Projects
Higher-Order Spatiotemporal Correlations
- What’s Inside Your Diffusion Model? A Score-Based Riemannian Metric to Explore the Data Manifold - arXiv 2025
- Introduced a mathematically rigorous framework for analyzing the geometry of visual representations using metrics derived from the score function in diffusion models
- Exploited the Riemannian geometry of the learned image manifold to provide tools for understanding meaningful transformations in visual space
- Decomposing stimulus-specific sensory neural information via diffusion models - Spotlight (top 3%) at NeurIPS 2025
- Established an axiomatic framework for pixel-wise and feature-wise information decomposition using score functions from diffusion models
- A multi-scale information geometry reveals the structure of mutual information in neural populations - Submitted to NeurIPS 2026
- Derived a unique metric whose mean trace equals the mutual information carried by a population code, emerging from a few coarse-graining axioms
- Showed the metric clusters representations by brain area (nearly blind to the encoder) and recovers synergy that Fisher information misses
Equivariant Architectures for Visual Processing
- Scale-Equivariant Networks for Neural Response Prediction - Poster, Dynamic Scale Equivariance in Retinal Neural Codes Supports Object Tracking, CoSyNe 2026
- Adapted Scale-Equivariant Steerable Networks for neural response prediction to naturalistic video stimuli
- Achieved equivalent or superior performance using only 16k parameters, compared to conventional CNNs requiring over 100k parameters (84% reduction)
- Showed scale equivariance is built into the retinal code in a cell-type-specific way (OFF-alpha ganglion cells)
- Velocity-Equivariant Video Understanding - In Progress
- Developing a unified spatiotemporal equivariance framework that maintains parameter efficiency while enhancing performance on video understanding tasks
- Validating preliminary results on dynamic stimuli
Spatiotemporal Gaussian Processes for Predicting Retinal Neural Responses
- Scalable gaussian process inference of neural responses to movies Accepted Poster at CoSyNe 2024 - Paper in Progress
- Developed a scalable Gaussian process framework for modeling neural responses to naturalistic movie stimuli
- Achieved results comparable with SOTA CNN models
Functional Cell-typing in large-scale electrophysiological recordings
- Towards end-to-end cell-typing in large-scale electrophysiological recordings Accepted Poster at CoSyNe 2024 - Paper in Progress
- Created an end-to-end framework for automated identification of cell types in large-scale neural recordings
- Leveraged functional response properties to classify different neuronal cell types
- Estimating the Surround of Ganglion Cells in Large-Scale Recordings. see Bernstein 2023 Poster
- Characterized center-surround receptive field properties in retinal ganglion cells
- Developed methods for accurate estimation in large-scale multi-electrode array recordings
Visual detection of elementary image features during natural behaviour
- Scene Structure Predicts Perceptual Decisions in Naturalistic Detection Tasks (Yang, Vercillo, Cutrona, Azeglio, Iannetti, Neri) - bioRxiv 2026 (presented at ECVP 2025)
- Work led by Jun Yang at ENS, Paris. The project involved a VR experiment and training different DNNs; my contribution is on the deep-learning part.
- Used deep neural networks to predict human perceptual decisions from trial-by-trial judgments, demonstrating that both local feature analysis and global scene context contribute equally to visual decision-making in naturalistic environments
