Maximilian Puelma Touzel

Research Associate at Mila, Québec AI Institute/Université of Montréal
Member of Centre for the Study of Democratic Citizenship (CSDC)

statistical inference & decision-making in human & machine learning
Applications to socio-{political, economic, technical, environmental} dilemmas

PhD, Physics (IMPRS Physics of biological and complex systems), University of Göttingen
MSc, Physics, University of Toronto
BSc, Physics & Mathematics, University of Toronto

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News:

Oct 2024	Happy to join the Complex Data Lab.
Sept 2024	Happy to join the Centre for the Study of Democratic Citizenship as a research member.
Sept 2024	Very happy on this first of a series: "A Simulation System Towards Solving Societal-Scale Manipulation" submitted to NeurIPs Socially Responsible Language Modelling Research (SOLAR)workshop! Here is the preprint.
Aug 2024	"Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada" submitted! Here is the preprint.
July 2024	ClimateMatch running again this year. Continuing as Day Lead for the socioeconomic curriculum, we've revamped the curriculum for the day. We leaned a fair bit on the Enroads simulator. Try it out.
May 2024	Workshop paper on Scalable approaches to the theory of many minds problem accepted to the Agentic Markets Workshop at ICML.
Apr 2024	Carbon tax topic modelling work published in Environmental Data Science.
Nov 2023	Gave a talk on using topic models on survey responses to infer ideology underpinning carbon tax opposition. This was at the CCAI workshop AAAI on Artificial Intelligence and Climate: The Role of AI in a Climate-Smart Sustainable Future at the AAAI Fall symposium in Washington, DC.
Aug/Sept 2023	Gave a 2-part talk on Transition Narratives and complex coordination problems at the ClimateMatchAcademy Monthly Seminar. (Part 1; Part 2).
July 2023	Climate Match Academy is alive and running! Delivered a day of socioeconomic curriculum and organized our Discord server of 100s of students from over 100 countries! Check out our always-accessible-from-anywhere Jupyter Book.
June 2023	Presented game theory of social norms work to Joel Leibo's group at Deepmind.
Dec 2022	Presenting topic modelling at NeurIPS Workshop Tackling Climate Change with Machine Learning.
Nov 2022	Giving a talk (open to the public) at University of Washington's data science seminar (recording).
Oct 2022	Attended the IBM/DIMACS Workshop on Bridging Game Theory and Machine Learning for Multi-party Decision Making at Rutgers.
Sept 2022	Giving a talk at the Montreal Computational & Quantitative Linguistics Lab .
Sept 2022	Presenting carbon tax work for the first time at the Montreal AI Symposium!
Sept 2022	Meriem's NoisET paper got accepted in Physical Chemistry A!
Sept 2022	Our mixing times paper was accepted to NeurIPS!
June 2022	RLDM2022 was great! Presented urgency work and ran our workshop on social alignment in human and machines. Workshop recording available on workshop website.
May 2022	Urgency work published in PLoS Computational Biology.
Apr. 2022	Presented our agent abstraction paper at ICLR workshop: From cells to societies: learning across scales.
Mar. 2022	Gave a couple guest lectures in graduate-level math course on dynamical systems at UdeM. So nice to have the time in a talk to spell things out and discuss with students!
Feb. 2022	Lyapunov spectra for RNNs paper accepted to Frontiers in Applied Mathematics & Statistics
Jan. 2022	2 accepted submissions to COSYNE: the neurodata validation of our decision-making model; and new work on noise robustness in recurrent neural nets with Colin Bredenberg.
Jan. 2022	Gave a talk on computing with transients at the Banff workshop on Dynamical Principles of Biological and Artificial Neural Networks.
Dec. 2021	Presented polynomial mixing times work at EcoRL workshop at NeurIPS. See the preprint.
Nov. 2021	Participated in Montreal's MAIN neuroAI conference.
Oct. 2021	Presented poster at Montreal's AI Symposium.
Sept. 2021	post on Mila blog post on our NeuroAI reading group.
Aug. 2021	Urgency work out as a preprint! Twitter summary thread here.
May 2021	Gave a talk on Stochastic Thermodynamics of learning to the Physics of Machine learning reading group at Mila.
Jan. 2021	Urgency work peer-reviewed and accepted at COSYNE.
Feb. 2021	Join us for a exciting day of talks and a panel of top experts on the goals and challenges for robust scientific explanations in neural and artificial intelligence systems.
Dec. 2020	Urgency work peer-reviewed and accepted at the Biological and Artificial Reinforcement Learning workshop at NeurIPS.
Dec. 2020	Montreal Artificial Intelligence and Neuroscience 2020 conference.
Nov. 2020	Presented urgency work at the inaugural NeuroAI conference, NAISYS, at Cold Spring Harbor Labs.
May 2020	Happy to lead the breakout session on higher cognition at UNIQUE's inaugural NeuroAI symposium.
Apr. 2020	Check out our preprint on Lyapunov spectra for RNN training.
Apr. 2020	Our work on inferring population dynamics from intrinsically variable, vastly subsampled, and indirectly accessed genetic sequences is published in PLoS Computational Biology.
Mar. 2020	We are presenting our decision-making work in collaboration with the Cisek Lab at COSYNE.
Dec. 2019	We are organizing a NeuroAI workshop at NeurIPS2019.
Dec. 2019	nnRNN paper presented in main track of NeurIPS.
Nov. 2019	With yet another wonderful edition of the Montreal Artificial Intelligence and Neuroscience (MAIN) conference, Montreal is further establishing itself as the hotbed for incisive NeuroAI research. Honoured to have my new postdoc work recognized with two awards here.
May 2019	We are organizing a Physics and AI Workshop in Montreal.
Jan 2019	family_size+=1!

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Summary

I am a computational modeller, theory-builder & data scientist/machine learning researcher interested in

how formed beliefs and incentives shape decision-making at the individual level and
how public opinion and social norms impact social dilemmas and alignment problems.

Powerful technologies have dual-use concerns: they are often good and bad for us. While we often talk about the negative impact of AI-powered technologies, there is the potential in many cases to use this technology to counter this impact, improve public discourse, and help us organize ourselves towards a sustainable future. There is science here, but in service of applications. Dampening social polarization and supporting effective sustainability transition policy addressing the climate emergency are two critical efforts I aim to contribute to.

Towards these ends, I coordinate interdisciplinary collaborations with domain experts in which I also contribute technical mathematical and computational expertise (theory and models/algorithms for decision-making agent learning, statistical inference, data analysis, ...).

My current research pursues three synergistic lines of research (example publication from each line):

Inferring Beliefs: Ideology from topic mixture statistics: Inference method and example application to carbon tax public opinion
Tracking Changes in Beliefs: Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada
Simulating societal-scale belief dynamics: A Simulation System Towards Solving Societal-Scale Manipulation

My approach to these problems stems from my expertise in machine-learning and system modelling, gained from quantitative modelling work in many areas related to intelligent behaviour. Now, however, it's typically social scientists (social psychologists/ sociologists/ political scientists/ economists) who have identified the problems I work on. In addition to pursuing applications in those fields, I aim to have a more indirect, though perhaps larger impact on those fields by contributing to a modernized computational social science grounded in large-scale, quantitative approaches. See Research and Publications sections for more details.