Hi! I'm an Assistant Professor of computer science at Northeastern University and director of the Geometric Learning Lab. My research is on symmetry in deep learning.
Our research spans from theory of deep learning through the lens of symmetry to development of new equivariant models to applications of equivariant neural networks to a diverse set of applications in science and engineering.
Updates
Awarded the NSF CAREER Grant for the project Improving Data Efficiency in Deep Learning with Relaxed Symmetry Constraints, receiving $600,000 for the period 2025-2030.
Awarded the Northeastern TEIR 1 Interdisciplinary Seed Grant funding of $50,000, in collaboration with Peter Schindler (MIE).
Awarded the EAI Seed Funding of $60,000, in collaboration with Peter Schindler (MIE).
I will be presenting Research on Improving Convergence and Generalization in Deep Learning Using Parameter Symmetries at the GATech AI4Science Presentation in October 2025.
I will be presenting Research on Equivariant Neural Networks for Dynamics and Control at the Woods Hole Oceanographic Research Institute in November 2025.
I will be speaking at the NeurReps Workshop in San Diego, CA in December 2025.
I will present at the Equivision Workshop during CVPR in Nashville, TN in June 2025.
I will give a talk at the GRASP Seminar at the University of Pennsylvania in May 2025.
I will be presenting on the topic of Improving Convergence and Generalization in Deep Learning Using Parameter Symmetries during the Joint Mathematics Meetings AMS Special Session in Seattle, WA in January 2025.
Recognized as an Outstanding Paper Finalist at the Conference on Robot Learning (CoRL) , 2024.
Awarded Best Paper at the 2nd Workshop on High-dimensional Learning Dynamics at ICLR , 2024.
I will present Research at the AstroAI Seminar at the Harvard Smithsonian Center for Astrophysics in June 2024.
I will discuss Equivariant Neural Networks at the Computer Vision Reading Group at CalTech in June 2024.
I will speak about pushing the limits of Equivariant Neural Networks at the IROS Workshop on Equivariant Robotics in Abu Dhabi, UAE in October 2024.
I will share insights on pushing the limits of Equivariant Neural Networks at The AI Institute in Cambridge, MA in October 2024.
I will discuss pushing the limits of Equivariant Neural Networks during the NeurReps Global Speaker Series at MIT in October 2024.
I will deliver an oral session on Improving Convergence and Generalization in Deep Learning Using Parameter Symmetries at ICLR in Vienna, Austria in May 2024.
I will present Research on Simulating Radar Using Equivariant Graph Neural Networks at the Recent Advances in AI for National Security Conference in Bedford, MA in November 2024.
I will discuss Equivariant Neural Networks at the Symposium on Graphics Processing Graduate School at MIT in June 2024.
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PUBLICATIONS
1. Equivariant action sampling for reinforcement learning and planning. Workshop on the Algorithmic Foundations of Robotics (WAFR), 2025.
(authors: Linfeng Zhao, Owen Howell, Xupeng Zhu, Jung Yeon Park, Zhewen Zhang, Robin Walters, and Lawson LS Wong. )
2. Data-Free Transformer Quantization Using Parameter-Space Symmetry. High-Dimensional Learning Dynamics Workshop, 2025.
(authors: Lucas Laird, Bo Zhao, Rose Yu, and Robin Walters. )
3. Se (3)-equivariant diffusion policy in spherical fourier space. In Forty-second International Conference on Machine Learning (ICML), 2025.
4. Understanding mode connectivity via parameter space symmetry. In Forty-second International Conference on Machine Learning (ICML), 2025.
(authors: BoZhao, NimaDehmamy, Robin Walters, and RoseYu.)
5. Hierarchical equivariant policy via frame transfer. In Forty-second International Conference on MachineL earning (ICML), 2025.
(authors: Haibo Zhao, Dian Wang, Yizhe Zhu, Xupeng Zhu, Owen Lewis Howell, Linfeng Zhao, Yaoyao Qian, Robin Walters, and Robert Platt.)
