Portrait of Prakash Panangaden

Prakash Panangaden

Core Academic Member
Full Professor, McGill University, School of Computer Science


Panangaden has been at McGill University since 1990, where for the past twenty-five years he has been working on various aspects of Markov processes: process equivalence, logical characterization, approximation and metrics. Recently he has worked on using metrics to enhance representation learning.

Panangaden first studied physics at the Indian Institute of Technology in Kanpur. For his MSc in physics at the University of Chicago, he studied stimulated emission from black holes, and for his PhD in physics at the University of Wisconsin–Milwaukee, he worked on quantum field theory in curved spacetime.

He was formerly an assistant professor of computer science at Cornell University, where he primarily worked on the semantics of concurrent programming languages.

A Fellow of the Royal Society of Canada and Fellow of the Association for Computing Machinery (ACM), Panangaden has published papers in physics, quantum information and pure mathematics.

Current Students

Master's Research - McGill University
Co-supervisor :


Conditions on Preference Relations that Guarantee the Existence of Optimal Policies
Jonathan Colaco Carr
Polynomial Lawvere Logic
Giorgio Bacci
Radu Mardare
Gordon D. Plotkin
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Sahand Rezaei-Shoshtari
Rosie Zhao
Behavioural pseudometrics for continuous-time diffusions
Linan Chen
Florence Clerc
Propositional Logics for the Lawvere Quantale
Giorgio Bacci
Radu Mardare
Gordon Plotkin
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes
Tyler Kastner
Mark Rowland
We present a novel perspective on behavioural metrics for Markov decision processes via the use of positive definite kernels. We define a ne… (see more)w metric under this lens that is provably equivalent to the recently introduced MICo distance (Castro et al., 2021). The kernel perspective enables us to provide new theoretical results, including value-function bounds and low-distortion finite-dimensional Euclidean embeddings, which are crucial when using behavioural metrics for reinforcement learning representations. We complement our theory with strong empirical results that demonstrate the effectiveness of these methods in practice.
Optimal Approximate Minimization of One-Letter Weighted Finite Automata
Clara Lacroce
Borja Balle
Behavioural equivalences for continuous-time Markov processes
Linan Chen
Florence Clerc
Augmenting Human Selves Through Artificial Agents – Lessons From the Brain
Georg Northoff
Maia Fraser
John Griffiths
Dimitris A. Pinotsis
Rosalyn Moran
Karl Friston
Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for f… (see more)unctional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI – but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called “strong AI” or “artificial consciousness.” In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the “self,” which we define as the “environment-agent nexus”; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD – in the implementation of artificial agents – offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA’s) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.
Towards an AAK Theory Approach to Approximate Minimization in the Multi-Letter Case
We study the approximate minimization problem of weighted finite automata (WFAs): given a WFA, we want to compute its optimal approximation … (see more)when restricted to a given size. We reformulate the problem as a rank-minimization task in the spectral norm, and propose a framework to apply Adamyan-Arov-Krein (AAK) theory to the approximation problem. This approach has already been successfully applied to the case of WFAs and language modelling black boxes over one-letter alphabets \citep{AAK-WFA,AAK-RNN}. Extending the result to multi-letter alphabets requires solving the following two steps. First, we need to reformulate the approximation problem in terms of noncommutative Hankel operators and noncommutative functions, in order to apply results from multivariable operator theory. Secondly, to obtain the optimal approximation we need a version of noncommutative AAK theory that is constructive. In this paper, we successfully tackle the first step, while the second challenge remains open.
Bisimulation metrics and norms for real-weighted automata
Borja Balle
Pascale Gourdeau
Extracting Weighted Automata for Approximate Minimization in Language Modelling