Karush Suri

Hi! I am a research engineer at Google X in Mountain View. Previously, I was a student researcher at Borealis AI. I completed my M.A.Sc in Computer Engineering at the University of Toronto with Yuri Lawryshyn and Kostas Plataniotis. My work was supported by the Edward S. Rogers Graduate Scholarship and ECE Fellowships. I earned my B.Tech in Electrical Engineering and Applied Mathematics from Amity University where I was affiliated as an undergraduate research assistant with Rinki Gupta. My work was a recipient of the Best Undergraduate Thesis Award. Besides research, I enjoy reading comic books and novels.

I aim to create generalist agents capable of accelerating their own learning. These agents must reason about sequential patterns and structures across a broad range of environments. Towards this goal, I develop algorithms in Meta Learning, Reinforcement Learning and Graph Representation Learning for addressing real-world sequence modelling problems.

Publications

Surprise Minimizing Multi-Agent Learning with Energy-based Models
K Suri, X Q Shi, K Plataniotis, Y Lawryshyn
NeurIPS 2022
paper webpage code talk reviews

Off-Policy Evolutionary Reinforcement Learning with Maximum Mutations
K Suri
AAMAS 2022. (oral)
paper webpage code blog talk reviews

Continuous Sign Language Recognition from Wearable IMUs using CapsNet and Game Theory
K Suri, R Gupta
Computers And Electrical Engineering, Elsevier, Vol. 78, 2019.
paper code demo reviews

Transfer Learning for sEMG-based Hand Gesture Classification using Master-Slave Nets
K Suri, R Gupta
IEEE IC3I 2018.
paper

Theses

Deep Hierarchical Reinforcement Learning
K Suri
University of Toronto, M.A.Sc Thesis, 2021.
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Deep Learning and Game Theory for Wearable Sensors
K Suri
Amity University, B.Tech Thesis, 2019.
link demo

Blog Posts

Discrete Stochastic Optimization

2024.
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Data Parallelism in JAX

2024.
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Coarsening Graphs with Neural Networks
ICLR 2022 Blog Track
2021.
link reviews

Variational Generalization Bounds

2020.
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Evolution-based Soft Actor-Critic

2020.
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Stacked Capsule Autoencoders

2020.
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Benchmarking Policy Search using Cyclic MDP

2019.
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DQN with Atari in 6 Minutes

2019.
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Capsule Networks for Digit Recognition in PyTorch

2018.
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