster, Aaron S. and Smidt, Tess},
  journal = {To appear, Acta Crystallographica Section A},
  year = {2026},
}

To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking

Hannah Lawrence^*, Elyssa Hofgard^*, Vasco Portilheiro, and 3 more authors

In International Conference on Learning Representations, 2026

arXiv Bib HTML

@inproceedings{lawrence2025augment,
  title = {To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking},
  author = {Lawrence, Hannah and Hofgard, Elyssa and Portilheiro, Vasco and Chen, Yuxuan and Smidt, Tess and Walters, Robin},
  year = {2026},
  booktitle = {International Conference on Learning Representations},
}

Relaxed Equivariant Graph Neural Networks

Elyssa Hofgard, Rui Wang, Robin Walters, and 1 more author

In ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling, 2024

arXiv Bib

@inproceedings{hofgard2024relaxed,
  title = {Relaxed Equivariant Graph Neural Networks},
  author = {Hofgard, Elyssa and Wang, Rui and Walters, Robin and Smidt, Tess},
  booktitle = {ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling},
  year = {2024},
}

Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution

Rui Wang, Elyssa Hofgard, Han Gao, and 2 more authors

In Forty-First International Conference on Machine Learning, 2024

Bib HTML

@inproceedings{wang2024discovering,
  title = {Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution},
  author = {Wang, Rui and Hofgard, Elyssa and Gao, Han and Walters, Robin and Smidt, Tess},
  booktitle = {Forty-First International Conference on Machine Learning},
  year = {2024},
}