Photo of a high performance cluster machine

Department of
Data Science

Photo of Leelavati  Narlikar

Leelavati Narlikar

Associate Professor and Deputy Chair, Data Science

Data Science

Machine learning, Statistical algorithms, Computational biology


Leelavati Narlikar received her Bachelor's degree in Computer Engineering from University of Pune and a PhD in Computer Science from Duke University, USA. She worked as a postdoctoral fellow at the National Institutes of Health and later at the Centre for Modelling and Simulation in University of Pune. She began her independent research career at the Chemical Engineering department of CSIR-National Chemical Laboratory. Since 2021, she has been a faculty member at IISER Pune.


Computational Biology and Machine Learning

Dr. Leelavati Narlikar's group works on designing algorithms for learning from diverse datasets. Their primary focus is on developing methods to gain biological insights from large-scale genome-level data. This data predominantly comes from high-throughput sequencing-based experiments, which measure various genome-wide biochemical activities. Some examples are ChIP-seq (which profiles protein-bound regions), ATAC-seq (which identifies open chromatin), STARR-seq (which assesses enhancer-activity of DNA), GRO-seq (which measures nascent transcripts). The goal is to infer the underlying sequence components that might be responsible for specific activities at the reported regions.

The group is also interested in applying their techniques to large, heterogeneous datasets in other domains such as healthcare.

Selected Publications

Biswas A., and Narlikar L. 2021. A universal framework for detecting cis-regulatory diversity in DNA regions. Genome Research, 31(9):1646-1662.

Biswas A., and Narlikar L. 2021. Resolving diverse protein-DNA footprints from exonuclease-based ChIP experiments. Bioinformatics, 37(S1):i367-i375.

Mitra S., Biswas A., and Narlikar L. 2018. DIVERSITY in binding, regulation, and evolution revealed from high-throughput ChIP. PLoS Computational Biology, 14(4):e1006090.