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. Currently, I am also a visiting researcher at ServiceNow Research in the Multimodal Foundation Models team, building foundation model for structured document understanding.

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 | Montreal, Canada | Banff, Canada |
ML Reproducibility Challenge (MLRC) | Summer Schools

  Recent News
     [May 24] Two papers accepted in EquiVision workshop, CVPR 2024 with a spotlight talk!
     [Apr 24] Gave a talk on Equivariant Adaptation of Large Pretrained Models at Google Research India. Slides here.
     [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

Improved Canonicalization for Model Agnostic Equivariance
Siba Smarak Panigrahi, Arnab Kumar Mondal
Equivariant Vision (EquiVision) workshop (CVPR) 2024


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*, S├ękou-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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