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I am a first-year PhD student at the Univ. of California, San Diego (UCSD) advised by Prof. Tsui-Wei (Lily) Weng. I recently completed my MS CS thesis at UCSD, working with Prof. Weng on Interpretability-Guided Adversarial Robustness. My major research interests are in Interpretability, Adversarial Robustness, and Domain Adaptation for Deep Neural Networks.
During Summer 2023, I worked as a Research Intern at Sony R&D Center, Tokyo on Labeled Dataset Generation using Generative AI models. Before starting MS, I was a Project Assistant at Video Analytics Lab, IISc, Bangalore with Prof. R. Venkatesh Babu and his Ph.D. student, Jogendra Nath Kundu. My work there was focused on empirical and theoretical aspects of Domain Adaptation. I completed B. Tech. (Electrical & Electronics Engg.) (2016-20) from Visvesvaraya National Institute of Technology, Nagpur, where I was a core coordinator at IvLabs (Robotics Club). |
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News
- Mar. 2025 - Serving as a reviewer for ICML 2025 and ICCV 2025
- Feb. 2025 - Our paper on Concept Bottleneck Generative Models is accepted at CVPR 2025
- Sept. 2024 - Recognized as an Outstanding Reviewer for ECCV 2024
- July 2024 - Our paper on Interpretability-Guided Adversarial Robustness is accepted at ECCV 2024
- June 2024 - Serving as a reviewer for NeurIPS 2024 and WACV 2025
- Dec. 2023 - Serving as a reviewer for ICML 2024 and ECCV 2024
- Oct. 2023 - Our paper on Source-free Domain Adaptation is accepted at WACV 2024
- Oct. 2023 - Serving as a reviewer for ICLR 2024 and CVPR 2024
- July 2023 - Our paper on Source-free Domain Adaptation is accepted at ICCV 2023
- June 2023 - Working as a summer intern (Jun-Sept) at Sony R&D Center, Tokyo
- May 2023 - Serving as a reviewer for NeurIPS 2023 and WACV 2024
- Feb. 2023 - Serving as a reviewer for ICCV 2023 and CoLLAs 2023
- Older news
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Interpretable Generative Models through Post-hoc Concept Bottlenecks
Akshay Kulkarni, Ge Yan*, Chung-En Sun*, Tuomas Oikarinen, and Tsui-Wei Weng CVPR 2025 |
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Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation
J. N. Kundu*, Akshay Kulkarni*, A. Singh, Varun Jampani, and R. Venkatesh Babu ICCV 2021 |
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Balancing Discriminability and Transferability for Source-Free Domain Adaptation
J. N. Kundu*, A. Kulkarni*, S. Bhambri*, D. Mehta, S. Kulkarni, V. Jampani, R. V. Babu ICML 2022 |
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Concurrent Subsidiary Supervision for Unsup. Source-Free Domain Adaptation
J. N. Kundu*, S. Bhambri*, Akshay Kulkarni*, H. Sarkar, V. Jampani, and R. V. Babu ECCV 2022 |
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Subsidiary Prototype Alignment for Universal Domain Adaptation
J. N. Kundu*, S. Bhambri*, Akshay Kulkarni*, H. Sarkar, V. Jampani, and R. V. Babu NeurIPS 2022 |
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Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation
S. Sanyal*, A. Asokan*, S. Bhambri*, Akshay Kulkarni, J. N. Kundu, and R. V. Babu ICCV 2023 |
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Amplitude Spectrum Transformation for Open Compound Domain Adaptation
J. N. Kundu*, Akshay Kulkarni*, S. Bhambri*, Varun Jampani, and R. Venkatesh Babu AAAI 2022 |
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Aligning Non-Causal Factors for Source-Free Domain Adaptation
S. Sanyal*, A. Asokan*, S. Bhambri*, Pradyumna, A. Kulkarni, J. N. Kundu, R. V. Babu WACV 2024 |
Academic Service
- Reviewer:
- CV conferences: WACV22-25, ECCV22/24, CVPR23-24, ICCV23, ACCV24
- ML conferences: ICML22/24-25, NeurIPS22-24, ICLR24, ACML22, CoLLAs23-25
- Journals: IEEE TPAMI, IEEE TIP
- Workshop Papers Reviewer: LatinXCV (CVPR22-23, ECCV22), Shift Happens (ICML22), MLRC22, WiCV (CVPR23), SCIS (ICML23), XAIA (NeurIPS23), CRL (NeurIPS23)
- Teaching Assistant: DSC291 (Trustworthy Machine Learning, Spring '23, UCSD), DSC210 (Numerical Linear Algebra, Fall '23, UCSD), DSC291 (Trustworthy Machine Learning, Spring '24, UCSD)
Awards
- Outstanding Reviewer: ECCV 2024
