Integrating Large-Scale AI Models with Statistical Genetics and Genomics - H. Zhao (Yale)

PerturbPlan: An analytical framework for designing Perturbseq experiments - Eugene Katsevich (UPenn)

Alignment in Large Language Models: Statistical and Game-Theoretic Perspectives - Weijie Su (UPenn)

Cautious extrapolation of causal predictions from past experimentation - R. Silva (College London)

Robust Bayesian Learning - Elvezio Ronchetti (University of Geneva)

Understanding Partial Transfer in CNNs viaKronecker Product Regression - Junhui Wang (C.U. HongKong)

Distribution-Informed Learning via Kernelized Stein Discrepancy Calibration - T. Zheng (Columbia U.)

Towards trustworthy deep learning for inverse problems - B. Adcock (SFU)

Case Studies in AI Uncertainty Quantification and Control - Edgar Dobriban (U.Penn)

Beyond Calibration: Quantifying Uncertainty via Downstream Decision Making - Geoff Pleiss (UBC)

Global Convergence of Adjoint-Optimized Neural PDEs - Konstantinos Spiliopoulos (Boston University)

Double Machine Learning for the Treatment of Nonresponse in Survey Sampling - M. Dagdoug (McGill U.)

Confidence Sets for Causal Orderings - Mladen Kolar (USC)

Toward Bayes-Optimal Multimodal In-Context Learning via Cross-Attention Architectures - P. Sur

Representation-Based Diffusion Models - Jian Huang

Prediction-Powered Conditional Inference - Xiaowu Dai (UCLA)

A geometric perspective on adversarial machine learning - Rachel Morris (Concordia University)

BabyTransformer CODL learns latent domains - Xin Bing (University of Toronto)

Generalization Beyond Observations: A Distributional Perspective - Xinwei Shen (UW Seattle)

Statistical and causal perspectives on machine learning - Erica Moodie (McGill University)

PerturbPlan: An analytical framework for designing Perturb-seq experiments - Eugene Katsevich -UPenn

Integrating Large-Scale AI Models - H. Zhao (Yale)

DémultiplIA Program: MediaTonik Testimonial

Quantifying and limiting false positives in AI-driven therapeutic discover - Jin Ying (UPenn)

DémultiplIA Program: Azimut Lab Testimonial

Proposed Code of Conduct for the Use of AI by Québec Political Parties

DémultiplIA Program | 2026 Cohort Testimonials

Quantum neuromorphic computing with superconducting circuits | Danijela Markovic (CNRS / Thales)

Experimental Progress in Scaling Surface Code and Autonomous Cali | Alexis Morvan, Google Quantum AI

William Fefferman, University of Chicago — Have We Seen a Demonstration of Experimental Quantum Adva

Atelier de résolution de problèmes industriels (ARPI)

Safety vs. AI Welfare: How Personal Identity and Cognition Bridge the Gap | Jonathan Simon (UdeM)

Beyond the "View from Nowhere": Consciousness as a Relational and Functional Capacity | P. Beaudoin

From Single Models to Multi-Agent Systems and Their Risks | Nouha Dziri (Cohere)

From Predictive Pattern Completion to Social and Cultural Norms | Joel Leibo (Google DeepMind)

Feedback Loop Dynamics in Collective Reasoning under Algorithmic Mediation | Kristina Lerman

The Future of Benchmarking: How Social Structures Shape Scientific Evaluation | Bernard Koch

In-Context Processing and Occam’s Razor: Learning the Best Models by Thinking on the Fly | G. Lajoie

Human and AI Solution Paths in Formalizing Expert Mathematics | Balint Gyevnar (Carnegie Mellon U.)

The Social Scaling of AI | Blaise Aguera y Arcos (Google)

Reasoning Models Generate Societies of Thought | James Evans (University of Chicago)

Tandem Training: a Reinforcement-Learning Framework for Social Agents | Ashton Anderson (U. Toronto)

In Search of Diversity: the Values Behind our RL Benchmarks | Gauthier Gidel (Université Montréal)

LLMs as simulacra of human social reasoning | Cameron Jones (Stony Brook University)

Simulating Emergent Behavior on Social Media | Chris Bail (Duke University)

Identifying the Atoms of Meaning in Biological and Artificial Visual Processing | DavidKlint

When Can We Predict Model Behavior | Naomi Saphra (Harvard University)

Mechanistic Principles of Concept Learning and Compositional Reasoning | Hidenori Tanaka (Harvard)

Uncovering Mesa-Optimization Algorithms in Transformers & Building | N. Scherrer

Connecting Perspectives on Dynamical Phenomena in Transformers | Stephanie C Y Chan (DeepMind)

Associative and Episodic Memory from Pre-Structured Spatial Scaffolds in the Hippocampus | Ila Fiete

Interpreting the Relation Between Visual and Linguistic Representations in a VLM | Benno Krojer

Fault tolerant quantum computation in the 21st century | Daniel Gottesman (University of Maryland)

Causal Representation Learning: A Natural Fit for Mechanistic Interpretability | Dhanya Sridhar

Deep Test-Time Thinking with Recursive Self-Aggregation (RSA) | Sarthak Mittal (UdeM/Mila)

The Effects of Data Pruning, Representation Capacity, and Latent Trajectories | Irina Rish (UdeM)

Fluid Reasoning Development: Comparing Mechanisms in Minds and Machine | Claire Stevenson (UvA)

Harnessing Qudits | Barry C Sanders (University of Calgary)

IA et démocratie : comprendre la proposition de code de bonne conduite

Operationalism, Causality, and Quantum Theory a Mostly Time Symm | Lucien Hardy (Perimeter)