6. Approximate equivariance in reinforcement learning. Artificial Intelligence and Statistics (AISTATS), 2025.
(authors: Jung Yeon Park, Sujay Bhatt, Sihan Zeng, Lawson LS Wong, Alec Koppel, Sumitra Ganesh, and Robin Walters.)
7. Reducing the sensitivity of neural physics simulator stomeshtopology via pretraining. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2025.
(authors: Justin Goodwin, Robin Walters, and Rajmonda Caceres.)
8. Learning efficient and robust language-conditioned manipulation using textual-visual relevancy and equivariant language mapping. IEEE Robotics and Automation Letters (RAL), 2025.
(authors: Mingxi Jia, Haojie Huang, Zhewen Zhang, Chenghao Wang, Linfeng Zhao, Dian Wang, Jason Xinyu Liu, Robin Walters, Robert Platt, and Stefanie Tellex.)
9. Push-grasp policy learning using equivariant models and grasps core optimization. IEEE Robotics and Automation Letters (RAL), 2025.
(authors: Boce Hu, Heng Tian, Dian Wang, Haojie Huang, Xupeng Zhu, Robin Walters, and Robert Platt.)
10. Symmetry-informed governing equation discovery. Neural Information Processing Systems (NeurIPS), 2024.
(authors: Jianke Yang, Wang Rao, Nima Dehmamy, Robin Walters, and Rose Yu.)
11. Matrixnet: Learning over symmetry groups using learned group representations. Neural Information Processing Systems (NeurIPS), 2024.
(authors: Lucas Laird, Circe Hsu, Asilata Bapat, and Robin Walters. )
12. The empirical impact of neural parameter symmetries, or lack thereof. Neural Information Processing Systems (NeurIPS) and Best Paper at Workshop on High-Dimensional Learning Dynamics (HiLD) at ICLR, 2024.
(authors: Derek Lim, Moe Putterman, Robin Walters, Haggai Maron, and Stefanie Jegelka.)
13. Equivariant diffusion policy. Out-standing Paper Finalist at Conference on Robot Learning (CoRL), 2024.
(authors: Dian Wang, Stephen Hart, David Surovik, Tarik Kelestemur, Haojie Huang, Haibo Zhao, Mark Yeatman, Jiuguang Wang, Robin Walters, and Robert Platt.)
14. Orbitgrasp: se(3)-equivariant grasp learning. Conference on Robot Learning (CoRL),2024.
(authors: Boce Hu, Xupeng Zhu, Dian Wang, Zihao Dong, Haojie Huang, Chenghao Wang, Robin Walters, and Robert Platt.)
15. Imagination policy: Using generative point cloud models for learning manipulation policies. Conference on Robot Learning (CoRL), 2024.
(authors: Haojie Huang, Karl Schmeckpeper, Dian Wang, Ondrej Biza, Yaoyao Qian, HaotianLiu, Mingxi Jia, Robert Platt, and Robin Walters.)
16. Fourier transporter: Bi-equivariant robotic manipulation in 3d. International Conference on Learning Representations (ICLR), 2024.
(authors: Haojie Huang, Owen Howell, Xupeng Zhu, Dian Wang, Robin Walters, and Robert Platt.)
17. Improving convergence and generalization using parameter symmetries. International Conference on Learning Representations (ICLR), Oral, 2024.
(authors: Bo Zhao, Robert M Gower, Robin Walters, and Rose Yu.)
18. Fast and expressive gesture recognition using a 2/12 combination-homomorphic electromyogramencoder. Transactionson Machine Learning Research, 2024.
(authors: Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger, Tales Imbiriba, Eugene Tunik, Deniz Erdogmus, Mathew Yarossi, and Robin Walters.)
19. International Mathematics Research Notices, 2024(24):14543–14575, 2024.
(authors: Valerio Toledano Laredo and RobinWalters. On the finkelberg – ginzburg mirabolic monodromy conjecture.)
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