Victor Livernoche | Ph.D. Student at Mila
I’m Victor Livernoche, a Montreal-born Ph.D. student at McGill University and Mila, supervised by Prof. Reihaneh Rabbany. Outside of research, I enjoy working out, playing sports, and making music. Academically, my work centers on generative modeling, anomaly and deepfake detection, and temporal graph learning. I’m especially interested in how large-scale generative systems can be used responsibly, and how we can design models and datasets that make AI more trustworthy and socially impactful.
About Me
Education
Ph.D., Computer Science
Machine learning research supervised by Prof. Reihaneh Rabbany.
M.Sc. (Thesis), Computer Science
Machine learning research supervised by Prof. Siamak Ravanbakhsh.
B.Sc., Honours Computer Science (Physics minor)
Experience
Research Scientist Student
Focused on diffusion models and anomaly detection; developed a new anomaly detection method based on diffusion models. Applied models to galactic star anomalies. Member of Mila’s Mental Health Committee.
Research Intern
Parametrized the BabyAI reinforcement learning environment in Prof. Yoshua Bengio’s group.
Undergraduate Research Assistant
Analyzed data compaction methods in large databases (with Prof. Oana Balmau).
Research Intern
Supported research operations (admin tasks, simulations, funding processes, partner communications) with Prof. Pierre‑Majorique Léger.
Research Interests
- Generative modeling for images and multimodal generation
- Energy‑based generative models (theory and applications)
- Deepfake detection against misinformation
- Temporal graph representation learning
- Anomaly detection
Skills
Publications
Deepfakes in the 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics
Concerns about AI-generated political content are growing, yet there is limited empirical evidence on how deepfakes actually appear and circulate across social platforms during major events in democratic countries. In this study, we present one of the first in-depth analyses of how these realistic synthetic media shape the political landscape online, focusing specifically on the 2025 Canadian federal election. By analyzing 187,778 posts from X, Bluesky, and Reddit with a high-accuracy detection framework trained on a diverse set of modern generative models, we find that 5.86% of election-related images were deepfakes. Right-leaning accounts shared them more frequently, with 8.66% of their posted images flagged compared to 4.42% for left-leaning users, often with defamatory or conspiratorial intent. Yet, most detected deepfakes were benign or non-political, and harmful ones drew little attention, accounting for only 0.12% of all views on X. Overall, deepfakes were present in the election conversation, but their reach was modest, and realistic fabricated images, although less common, drew higher engagement, highlighting growing concerns about their potential misuse.
OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection
Large-scale dataset and crowdsourced platform for deepfake detection; coauthors include Arodi, Musulan, Yang, Salvail‑Berard, Marceau Caron, Godbout, and Rabbany.
On Diffusion Modeling for Anomaly Detection
Explores diffusion-model-based anomaly detection, showing strong performance with efficient scoring strategies.
A Reproduction of Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification
Reproduction study evaluating Automatic Multi‑Label Prompting for few-shot classification.
Other Projects
Neural Network from Scratch
A Jupyter notebook implementing a neural network from scratch using NumPy.
Upcoming
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Get In Touch
I'm always interested in discussing research opportunities, collaborations, or innovative projects in temporal graphs and machine learning.