Professor & IEEE Fellow · Northeastern University London
I work at the intersection of information theory, machine learning, and quantum information processing. My group develops principled methods for reliable and efficient communication, computing, and learning systems — from Bayesian learning and neuromorphic computing to quantum machine learning and conformal prediction.
Classical and Quantum Information Theory published by Cambridge University Press.
Spotlight paper (top 3.1%) at NeurIPS 2025 on adaptive prediction-powered evaluation.
Fundamental limits for communication and learning systems.
Quantum machine learning, communication, and information-theoretic aspects of quantum computing.
Bayesian learning, conformal prediction, meta-learning, and principled approaches to uncertainty.
Spiking neural networks and brain-inspired architectures for efficient learning and inference.
Conformal calibration and AI-native wireless design.
I am always looking for motivated PhD students and postdoctoral researchers with interests in information theory, machine learning, quantum computing, or wireless systems. If you are interested, please get in touch with a CV and a brief description of your research interests.
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