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!

Research

	DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers WACV 2023 PROJECT PAGE PAPER CODE 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.
	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
	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 PROJECT PAGE PAPER CODE 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: Data-free Adversarial Defense at Test Time WACV 2022 PROJECT PAGE PAPER CODE 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: A Simple but Effective solution for Visible-Thermal Person ReID BMVC 2021, Oral PROJECT PAGE PAPER CODE 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 between Robustness and Dataset Bias: An empirical study NeurIPS-2020 pre-registration Workshop 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 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: Reliably Predicting from Imbalanced Satellite Data CVPR-2020 Workshop on AGRICULTURE-VISION, ORAL 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

	DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers WACV 2023 PROJECT PAGE PAPER CODE 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.
	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
	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 PROJECT PAGE PAPER CODE 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: Data-free Adversarial Defense at Test Time WACV 2022 PROJECT PAGE PAPER CODE 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: A Simple but Effective solution for Visible-Thermal Person ReID BMVC 2021, Oral PROJECT PAGE PAPER CODE 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 between Robustness and Dataset Bias: An empirical study NeurIPS-2020 pre-registration Workshop 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 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: Reliably Predicting from Imbalanced Satellite Data CVPR-2020 Workshop on AGRICULTURE-VISION, ORAL 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

Namaste 🙏

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