Talks

I am an experienced public speaker, having given over 40 talks & presentations at top conferences, universities, and companies, such as AAAI, QIP, TQC, University of Oxford, Imperial College London, Perimeter Institute for Theoretical Physics, The Institute for Health Metrics and Evaluation (IHME), and psHealth, among others.

Causal Learning and reasoning

Video: link, January 2023

In this talk I will present two recent causal inference projects done with my collaborators. One of which is concerned about the ability to disentangle the effect of multiple treatments in the presence of hidden confounders. The other is about how one can learn and reason with counterfactual distributions. In both cases I will strive to motivate and contextualise the results with real word examples. Along the way I'll discuss the distinction between learning and inference in the context of a causal model.

Causal Inference in Healthcare

Video: link, February 2020.

Abstract: Causal reasoning is vital for effective reasoning in science and medicine. In medical diagnosis, for example, a doctor aims to explain a patient’s symptoms by determining the diseases causing them. This is because causal relations - unlike correlations - allow one to reason about the consequences of possible treatments. However, all previous approaches to machine-learning assisted diagnosis, including deep learning and model-based Bayesian approaches, learn by association and do not distinguish correlation from causation. I will show that these approaches systematically lead to incorrect diagnoses. I will outline a new diagnostic algorithm, based on counterfactual inference, which captures the causal aspect of diagnosis overlooked by previous approaches and overcomes these issues. I will additionally describe recent algorithms from my group which can discover causal relations from uncontrolled observational data and show how these can be applied to facilitate effective reasoning in medical settings such as treatment decisions.

Higher-order Interference Doesn’t Help in Finding a Needle in a Haystack

Video: link, August 2016.

Abstract: Grover's algorithm constitutes the optimal quantum solution to the search problem and provides a quadratic speed-up over all possible classical search algorithms. Quantum interference between computational paths has been posited as a key resource behind this computational speed-up. However there is a limit to this interference, at most pairs of paths can ever interact in a fundamental way. Could more interference imply more computational power? Sorkin has defined a hierarchy of possible interference behaviours—currently under experimental investigation—where classical theory is at the first level of the hierarchy and quantum theory belongs to the second. Informally, the order in the hierarchy corresponds to the number of paths that have an irreducible interaction in a multi-slit experiment. In this work, we consider how Grover's speed-up depends on the order of interference in a theory. Surprisingly, we show that the quadratic lower bound holds regardless of the order of interference. Thus, at least from the point of view of the search problem, post-quantum interference does not imply a computational speed-up over quantum theory.