Leslie Ann Goldberg
  • Professor Leslie Ann Goldberg

  • Email (CS dept or University of Oxford): head-of-dept@cs.ox.ac.uk
  • Email (my research, my teaching, or St Edmund Hall): leslie.goldberg@seh.ox.ac.uk
  • Teams (within ox.ac.uk) coml0521 (don't try the head-of-dept address for teams, because it doesn't reach me!)
  • Office: 256 Wolfson Building

  • PA: Jayne Bullock academic.support@cs.ox.ac.uk

  • Phone: +44 1865 610755 (forwards to teams, email is a quicker way to reach me)
  • Address: Department of Computer Science, University of Oxford,
    Wolfson Bldg, Parks Rd,
    Oxford OX1 3QD United Kingdom

I am the Head of the Department of Computer Science (and a Professor of Computer Science) at the University of Oxford. I am also a Senior Research Fellow at St Edmund Hall. I co-teach the MSc and Part C course Probability and Computing with Marc Roth.

Faculty hiring in our Department: The deadline has passed for positions that start in Autumn of 2024. We will be hiring again next year, and details will eventually be here.

Prospective PhD students: The deadline has passed for funded DPhil (PhD) places to start in Autumn of 2024. Information amount how to apply (to start in Autumn of 2025) will be here: https://www.ox.ac.uk/admissions/graduate/courses/dphil-computer-science. Prospective students should feel free to get in touch if any of the projects described here sound interesting, or if you have other related ideas in algorithms or complexity theory.

Research Interests

I am interested in foundational questions in Algorithms and Complexity Theory. A primary goal in this area is to figure out which computational problems can be solved with fast algorithms and to discover fast algorithms for solving these problems. The other primary goal is to figure out which problems provably can't be solved with fast algorithms, and to prove that no fast algorithms exist for solving these problems (usually relying on conjectures from the field of complexity theory).

I am especially interested in randomised algorithms, which are algorithms that use probabilistic methods to solve problems. Randomised algorithms arise in a huge variety of computational applications including algorithms for communication and information spread in networks, algorithms for machine learning, and algorithms for analysing computational models from statistical physics. I am particularly interested in the rigorous, mathematical analysis of these algorithms - proving results about how long the algorithms take, and how accurate they are.

My current research projects include

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