Research Interests
I'm interested in reinforcement learning, computer vision, efficient deep learning and robotics. I hope to work on research problems that deeper my current understanding of these subjects, while also prompting me to think in new ways.
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Publications
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Towards Adversarial Robustness And Backdoor Mitigation in SSL
Nilaksh*, Aryan Satpathy*, Dhruva Rajwade*, Somesh Kumar
Accepted at IEEE International Conference on Acoustics, Speech, and Signal Processing, 2025
arXiv
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code
Defense against backdoor attacks on Self-Supervised Learning in frequency space.
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Adaptive Reinforcement Learning for Robot Control
Yu Tang Liu, Nilaksh, Aamir Ahmad
Accepted at IEEE International Conference on Intelligent Robots and Systems (IROS), 2024
paper
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code
A novel adaptive agent that leverages transfer learning techniques to dynamically adapt policy in response to different tasks and environmental conditions
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Barrier Functions Inspired Reward Shaping for Reinforcement Learning
Nilaksh, Abhishek Ranjan, Shreenabh Agrawal, Aayush Jain, Pushpak Jagtap, Shishir Kolathaya
IEEE International Conference on Robotics and Automation (ICRA), 2024
paper
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code
A novel safety-oriented reward-shaping framework inspired by barrier functions, offering simplicity and ease of implementation across various environments and tasks.
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Projects
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Automated Geometry Problem Solver
Research Project, 2022
code
A solver for SAT level geometry problems that uses a domain adapted BART transformer to predict correct geometric theorem sequences.
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TinyCompiler: a compiler for BASIC
Personal Project, 2023
code
A simple compiler written in C++ that compiles a BASIC like language to C code. Produces the abstract syntax tree (AST) of the source for optimizations.
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NSES: Natural Selection Evolution Simulator
Term Project, 2020
code
Simulates natural selection in a small world by mutating rudimentary genes and using a simple genetic algorithm. Conforms to the Lotka–Volterra predator–prey model.
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