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I am interested in applications of probabilistic machine learning to problems in contemporary sciences like computational biology, high energy physics and astronomy. Uncertainty quantification in predictions is becoming increasingly mainstream as several applications in science and industry require statistical guarantees in their predictions. Bayesian non-parametrics is a leading paradigm that allows the user to stipulate a prediction in terms of a probability distribution and allows automatic calibration of model complexity. My Ph.D. is funded by the Alan Turing Institute and Qualcomm Innovation Fellowship (Europe) which I was awarded in 2020.
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INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 151 (2022)
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arxiv(2020)
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