Siba Smarak Panigrahi

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I am a second year M.Sc. (Thesis) student at McGill University and Mila under the supervision of Prof. Siamak Ravanbakhsh. My research interests span geometric deep learning, generative models, and computer vision.

Previously, I was a Research Intern at PROSE at Microsoft, working on designing algorithms and evaluation set-ups for email classification in real-world (online) settings. I was fortunate to work on topological data analysis at Adobe Research, India and on explainability in pre-trained language models at INK-Lab, University of Southern California under Prof. Xiang Ren.

I earned my B. Tech. in Computer Science and Engineering from Indian Institute of Technology Kharagpur. I was part of the CVIR Lab under the supervision of Prof. Abir Das and Dr. Rameswar Panda where I worked on contextual bias and multimodal learning problems.

Other: Advice on Grad Applications | Coming to Montreal | Banff Trip |
Tips on ML Reproducibility Challenge | Summer Schools

Lore Podcast Summaries

     [Mar 24] Received GREAT award from McGill University to attend ICLR 2024!
     [Mar 24] Released open-source Python package EquiAdapt! Feedback is welcome.
     [Jan 24] One paper accepted in ICLR 2024!
     [Oct 23] Organizing ML Reproducibility Challenge 2023!
     [Oct 23] Sponsored CA$ 6000 Google Cloud credits for CampusPulse Ideathon with KDAG, IIT Kharagpur!
     [Sep 23] One paper accepted in NeurIPS 2023!
     [Sep 23] Accepted to Google's CS research mentorship program (CSRMP) Class of 2023b
     [Jul 23] Received outstanding reviewer award for ML Reproducibility Challenge 2022
     [Jul 23] One paper accepted in European Workshop on Reinforcement Learning (EWRL) 2023
     [May 23] Organizing Molecular ML Conference 2023 @ Mila!
     [Apr 23] Selected to attend CIFAR Deep Learning + Reinforcement Learning (DLRL) Summer School 2023
     [Mar 23] Selected for The Cornell, Maryland, Max Planck Pre-doctoral Research School 2023 (at Saarbrücken!)
     [Mar 23] Selected to attend Oxford ML (OxML) Summer School 2023 (MLx Health)
     [Aug 22] One paper accepted in the NeurIPS 2022 Journal Showcase Track (also NeurIPS Spotlight)
     [Jul 22] Received J.N.Tata Endowment and awarded 'JN Tata Scholar' title
     [May 22] Selected to attend Eastern European Machine Learning (EEML) Summer School 2022
     [Apr 22] One paper accepted in the ML Reproducibility Challenge 2021
     [Jan 22] Selected to attend Research Week with Google 2022
     [Dec 21] Received the Prof. J.C. Ghosh Memorial Endowment Prize for highest CGPA at the end of VI semester
     [Sep 21] One paper accepted in the 8th workshop on ArgumentMining at EMNLP 2021
     [Dec 20] One paper accepted in the 10th International Advance Computing Conference (IACC 2020)
     [Jun 19] Received the Technology Alumni Association Award for highest CGPA at the end of II semester

Efficient Dynamics Modeling in Interactive Environments with Koopman Theory
Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, Siamak Ravanbakhsh
International Conference on Learning Representations (ICLR) 2024; European Workshop on Reinforcement Learning (EWRL) 2023


Equivariant Adaptation of Large Pre-Trained Models
Arnab Kumar Mondal*, Siba Smarak Panigrahi*, Sékou-Oumar Kaba, Sai Rajeswar, Siamak Ravanbakhsh
Conference on Neural Information Processing Systems (NeurIPS) 2023


[Re]: Value Alignment Verification
Siba Smarak Panigrahi*, Sohan Patnaik*
The ML Reproducibility Challenge (MLRC), 2021;
NeurIPS Journal Showcase Track, 2022; NeurIPS Spotlight, 2022

pdf / code

Leveraging Pretrained Language Models for Key Point Matching
Manav Nitin Kapadnis*, Sohan Patnaik*, Siba Smarak Panigrahi*, Varun Madhavan*, and Abhilash Nandy
8th workshop on ArgumentMining at Empirical Methods in Natural Language Processing (EMNLP), 2021

pdf / code

Multi-class Emotion Classification Using EEG Signals
Divya Acharya, Riddhi Jain, Siba Smarak Panigrahi, Rahul Sahni, Siddhi Jain, Sanika Prashant Deshmukh, and Arpit Bhardwaj
10th International Advance Computing Conference (IACC), 2020

pdf / code

Library to make any existing neural network architecture equivariant.

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