Vivek Bharadwaj
I am an EECS graduate student at UC Berkeley, advised by the fantastic pair Jim Demmel and Aydın Buluç. I'm associated with the BeBOP and PASSION groups, and I'm supported by a Department of Energy Computational Science Graduate Fellowship from the Krell Institute.
- Geometric Deep Learning
- Sparse Computations
- Randomized Sketches
- Graph Algorithms
Interests
- PhD in Computer Science.
UC Berkeley, 2020-Present - BS in CS + Mathematics.
Caltech, 2016-2020
Background
Email: firstname_lastname [at] berkeley.edu


I am an EECS graduate student at UC Berkeley, advised by the fantastic pair Jim Demmel and Aydın Buluç. I'm associated with the BeBOP and PASSION groups, and I'm supported by a Department of Energy Computational Science Graduate Fellowship from the Krell Institute.
- Geometric Deep Learning
- Sparse Computations
- Randomized Sketches
- Graph Algorithms
Interests
- PhD in Computer Science.
UC Berkeley, 2020-Present - BS in CS + Mathematics.
Caltech, 2016-2020
Background
Email: firstname_lastname [at] berkeley.edu
news
Jun 22, 2025
06/22/25
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I'm presenting this poster at LBNL's Deep Learning for Science workshop - stop by to chat if you're attending. |
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Apr 5, 2025
04/05/25
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OpenEquivariance was accepted to SIAM ACDA 2025! See you in Montréal. |
Jan 26, 2025
01/26/25
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Austin Glover and I are thrilled to announce OpenEquivariance, a fast kernel generator for rotation equivariant deep neural networks. These machine learning models rotate their predictions in a compatible way when the input (a point cloud) rotates. Read it here. |
recent & upcoming talks
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Mar 4, 2025SIAM CSE 2025 Matrix Computations Minisymposium, Fort Worth, TXEngineering Fast Kernels for Rotation-Equivariant Deep Neural NetworksNov 1, 2024Subspace Embeddings for Khatri-Rao Products (Redux)May 13, 2024SIAM Conference on Applied Linear Algebra, Paris, FranceSampling-Based Sketches for Tensor Train Core Chains
selected publications
* denotes equal contribution. See more papers on the dedicated publications page.-
ACDA '25NeurIPSNeurIPS