I am a CS PhD student at Mila and Concordia University advised by Prof. Eugene Belilovsky. My research is supported by FRQNT and Frederick Lowy Scholars fellowships.
Before starting my PhD, I was a Visiting Scholar with Prof. Devi Parikh and Prof. Dhruv Batra at Georgia Tech, where I worked on building multi-modal embodied agents which can navigate in a photo-realistic environment using visual and language cues.
I completed my Bachelors and Masters in Electronics and Communication Engineering (ECE) in 2019 at IIIT Hyderabad, where I pursued research in long-term visual object tracking with Prof. Vineet Gandhi. My Masters thesis is available here.
During my Masters, I also spent two wonderful semesters at UC San Diego and Stanford University in 2018. I worked with Prof. Sicun Gao at UC San Diego on sample efficient Reinforcement Learning algorithms for Atari games. At Stanford, I collaborated with Prof. Noah Goodman on recognizing humor in text.
Jun 2025 | Released assayer for automatic ML model checkpoint monitoring and evaluation! |
Jun 2025 | Celo accepted to TMLR! JAX code is released here. |
May 2025 | Project with Apple MLR on understanding input selectivity in Mamba accepted to ICML 2025! |
Jan 2025 | NiNo accepted to ICLR 2025! |
Apr 2024 | Interning at Apple MLR Barcelona x Cambridge! |
Dec 2023 | Preprint out on arXiv: Can We Learn Communication-Efficient Optimizers? |
Sep 2023 | Got married! |
Aug 2023 | Reviewed for TPAMI journal. |
Apr 2023 | Won FRQNT fellowship, thanks Gouvernement du Québec! |
Jul 2022 | Received Outstanding Reviewer award at ICML 2022! |
(* denotes equal contribution)
Celo: Training Versatile Learned Optimizers on a Compute Diet
Abhinav Moudgil, Boris Knyazev, Guillaume Lajoie, Eugene Belilovsky
TMLR, 2025
Accelerating Training with Neuron Interaction and Nowcasting Networks
Boris Knyazev, Abhinav Moudgil, Guillaume Lajoie, Eugene Belilovsky, Simon Lacoste-Julien
ICLR, 2025
Meta-learning Optimizers for Communication-Efficient Learning
Charles-Étienne Joseph*, Benjamin Thérien*, Abhinav Moudgil, Boris Knyazev, Eugene Belilovsky
TMLR, 2025
Learning to Optimize with Recurrent Hierarchical Transformers
Abhinav Moudgil, Boris Knyazev, Guillaume Lajoie, Eugene Belilovsky
Frontiers4LCD Workshop, ICML 2023
Towards Scaling Difference Target Propagation by Learning Backprop Targets
Maxence Ernoult, Fabrice Normandin*, Abhinav Moudgil*, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake Richards, Yoshua Bengio
ICML 2022
SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation
Abhinav Moudgil, Arjun Majumdar, Harsh Agrawal, Stefan Lee, Dhruv Batra
NeurIPS 2021
Contrast and Classify: Alternate Training for Robust VQA
Yash Kant, Abhinav Moudgil, Dhruv Batra, Devi Parikh, Harsh Agrawal
ICCV 2021, NeurIPS Self-Supervised Learning Workshop 2020
Exploring 3Rs of Long-term Tracking: Re-detection, Recovery and Reliability
Shyamgopal Karthik, Abhinav Moudgil, Vineet Gandhi
WACV 2020
Long-Term Visual Object Tracking Benchmark
Abhinav Moudgil, Vineet Gandhi
ACCV 2018 (Oral Presentation)
Python RQ watchdog to automatically monitor and evaluate ML model checkpoints offline during training.
Implements custom distributed scheme for our DTP algorithm (ICML 2022) in PyTorch, parallelizing feedback weight training across GPUs.
Fast PyTorch implementation of visual tracker GOTURN (Held et al., ECCV 2016) which tracks an input object in a video at 100FPS with a deep siamese convolutional network.
MATLAB implementation of MOSSE tracker (Bolme et al., CVPR 2010), which forms the basis for all the correlation filter-based object tracking algorithms.
Python implementation which reproduces results of the paper “A computational model of linguistic humor in puns” (Kao et al., CogSci 2015). It employs a probabilistic model to compute funniness rating for a given sentence.
Collection of Python scripts for building Short Jokes dataset containing 231,657 jokes scraped from various websites like Reddit, Twitter etc.
Implementation of various algorithms like Deep Q-learning, Policy Gradient, Simulated Annealing and Hill Climbing in Tensorflow / PyTorch; tested on OpenAI Gym environments.