@article{JMLR:v25:23-0680,author={Duval, Alexandre and Schmidt, Victor and Miret, Santiago and Bengio, Yoshua and Hern{{\'a}}ndez-Garc{{\'i}}a, Alex and Rolnick, David},title={PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design},journal={Journal of Machine Learning Research},year={2024},volume={25},number={106},pages={1-26},}
Improving Molecular Modeling with Geometric GNNs: an Empirical Study
Ali Ramlaoui, Théo Saulus, Basile Terver, and
4 more authors
@article{ramlaoui2024improving,title={Improving Molecular Modeling with Geometric GNNs: an Empirical Study},author={Ramlaoui, Ali and Saulus, Théo and Terver, Basile and Schmidt, Victor and Rolnick, David and Malliaros, Fragkiskos D. and Duval, Alexandre},year={2024},journal={arXiv preprint arXiv: 2407.08313},}
2023
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Alexandre Duval*, Victor Schmidt*, Alex Hernandez Garcia, and
4 more authors
@article{duval2023faenet,title={FAENet: Frame Averaging Equivariant GNN for Materials Modeling},dimensions={false},author={Duval*, Alexandre and Schmidt*, Victor and Garcia, Alex Hernandez and Miret, Santiago and Malliaros, Fragkiskos D. and Bengio, Yoshua and Rolnick, David},year={2023},journal={ICML 2023},}
torchgfn: A PyTorch GFlowNet library
Salem Lahlou, Joseph D. Viviano, and Victor Schmidt
@article{lahlou2023torchgfn,title={torchgfn: A PyTorch GFlowNet library},dimensions={false},author={Lahlou, Salem and Viviano, Joseph D. and Schmidt, Victor},year={2023},journal={arXiv preprint arXiv: 2305.14594},}
Proactive Contact Tracing
Prateek Gupta, Tegan Maharaj, Martin Weiss, and
14 more authors
The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting daily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as “pingdemic,” may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users’ infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
@article{10.1371/journal.pdig.0000199,doi={10.1371/journal.pdig.0000199},author={Gupta, Prateek and Maharaj, Tegan and Weiss, Martin and Rahaman, Nasim and Alsdurf, Hannah and Minoyan, Nanor and Harnois-Leblanc, Soren and Merckx, Joanna and Williams, Andrew and Schmidt, Victor and St-Charles, Pierre-Luc and Patel, Akshay and Zhang, Yang and Buckeridge, David L. and Pal, Christopher and Schölkopf, Bernhard and Bengio, Yoshua},journal={PLOS Digital Health},publisher={Public Library of Science},title={Proactive Contact Tracing},year={2023},month=mar,volume={2},url={https://doi.org/10.1371/journal.pdig.0000199},pages={1-19},number={3},}
A Hitchhiker’s Guide to Geometric GNNs for 3D Atomic Systems
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, and
7 more authors
@article{duval2023hitchhikers,title={A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems},author={Duval, Alexandre and Mathis, Simon V. and Joshi, Chaitanya K. and Schmidt, Victor and Miret, Santiago and Malliaros, Fragkiskos D. and Cohen, Taco and Lio, Pietro and Bengio, Yoshua and Bronstein, Michael},year={2023},journal={arXiv preprint arXiv: 2312.07511},}
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions
Alvaro Carbonero, Alexandre Duval, Victor Schmidt, and
4 more authors
@article{carbonero2023importance,title={On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions},author={Carbonero, Alvaro and Duval, Alexandre and Schmidt, Victor and Miret, Santiago and Hernandez-Garcia, Alex and Bengio, Yoshua and Rolnick, David},year={2023},journal={arXiv preprint arXiv: 2310.06682},}
2022
Surface micropatterning for the formation of an in vitro functional endothelial model for cell-based biosensors
Zhor Khadir, Victor Schmidt, Kevin Chabot, and
6 more authors
@article{KHADIR2022114481,title={Surface micropatterning for the formation of an in vitro functional endothelial model for cell-based biosensors},journal={Biosensors and Bioelectronics},pages={114481},year={2022},issn={0956-5663},doi={https://doi.org/10.1016/j.bios.2022.114481},url={https://www.sciencedirect.com/science/article/pii/S0956566322005218},dimensions={false},author={Khadir, Zhor and Schmidt, Victor and Chabot, Kevin and Bryche, Jean-François and Froehlich, Ulrike and Moreau, Julien and Canva, Michael and Charette, Paul and Grandbois, Michel},keywords={Surface micropatterning, Endothelium model, Cell-based biosensing, Cell response, Surface plasmon resonance imaging},}
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Victor Schmidt*, Alexandra Sasha Luccioni*, Mélisande Teng, and
