I joined Cambridge CBL in October 2022 as a PhD student participating in the Cambridge — Tübingen PhD Fellowships in Machine Learning program and the ELLIS PhD Program. My research focuses on Bayesian machine learning, particularly Gaussian processes, variational inference and neural compression. My academic supervisors are José Miguel Hernández-Lobato (University of Cambridge, Professor) and Bernhard Schölkopf (Max Planck Institute for Intelligent Systems, Director), and my studies and research are funded by the Harding Distinguished Postgraduate Scholars Programme. Before starting my PhD, I received my B.Sc. in Computer Science and M.Sc. in Autonomous Systems from TU Darmstadt in Germany, where I worked with Stefan Roth, Jan Peters and Kristian Kersting and received the Deutschlandstipendium. I also attended the University of British Columbia, Stanford University and Aalto University through student exchange programs and summer sessions, and visited Louisiana State University as part of Fulbright’s Leaders in Entrepreneurship program.

Latest News

• Our paper Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes was accepted at NeurIPS 2024!
• I spent my summer as a Research Scientist Intern at Meta, working on scalable Gaussian processes for learning curve prediction.
• I presented our paper Stochastic Gradient Descent for Gaussian Processes Done Right at ICLR 2024!
• I gave an oral presentation at NeurIPS 2023 about our paper Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent!
• I presented 2 posters and gave 1 contributed talk at AABI 2023, and presented 2 posters at ICML 2023 workshops!

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