Dr Sharu Jose PhD

School of Computer Science
Assistant Professor

Contact details

Email: s.t.jose@bham.ac.uk
Twitter: @sharu_theresa

Address: School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Sharu Jose is an Assistant Professor in the Department of Computer Science. Her research interests lie broadly at the intersection of information theory, learning theory and machine learning. In particular, she is interested in information-theoretic generalization analysis of classical and quantum machine learning models, hybrid quantum-classical variational algorithms (convergence/generalization) and quantum error mitigation.

Qualifications

Ph.D. in Information Theory, 2018 (Indian Institute of Technology Bombay, India)
M.Tech in Systems and Control Engineering, 2013
(Indian Institute of Technology Roorkee, India)
B.Tech in Electrical and Electronics Engineering, 2011
(University of Kerala, India)

Biography

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.

Postgraduate supervision

Quantum Machine Learning - Theoretical and Algorithmic Foundations

Research

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

Publications

Recent publications

Article

Thomas, AM, Youel, H & Jose, ST 2025, 'VAE-QWGAN: addressing mode collapse in quantum GANs via autoencoding priors', Quantum Machine Intelligence, vol. 7, no. 2, 91. https://doi.org/10.1007/s42484-025-00314-z

Banchi, L, Pereira, JL, Jose, ST & Simeone, O 2024, 'Statistical Complexity of Quantum Learning', Advanced Quantum Technologies. https://doi.org/10.1002/qute.202300311

Jose, ST & Moothedath, S 2024, 'Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis', Entropy, vol. 26, no. 7, 606. https://doi.org/10.3390/e26070606

Zhang, Y, Simeone, O, Jose, ST, Maggi, L & Valcarce, A 2023, 'Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management', IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 5, pp. 1282-1295. https://doi.org/10.1109/TCCN.2023.3287240

Jose, ST & Simeone, O 2022, 'An information-theoretic analysis of the cost of decentralization for learning and inference under privacy constraints', Entropy, vol. 24, no. 4, 485. https://doi.org/10.3390/e24040485

Jose, ST & Simeone, O 2022, 'Error-Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis', IEEE Transactions on Quantum Engineering, vol. 3, 3103119. https://doi.org/10.1109/TQE.2022.3229747

Jose, ST, Simeone, O & Durisi, G 2022, 'Transfer meta-learning: information- theoretic bounds and information meta-risk minimization', IEEE Transactions on Information Theory, vol. 68, no. 1, pp. 474-501. https://doi.org/10.1109/TIT.2021.3119605

Conference contribution

Jose, S, Moothedath, S & Moghimi, M 2025, Neural Contextual Bandits Under Delayed Feedback Constraints. in 2025 IEEE 64th Conference on Decision and Control (CDC). Proceedings of the IEEE Conference on Decision & Control, IEEE, 2025 IEEE 64th Conference on Decision and Control, Rio de Janeiro, Brazil, 10/12/25.

Chu, Z, Jose, ST & Stella, L 2025, PrELIN: Provably Efficient Local-Information Networked Multi-Agent Reinforcement Learning. in 2025 IEEE 64th Conference on Decision and Control (CDC). Proceedings of the IEEE Conference on Decision & Control , IEEE, 2025 IEEE 64th Conference on Decision and Control, Rio de Janeiro, Brazil, 10/12/25.

Thomas, AM, Chan, Y-C, Valencia, HO, Jose, ST & Wu, R 2025, QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning. in IEEE Quantum Artificial Intelligence. IEEE, 2025 IEEE International Conference on Quantum Artificial Intelligence, Naples, Italy, 2/11/25.

Georgiou, P, Jose, S & Simeone, O 2024, Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis. in 2024 IEEE International Symposium on Information Theory (ISIT)., 10619403, IEEE International Symposium on Information Theory proceedings, IEEE, 2024 IEEE International Symposium on Information Theory (ISIT), Athens, Greece, 7/07/24. https://doi.org/10.1109/ISIT57864.2024.10619403

Jose, S, Park, S & Simeone, O 2022, Information-theoretic analysis of epistemic uncertainty in Bayesian meta-learning. in G Camps-Valls, FJR Ruiz & I Valera (eds), International Conference on Artificial Intelligence and Statistics, 28-30 March 2022, A Virtual Conference. Proceedings of Machine Learning Research, vol. 151, PMLR, pp. 9758-9775, The 25th International Conference on Artificial Intelligence and Statistics, 28/03/22. <https://proceedings.mlr.press/v151/theresa-jose22a.html>

Jose, S & Simeone, O 2021, An information-theoretic analysis of the impact of task similarity on meta-learning. in 2021 IEEE International Symposium on Information Theory (ISIT)., 9517767, IEEE International Symposium on Information Theory proceedings, IEEE, pp. 1534-1539, 2021 IEEE International Symposium on Information Theory, Melbourne, Victoria, Australia, 12/07/21. https://doi.org/10.1109/ISIT45174.2021.9517767

Jose, S & Simeone, O 2021, A unified PAC-Bayesian framework for machine unlearning via information risk minimization. in 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)., 9596170, Machine learning for signal processing , IEEE. https://doi.org/10.1109/MLSP52302.2021.9596170

Rezazadeh, A, Jose, S, Durisi, G & Simeone, O 2021, Conditional mutual information-based generalization bound for meta learning. in 2021 IEEE International Symposium on Information Theory (ISIT)., 9518020, IEEE International Symposium on Information Theory proceedings, IEEE, pp. 1176-1181. https://doi.org/10.1109/ISIT45174.2021.9518020

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