Research
I study intelligence by combining neuroscience, machine learning, and ethology. My work follows a deliberate arc: understand natural social cognition with quantitative rigor, build measurement tools that bridge biological and artificial systems, then use those tools to explain how brains compute social meaning – and what that might teach us about building AI that understands the social world.
The Primate Brain During Natural Social Behavior
Testard & Tremblay et al., 2024 · Nature
Quantitative analysis of neural population activity during naturalistic primate social behavior.
This work established a foundation for studying primate social cognition under naturalistic conditions, combining ethological annotation, computer vision, and wireless neural recording in unrestrained dyads.
I led the behavioral quantification pipeline, producing standardized behavioral event streams (labeling/QC and time alignment) used for downstream single-neuron and population analyses.
PrimateFace: Resource for Cross-Species Face Analysis
Parodi et al., 2025 · bioRxiv
A cross-species primate face dataset and benchmark for measuring generalization in facial analysis models.
To connect animal behavior to modern CV, I built PrimateFace as an evaluation substrate: a curated dataset spanning multiple primate species, with standardized training/evaluation infrastructure and systematic comparisons across model families (CNNs, transformers, vision-language models).
PrimateFace is genus-balanced to make cross-species generalization measurable rather than incidental. In practice it supports (i) individual identification/verification for longitudinal behavioral studies, (ii) robust face embeddings for tracking and re-identification, and (iii) pretraining/evaluation for downstream models used in primate video analysis.
Neural Basis of Social Intelligence
PhD Thesis · mid-STS
Integrating deep behavioral modeling with wireless neural recordings to study social intelligence.
My thesis brings these threads together: I use deep learning to extract behavioral representations (pose, kinematics, interaction structure, behavioral syllables) to bridge complex social behavior and neural activity in macaque mid-superior temporal sulcus (STS).
These representations let me test mechanistic hypotheses with single-unit and population methods, including dimensionality reduction, time-resolved analyses, and RL-based modeling.
All Publications
- Grounding Intelligence in Movement
- PrimateFace: A Machine Learning Resource for Automated Face Analysis in Human and Non-human Primates
- Primate neuroethology: a new synthesis
- Neural signatures of natural behaviour in socializing macaques
- Attention deficits linked with proclivity to explore while foraging
- Computational Kinematics of Dance: Distinguishing Hip Hop Genres
- PrimateFace: Large-scale resource for cross-species primate facial analysis
- Quantifying cross-species primate facial cues
- Vision-language models for decoding provider attention during neonatal resuscitation
- A large language model-assisted education tool to provide feedback on open-ended responses
- Information gathering explains decision dynamics during human and monkey reward foraging
- Quantifying grooming in paired macaques
- PrimateFace: A Resource for Generalizable Cross-Species Facial Analysis