8 more authors
@article{schmidt2021climategan,title={ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods},dimensions={false},author={Schmidt*, Victor and Luccioni*, Alexandra Sasha and Teng, Mélisande and Zhang, Tianyu and Reynaud, Alexia and Raghupathi, Sunand and Cosne, Gautier and Juraver, Adrien and Vardanyan, Vahe and Hernandez-Garcia, Alex and Bengio, Yoshua},year={2022},journal={ICLR},}
2021
Predicting Infectiousness for Proactive Contact Tracing
Yoshua Bengio, Prateek Gupta, Tegan Maharaj, and
20 more authors
@inproceedings{bengio2021predicting,title={Predicting Infectiousness for Proactive Contact Tracing},dimensions={false},author={Bengio, Yoshua and Gupta, Prateek and Maharaj, Tegan and Rahaman, Nasim and Weiss, Martin and Deleu, Tristan and Muller, Eilif Benjamin and Qu, Meng and victor schmidt and St-charles, Pierre-luc and hannah alsdurf and Bilaniuk, Olexa and david buckeridge and gaetan caron and pierre luc carrier and Ghosn, Joumana and satya ortiz gagne and Pal, Christopher and Rish, Irina and Sch{\"o}lkopf, Bernhard and abhinav sharma and Tang, Jian and andrew williams},booktitle={ICLR},year={2021},url={https://openreview.net/forum?id=lVgB2FUbzuQ},}
Using Artificial Intelligence to Visualize the Impacts of Climate Change
Alexandra Luccioni, Victor Schmidt, Vahe Vardanyan, and
1 more author
@article{9325146,journal={IEEE Computer Graphics and Applications},volume={41},pages={8-14},doi={10.1109/MCG.2020.3025425},title={Using Artificial Intelligence to Visualize the Impacts of Climate Change},year={2021},dimensions={false},author={Luccioni, Alexandra and Schmidt, Victor and Vardanyan, Vahe and Bengio, Yoshua},url={https://ieeexplore.ieee.org/document/9325146},}
2020
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing
Prateek Gupta, Tegan Maharaj, Martin Weiss, and
26 more authors
@article{gupta2020coviagentsim,title={COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing},dimensions={false},author={Gupta, Prateek and Maharaj, Tegan and Weiss, Martin and Rahaman, Nasim and Alsdurf, Hannah and Sharma, Abhinav and Minoyan, Nanor and Harnois-Leblanc, Soren and Schmidt, Victor and Charles, Pierre-Luc St. and Deleu, Tristan and Williams, Andrew and Patel, Akshay and Qu, Meng and Bilaniuk, Olexa and Caron, Gaétan Marceau and Carrier, Pierre Luc and Ortiz-Gagné, Satya and Rousseau, Marc-Andre and Buckeridge, David and Ghosn, Joumana and Zhang, Yang and Schölkopf, Bernhard and Tang, Jian and Rish, Irina and Pal, Christopher and Merckx, Joanna and Muller, Eilif B. and Bengio, Yoshua},year={2020},journal={arXiv preprint arXiv: Arxiv-2010.16004},}
Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks
Victor Schmidt, Mustafa Alghali, Kris Sankaran, and
2 more authors
@article{schmidt2020modeling,title={Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks},dimensions={false},author={Schmidt, Victor and Alghali, Mustafa and Sankaran, Kris and Yuan, Tianle and Bengio, Yoshua},year={2020},journal={Climate Change AI workshop, NeurIPS},}
Using Simulated Data to Generate Images of Climate Change
Gautier Cosne, Adrien Juraver, Mélisande Teng, and
4 more authors
Machine Learning in the Real World workshop, ICLR, Mar 2020
@article{cosne2020using,title={Using Simulated Data to Generate Images of Climate Change},dimensions={false},author={Cosne, Gautier and Juraver, Adrien and Teng, Mélisande and Schmidt, Victor and Vardanyan, Vahe and Luccioni, Alexandra and Bengio, Yoshua},year={2020},journal={Machine Learning in the Real World workshop, ICLR},}
2019
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
Victor Schmidt, Alexandra Luccioni, S. Karthik Mukkavilli, and
4 more authors
@article{schmidt2019isualizing,title={Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks},dimensions={false},author={Schmidt, Victor and Luccioni, Alexandra and Mukkavilli, S. Karthik and Balasooriya, Narmada and Sankaran, Kris and Chayes, Jennifer and Bengio, Yoshua},year={2019},journal={AI for Social Good workshop, ICLR},}
Quantifying the Carbon Emissions of Machine Learning
Alexandre Lacoste*, Alexandra Luccioni*, Victor Schmidt*, and
1 more author
@article{lacoste2019quantifying,title={Quantifying the Carbon Emissions of Machine Learning},dimensions={false},author={Lacoste*, Alexandre and Luccioni*, Alexandra and Schmidt*, Victor and Dandres, Thomas},year={2019},journal={Climate Change AI workshop, NeurIPS},}
