Portrait of Jean-Pierre Falet is unavailable

Jean-Pierre Falet

PhD - Université de Montréal
Supervisor
Research Topics
AI Safety
Deep Learning
Learning on Graphs
Probabilistic Models
Reasoning

Publications

A longitudinal study of research productivity in McGill University's MD-PhD and Clinician Investigator Program Graduates from 2000 to 2017
Jamie Magrill
Mimosa Luigi
Isabella Arthur
Paria Asadi
Anna Frumkin
Mark Sorin
Joan Miguel Romero
Julia Luo
Nathalie Johnson
Mark J. Eisenberg
BACKGROUND: Canadian MD-PhD and Clinician Investigator Program (CIP) pathways both train physician-scientists, yet longitudinal data on grad… (see more)uate research productivity are limited. METHODS: We conducted a longitudinal study of McGill University MD-PhD and CIP graduates who completed these programs between 2000 and 2017, using sex- and year-matched MD-only graduates as controls. Outcomes were collected for publications indexed through to December 31, 2022, and included H-index, total publications, total citations, per-paper journal impact factor, per-paper citations, and authorship position (first, second, senior). Productivity was assessed across four time periods: (1) pre-medical/medical training, (2) residency, (3) post-residency/fellowship, and (4) independent practice. RESULTS: Among 549 graduates (MD-PhD = 31; CIP = 97; MD-only = 421), research productivity, research impact, and active research involvement (defined as ≥3 first- or senior-author papers in the prior 5 years) were similar between MD-PhD and CIP graduates; both exceeded MD-only graduates across all metrics. Within-group sex differences were not significant. MD-PhD graduates were more productive during pre-medical/medical training, whereas CIP graduates were comparatively more productive during residency. Sustained productivity in independent practice correlated with research engagement during medical school, residency, and fellowship. In both programs, graduates with active research involvement had higher authorship counts in the 10 years immediately following graduation from medical school. DISCUSSION: Research productivity and impact were comparable between MD-PhD and CIP graduates, and both groups exceeded MD-only peers across measured research metrics. Across both programs, graduates with active research involvement had higher early-career authorship counts, particularly during residency and post-residency/fellowship time periods. These findings provide descriptive benchmarking data for future studies of physician-scientist training pathways in Canada.
Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can cont… (see more)ain inaccurate statements that reduce performance and trustworthiness. To address this, we propose to augment each reasoning step in a CoT with a latent veracity (or correctness) variable. To efficiently explore this expanded space, we introduce Veracity Search (VS), a discrete search algorithm over veracity assignments. It performs otherwise intractable inference in the posterior distribution over latent veracity values by leveraging the LM's joint likelihood over veracity and the final answer as a proxy reward. This efficient inference-time verification method facilitates supervised fine-tuning of an Amortized Veracity Inference (AVI) machine by providing pseudo-labels for veracity. AVI generalizes VS, enabling accurate zero-shot veracity inference in novel contexts. Empirical results demonstrate that VS reliably identifies errors in logical (ProntoQA), mathematical (GSM8K), and commonsense (CommonsenseQA) reasoning benchmarks, with AVI achieving comparable zero-shot accuracy. Finally, we demonstrate the utility of latent veracity inference for providing feedback during self-correction and self-improvement.
Delta-AI: Local Objectives for Amortized Inference in Sparse Graphical Models
We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call …