We are a research group based at the School of Data Science, Indian Institute of Science Education and Research (IISER) Thiruvananthapuram, working at the intersection of machine learning, statistical learning, and data-centric AI. Our core interest lies in developing principled learning algorithms for complex, structured, and partially observed data, with an emphasis on clinical, biomedical, and scientific applications. A recurring theme in our work is the design of theoretically grounded yet scalable methods that remain reliable under real-world constraints such as missing data, limited labels, and heterogeneous feature types.

Current research directions include:

• Semi-supervised and structure-aware representation learning including fast node embedding methods for graphs when rich node features are missing

• Bio-inspired and neuromorphic learning paradigms with a focus on fast learning rules for spiking neural networks

• Synthetic tabular data generation with dependency preservation for clinical and physiological datasets

While some of our applications are rooted in biomedical and clinical sciences, the algorithms we develop are domain-agnostic and broadly applicable to graph data, time series, and high-dimensional tabular datasets. We strongly value collaborative research, intellectual honesty, and methodological rigor, and actively seek interdisciplinary collaborations that push the boundaries of data-driven scientific discovery.

At present, we are not accepting applications for internships. Students interested in pursuing a minor project aligned with the research directions of the lab may apply. Application emails should include a CV, an academic transcript, and a brief statement of research interests clearly aligned with the lab’s focus areas. We accept major project students exclusively from the School of Data Science.