Portrait of Nolan Smyth

Nolan Smyth

nolan.smyth@mila.quebec
Postdoctorate - Université de Montréal
Supervisor
Yashar Hezaveh
Co-supervisor
Laurence Perreault-Levasseur
Research Topics
AI Safety
Applied Machine Learning
Astrophysics
Website
Google Scholar
Bluesky
GitHub

Publications

Efficient Safety Alignment of Language Models via Latent Personality Traits
Mohamed Amine Merzouk
Nolan Smyth
Damiano Fornasiere
Linh Le
David Williams-King
Adam Oberman
Current safety methods for large language models are known to be vulnerable to adversarial attacks, motivating research into robust alternat… (see more)ives. Latent Adversarial Training (LAT) is among the most effective defenses, but can degrade utility and requires training on large datasets of harmful prompts. We introduce Latent Personality Alignment (LPA), which replaces explicit harm refusal with adversarial training on just 66 harm-agnostic statements drawn from psychometric personality literature. We hypothesize that personality-anchored representations share latent structure with harm avoidance, so adversarially stabilizing them implicitly constrains the subspace exploited by jailbreak attacks. LPA achieves near-zero attack success rates on HarmBench across direct requests and five jailbreak methods, despite never seeing harmful content during training and no loss of performance on standard benchmarks. Moreover, the training process is lightweight; the entire procedure completes in minutes on a single GPU and uses 75x fewer examples than standard LAT. Extensive ablations demonstrate the robustness, efficiency, and generalization of our method.
2026-07-07
Conference on Language Modeling (accepted)
doi.org
openreview.net
Microlensing Detection and Inference via Learned Bayes Factors
Nolan Smyth
Laurence Perreault-Levasseur
Yashar Hezaveh
We present a unified framework for gravitational microlensing event detection and parameter inference. Traditional pipelines use determinist… (see more)ic hard cuts on photometric statistics, systematically missing low-magnification events in the finite-source regime. We instead frame detection as Bayesian model comparison using Evidence Networks, which learn calibrated Bayes factors from binary-labeled simulations, and combine this with Neural Posterior Estimation (NPE) for amortized parameter inference. Both share a transformer encoder that handles irregularly-sampled time series without imputation. On simulated Roman Space Telescope data, our Evidence Network achieves
2026-05-29
AI4Physics @ International Conference on Machine Learning (published)
openreview.net
openreview.net
Transformer Embeddings for Fast Microlensing Inference
Nolan Smyth
Laurence Perreault-Levasseur
Yashar Hezaveh
2025-12-11
ArXiv (preprint)
arxiv.org
arxiv.org
Neural Deprojection of Galaxy Stellar Mass Profiles
M. J. Yantovski-Barth
Hengyue Zhang
Nolan Smyth
Connor Stone
Martin Bureau
Yashar Hezaveh
Laurence Perreault-Levasseur
We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable… (see more) mapping. Specifically, we train a neural network to translate Nuker profile parameters into analytically deprojectable Multi Gaussian Expansion components, enabling physically realistic stellar mass models without requiring optical observations. We integrate this model into SuperMAGE, a differentiable dynamical modelling pipeline for Bayesian inference of supermassive black hole masses. Applied to ALMA data, our approach finds results consistent with state-of-the-art models while extending applicability to dust-obscured and active galaxies where optical data analysis is challenging.
2025-11-24
arXiv (preprint)
doi.org
arxiv.org
Searching for Free-floating Planets with TESS: Results from Sectors 61–65
Michelle Kunimoto
William DeRocco
Nolan Smyth
Steve Bryson
B. Scott Gaudi
Abstract Though free-floating planets (FFPs) may outpopulate their bound counterparts in the terrestrial-mass range, they remain one of the … (see more)least explored exoplanet demographics. Due to their negligible electromagnetic emission at all wavelengths, the only observational technique able to detect these worlds is gravitational microlensing. Microlensing by terrestrial-mass FFPs induces rare, short-duration magnifications of background stars, requiring high-cadence, wide-field surveys to detect these events. The Transiting Exoplanet Survey Satellite (TESS), though designed to detect close-bound exoplanets via transits, boasts a full-frame image cadence as short as 200 s and has monitored hundreds of millions of stars, providing a unique dataset in which to search for rare short-duration transients. We have performed a preliminary search for FFP microlensing in 7.5 million light curves from TESS Sectors 61–65. We find one short-duration event with a light-curve morphology consistent with expectations for a low-mass FFP, but in tension with the expected FFP abundance in this mass range. We consider possible false-positive interpretations of this event such as stellar flares, heartbeat binaries, and centrifugal breakout. We find that all interpretations pose some challenges, and we discuss the possibility that the event may constitute a first example of a new class of pernicious false positives that future space-based microlensing efforts will encounter. Our ongoing search through the TESS dataset will significantly support the upcoming hunt for rogue worlds with dedicated space-based microlensing surveys, and our results may be used alongside these surveys to place interesting constraints on the spatial distribution of FFPs in the Galaxy.
2025-11-10
Astronomical Journal (published)
doi.org