Tess Smidt
Associate Professor
Develops Euclidean neural networks that respect the symmetries of physical space, such as e3nn, for molecular and materials simulation.
Tess Smidt's lab at MIT works on proteins, drugs & materials, physics-based simulation & animation and physics, climate & scientific computing. 12 papers since 2023, 6 at top venues, mostly ICLR and NeurIPS. 8 PhD students and 1 master's student. 16 alumni, most often to NVIDIA. Applications close .
At a glance
- papers, 2023–2026
- 3/ year
- at T1 venues
- 50%
- PhD students
- 8
- MS students
- 1
- alumni listed
- 16
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 5 papers2025 · T1 2
- 4 papers2024 · T1 3
- 3 papers2023 · T1 1
T1 venuesOtherVenues: ICLR, NeurIPS, ICML, TMLR
Research focuspaper titles that use each topic's words
- Proteins, drugs & materials2 papers
- Physics-based simulation & animation1 paper
- molecular1 paper
- Physics, climate & scientific computing0 papers
- space0 papers
Paper list
From the lab homepage.
20255 papers
- NeurIPS (T1)A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models (opens in a new tab)
- NeurIPS AI for Science WorkshopAccelerating Protein Molecular Dynamics Simulation with DeepJump (opens in a new tab)
- NeurIPS Machine Learning and the Physical Sciences WorkshopImplicit Augmentation from Distributional Symmetry in Turbulence Super-Resolution (opens in a new tab)
- ICML (T1)The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products (opens in a new tab)
- NeurIPS AI for Materials WorkshopTraining a Foundation Model for Materials on a Budget (opens in a new tab)
20244 papers
- NeurIPS (T1)A recipe for charge density prediction (opens in a new tab)
- ICLR (T1)Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations (opens in a new tab)
- Bulletin of the American Physical SocietyEquivariant symmetry breaking (opens in a new tab)
- TMLR (T1)Generalizing denoising to non-equilibrium structures improves equivariant force fields (opens in a new tab)
20233 papers
- Machine Learning: Science and Technology, 4(1):015027A recipe for cracking the quantum scaling limit with machine learned electron densities (opens in a new tab)
- NeurIPS AI for Science WorkshopRelaxed octahedral group convolution for learning symmetry breaking in 3d physical systems (opens in a new tab)
- ICLR (T1)Symphony: Symmetry-equivariant point-centered spherical harmonics for 3d molecule generation (opens in a new tab)
Where alumni went16 listed, 22% to industry
Industry 2Academia 7
Most often to
- NVIDIA
Also to
- MIT
- the University of Amsterdam
- UC Berkeley
Apply to MITFall 2027
MIT EECS Computer Science PhD
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- Single round
- Opens
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- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
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Data
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- Papers
- The lab homepage
- Members
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- Lab homepage
Tiers are venue classifications, not lab rankings.
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