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 setup 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 area of computational biology, object detection in images, video segmentation and summarization, detection of rare topics in text documents, statistical modeling of computer systems.

We are located in Bangalore which is the silicon valley of India. We are also collaborating with industries as well as other universities for cutting edge research.

news

Jan 15, 2016 A simple inline announcement with Markdown emoji! :sparkles: :smile:
Nov 07, 2015 A long announcement with details
Oct 22, 2015 A simple inline announcement.

selected publications

  1. CheXwhatsApp: A Dataset for Exploring Challenges in the Diagnosis of Chest X-rays through Mobile Devices
    Mariamma Antony, Rajiv Porana, Sahil M. Lathiya, and 2 more authors
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025, Nashville, TN, USA, June 11-15, 2025, 2025
  2. LevAttention: Time, Space and Streaming Efficient Algorithm for Heavy Attentions
    Ravindran Kannan, Chiranjib Bhattacharyya, Praneeth Kacham, and 1 more author
    In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025, 2025
  3. DO-EM: Density Operator Expectation Maximization
    Adit Vishnu, Abhay Shastry, Dhruva Kashyap, and 1 more author
    CoRR, 2025
  4. When Routers, Switches and Interconnects Compute: A processing-in-interconnect Paradigm for Scalable Neuromorphic AI
    Madhuvanthi Srivatsav R, Chiranjib Bhattacharyya, Shantanu Chakrabartty, and 1 more author
    CoRR, 2025
  5. Random Separating Hyperplane Theorem and Learning Polytopes
    Chiranjib Bhattacharyya, Ravindran Kannan, and Amit Kumar
    In 51st International Colloquium on Automata, Languages, and Programming, ICALP 2024, July 8-12, 2024, Tallinn, Estonia, 2024
  6. DisCEdit: Model Editing by Identifying Discriminative Components
    Chaitanya Murti and Chiranjib Bhattacharyya
    In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024, 2024