Machine Learning Lab
Department of Computer Science and Automation, Indian Institute of Science, Bangalore.
The Machine Learning Lab of the Department of Computer Science and Automation at the Indian Institute of Science was set up to study theoretical and applied aspects of machine learning in various domains. Our aim is to explore and understand artificial intelligence, including machine learning, deep learning, numerical optimization, and natural language processing, and to perform research on their applicability in various domains.
To this end, we develop numerous machine learning algorithms and tools for complex real-world applications. We want to be able to build AI-enabled systems that solve problems for social good. We are actively pursuing applications in the areas of computational biology, object detection in images, video segmentation and summarization, detection of rare topics in text documents, and statistical modeling of computer systems.
We are located in Bangalore, which is the Silicon Valley of India. We also collaborate with industry as well as other universities on cutting-edge research.
We are unable to respond to part-time, short-term (less than one year), and/or remote student mentorship requests. For open positions, see the Opportunities page.
Our Collaborators
news
| May 01, 2026 | The paper “Blending Neural Control Density Functions for Stabilization and Safety” by Sahil Chaudhary, Chaitanya Murti, and Chiranjib Bhattacharyya was published in ICML 2026. |
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| Sep 18, 2025 | The paper “On Optimal Steering to Achieve Exact Fairness” by Mohit Sharma, Amit Deshpande, Chiranjib Bhattacharyya, and Rajiv Ratn Shah was published in NeurIPS 2025. |
| Sep 18, 2025 | The paper “ModHiFi: Identifying High Fidelity Predictive Components for Model Modification” by Dhruva Kashyap, Chaitanya Murti, Pranav K Nayak, Tanay Narshana, and Chiranjib Bhattacharyya was published as a Spotlight at NeurIPS 2025. |
| Feb 26, 2025 | The paper “CheXwhatsApp: A Dataset for Exploring Challenges in the Diagnosis of Chest X-rays through Mobile Devices” by Mariamma Antony, Rajiv Porana, Sahil M Lathiya, Siva Teja Kakileti, and Chiranjib Bhattacharyya was published in CVPR 2025. |
| Sep 25, 2024 | The paper “Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms” by Marc Wanner (Chalmers), Laura Lewis (Cambridge), Chiranjib Bhattacharyya (IISc), Devdatt Dubhashi (Chalmers), and Alexandru Gheorghiu (Chalmers) was published in NeurIPS 2024. |
selected publications
- ICMLBlending Neural Control Density Functions for Stabilization and SafetyIn Forty-third International Conference on Machine Learning, ICML 2026, 2026
- NeurIPSOn Optimal Steering to Achieve Exact FairnessIn Advances in Neural Information Processing Systems 38, NeurIPS 2025, 2025
- NeurIPSModHiFi: Identifying High Fidelity Predictive Components for Model ModificationIn Advances in Neural Information Processing Systems 38, NeurIPS 2025, 2025
- CVPRCheXwhatsApp: A Dataset for Exploring Challenges in the Diagnosis of Chest X-rays through Mobile DevicesIn IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025, Nashville, TN, USA, June 11-15, 2025, 2025
- ICLRLevAttention: Time, Space and Streaming Efficient Algorithm for Heavy AttentionsIn The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025, 2025