Dr. Sharu Theresa Jose received her Ph.D. from Indian Institute of Technology Bombay (IITB), India in August of 2018. Her thesis was on finite blocklength information theory with her main contribution being  a novel linear programming-based framework to develop converses for coding problems in information theory. During the course of her Ph.D., she published 3 articles in the IEEE Transactions on Information Theory, and was awarded the Best Ph.D. Thesis Award from IITB.

From October 2019 to July 2022, she was a postdoctoral researcher with Prof. Osvaldo Simeone of King's College London working on problems at the intersection of information theory and machine learning.

Her broad interests lie at the intersection of machine learning (classical and quantum), statistical learning theory and information theory. She aims to use information-theory to advance theoretical understanding of learning problems as well as algorithm development. Some of her recent works include:

1. Information-theoretic generalization analysis of multi-task learning problems that include transfer learning and meta-learning

2. Quantum machine learning - generalization analysis, convergence analysis of variational quantum algorithms in NISQ devices, quantum error mitigation