Tess Smidt
Associate Professor Of Electrical Engineering And Computer Science
Cambridge, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolTess Smidt is an Associate Professor of Electrical Engineering and Computer Science at MIT with 12 years of experience at the intersection of computational materials science and equivariant machine learning. She combines deep academic training (PhD, UC Berkeley) and national-lab postdoctoral work with hands-on software engineering, contributing performance-critical back-end code to prominent open-source projects like e3nn (Euclidean symmetry neural nets) and pymatgen (Materials Project). Her work focuses on optimizing core mathematical operations—spherical harmonics, rotations, interpolation—and building robust workflow tooling, demonstrating a rare blend of geometry-first thinking and practical engineering. Based in Cambridge, she brings production-grade software practices to research problems, shipping utilities and tests as well as novel algorithms. Outside academia she’s long experimented with personal projects (blondegeek) and even has an architecture background, which informs her attention to structure and design in code.
12 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Physics, Doctor of Philosophy (Ph.D.) Physics at University of California, BerkeleySB Physics with Minor in Architecture, SB Physics with Minor in Architecture at Massachusetts Institute of Technology
English, Russian
Github Skills (16)
ec10
struct10
pytorch10
scipy10
workflow-automation10
vectorization10
python10
fireworks10
linear-algebra10
harmonic10
computational-materials-science10
science9
material38
material-design8
materials-informatics8
Programming languages (7)
Github contributions (5)
e3nn/e3nn
Dec 2018 - Aug 2021
A modular framework for neural networks with Euclidean symmetry
Role in this project:
Back-end Developer
Back-end DeveloperContributions:4 releases, 1 review, 207 commits in 2 years 8 months
Contributions summary:Tess's contributions center around vectorization of functions, specifically within the context of spherical harmonics and rotation operations. The commits demonstrate the optimization of core mathematical functions related to the se3cnn/SO3 module, indicating a focus on improving the performance of fundamental operations. The work appears to primarily involve improving the efficiency of mathematical functions, and introducing utilities.
neural-network
materialsproject/pymatgen
Nov 2015 - Jan 2019
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
Role in this project:Back-end Developer
Back-end DeveloperContributions:40 commits, 13 PRs, 25 comments in 3 years 2 months
Contributions summary:Tess primarily contributed to the `pymatgen` repository by improving the `BSPlotter` class within the `pymatgen.electronic_structure` module. Their work focused on adding warnings for interpolation failures and incorporating a smooth tolerance for the `scipy.interpolate.splrep` function. They also merged several branches, indicating a role in integrating and managing code changes. Further contributions were seen in other areas like the boltztrap module and the ferroelectricity module.
pymatgenpythonsciencematerials-sciencematerials-informatics
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