Portrait of Divya Sharma

Divya Sharma

divya.sharma@mila.quebec
Postdoctorate - Université de Montréal
Supervisor
Yoshua Bengio
Co-supervisor
Alex Hernández-García
Research Topics
Deep Learning
Generative Models
Graph Neural Networks
Molecular Modeling
Website
GitHub

Publications

Enhancing Psychiatry Training Using an Agentic AI Simulated Consultation Tool: Prospective Cohort Study
Alice Rueda
Huda F Al-Shamali
Zack Cote
Niloy Roy
Reinhard Janssen‐Aguilar
Jithin Joseph
Bazen Gashaw Teferra
Mohammad Amin Kamaleddin
Lisa Burback
Olga Winkler
Bill Kapralos
Andrei Torres
Divya Sharma
Sridhar Krishnan
Guillaume Dumas
Andrew J. Greenshaw
Alexandre Hudon
Sanjeev Sockalingam
Yanbo Zhang
Adam Dubrowski … (see 1 more)
Venkat Bhat
Background: Canadian psychiatry residents must demonstrate consultation competency, assessed using the standardized assessment of a clinical… (see more) encounter report (STACER). However, opportunities to practice these skills and receive constructive assessment remain limited in clinical settings. Objective: This study aimed to evaluate the technical feasibility of an agentic AI system designed to support psychiatry residents' consultation competence through simulated patient encounters with a patient agent and structured feedback from a rater agent. Methods: We conducted a two-phase technical feasibility prospective single-arm cohort study of the STACER Agentic System, a large language model-based platform integrating a patient agent and a rater agent. Phase 1 involved automated evaluation of the patient agent using a psychiatrist agent across 227 synthetic major depressive disorder cases. Performance was assessed using DeepEval metrics (correctness, clarity, medical faithfulness, turn relevance, and role adherence) with descriptive statistics and 95% CIs. Phase 2 involved a preliminary user study with 14 convenience-sampled participants: a total of 5 members of the clinical research team and 9 psychiatry residents from the University of Alberta. Participants completed simulated diagnostic interviews and case presentations. Performance was evaluated using STACER-based scoring by the rater agent and 2 psychiatrists. Interrater reliability was assessed using intraclass correlation coefficients (α=.05). Participants rated realism, behavioral consistency, psychiatric nuance, and feedback utility using Likert scales and free-text answers. Results: The patient agent demonstrated high behavioral (51/56, 91.07%) and symptom fidelity (105/110, 95.45%), with strong automated performance (medical faithfulness mean 0.99, 95% CI 0.99-1.00; turn relevance 0.99, 95% CI 0.986-0.992). Participants rated simulations as psychiatrically plausible and diagnostically useful, particularly for depressive symptom representation, although rapport building was moderate (mean 2.78, SD 1.56 to mean 3.00, SD 1.41, out of 5.00) due to limited nonverbal cues. The rater agent generated structured STACER-aligned feedback with high intrarater consistency, especially at the section subtotal level. Interrater reliability with psychiatrists was poor at the item level (intraclass correlation coefficient range=0.25-0.49) but improved to good-to-excellent agreement at the section level for psychiatry resident sessions (intraclass correlation coefficient range=0.89-0.93). The rater agent's scores fell between those of the 2 psychiatrists for the clinical research team and were lower than both human raters for psychiatry residents. Conclusions: The STACER Agentic System demonstrates the technical feasibility of using agentic AI to simulate psychiatric consultations and deliver STACER-aligned formative feedback. By combining adaptive multiturn psychiatric simulation with competency-based evaluation, it shows promise in supporting cognitive aspects of consultation, though it remains limited in facilitating relational skills such as rapport building. These findings suggest agentic AI could expand scalable, low-risk opportunities for deliberate practice and formative feedback in competency-based psychiatric education. Further controlled studies are needed to evaluate educational effectiveness and integration into residency training.
2026-07-20
JMIR Medical Education (published)
doi.org
OBELiX: a curated dataset of crystal structures and experimentally measured ionic conductivities for lithium solid-state electrolytes
Félix Therrien
Jamal Abou Haibeh
Divya Sharma
Rhiannon Hendley
Leah Wairimu Mungai
Sun Sun
Alain Tchagang
Jiang Su
Samuel Huberman
Yoshua Bengio
Hongyu Guo
Alex Hernández-García
Homin Shin
OBELiX is a database of 599 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities … (see more)gathered from literature and curated by domain experts.
2025-12-31
Digital Discovery (published)
doi.org
openreview.net
Adsorption energies are necessary but not sufficient to identify good catalysts
Shahana Chatterjee
Alexander Davis
Lena Podina
Divya Sharma
Yoshua Bengio
Alexandre AGM Duval
Oleksandr Voznyy
Alex Hern'andez-Garcia
David Rolnick
Félix Therrien
2025-12-04
ArXiv (preprint)
arxiv.org
arxiv.org
Crystal-GFN: sampling materials with desirable properties and constraints
Mistal
Alex Hernández-García
Alexandra Volokhova
Alexandre AGM Duval
Yoshua Bengio
Divya Sharma
Pierre Luc Carrier
Michał Koziarski
Victor Schmidt
2023-10-26
NeurIPS.cc/2023/Workshop/AI4Mat (spotlight)
openreview.net
openreview.net
Crystal-GFN: sampling crystals with desirable properties and constraints
Mila AI4Science
Alex Hernández-García
Alexandre Duval
Alexandra Volokhova
Yoshua Bengio
Divya Sharma
Pierre Luc Carrier
Yasmine Benabed
Michał Koziarski
Victor Schmidt
Gian-Marco Rignanese
Pierre-Paul De Breuck
Paulette Clancy
Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such … (see more)as electrocatalysts, super-ionic conductors or photovoltaic materials can have a crucial impact, for instance, in improving the efficiency of renewable energy production and storage. In this paper, we introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials, namely the space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physical and structural hard constraints, as well as the use of any available predictive model of a desired physicochemical property as an objective function. To design stable materials, one must target the candidates with the lowest formation energy. Here, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.
2023-10-06
arXiv (preprint)
doi.org
arxiv.org