Ruchit Rawal


Namaste 🙏

I am a research intern in the BrAIN group at the Max Planck Institute for Software Systems (MPI-SWS) advised by Prof. Mariya Toneva. My primary research interests are centered around the intersection of machine learning and computational neuroscience.

Previously I was a (research) project assistant at the Indian Institute of Science under the guidance of Prof. Anirban Chakraborty, where I developed methods for training adversarially robust neural networks under low-resource constraints.

Feel free to drop me an email, if you would like to discuss possible collaborations!

Recent News


de-crop overview

Performance of existing certified defenses significantly deteriorates as training data size is reduced. We introduce a novel data-augmentation technique that improves their data-efficiency.

Gaurav Kumar Nayak*, Ruchit Rawal*, Anirban Chakraborty.

Dataset bias & Robustness
Dynamics of Dataset Bias and Robustness
Principles of Distribution Shift (PODS), ICML-Workshop 2022

Exploring the effects of various techniques for improving robustness under distribution-shift on the dataset-bias.

Prabhu Pradhan*, Ruchit Rawal*

Sample Level Adversarial Vulnerability overview
Holistic Approach To Measure Sample-Level Adversarial Vulnerability and Its Utility in Building Trustworthy Systems
CVPR-2022 Wokrshop on Human-centered Intelligent Services: Safety and Trustworthy

Propose a holistic approach for quantifying adversarial vulnerability of a sample by combining multiple perspectives.

Gaurav Kumar Nayak*, Ruchit Rawal*, Rohit Lal*, Himanshu Patil, Anirban Chakraborty.

Dad Overview

We propose a novel problem of "test-time adversarial defense in absence of training data and even their statistics" and provide first steps to solve it.

Gaurav Kumar Nayak*, Ruchit Rawal*, Anirban Chakraborty.

MMD-ReID Overview

Learning modality invariant features is central to the problem of Visible-Thermal cross-modal Person Reidentification (VT-ReID). We propose a simple but effective framework, MMD-ReID, that reduces the modality gap by an explicit discrepancy reduction constraint.

Chaitra Jambigi*, Ruchit Rawal*, Anirban Chakraborty

Rendezvous Overview

We shine a light on the effects of various techniques for perturbarial robustness on dataset-distribution bias (i.e. class imbalance).

Prabhu Pradhan*, Ruchit Rawal*, Gopi Kishan

Generalizing across the (in)visible spectrum Overview
Generalizing across the (in)visible spectrum
ICML-2020 Workshop on Extreme Classification, ORAL

Improvements in robustness do not translate to better intrinsic generalization (esp. in case of high imbalance or long-tail scenarios).

Ruchit Rawal*, Prabhu Pradhan*

Climate Adaptation Overview
Climate Adaptation: Reliably Predicting from Imbalanced Satellite Data

Real-World datasets usually suffer from high class imbalance, impeding a model's ability to learn effectively. We provide a comprehensive guide on the effectiveness of various state-of-the-art methodologies in a combination setting.

Ruchit Rawal*, Prabhu Pradhan*