I completed my PhD at University of Edinburgh supervised by Dr. Matthias Hennig. I am now a postdoctoral researcher at the Zuckerman Institute at Columbia University, working with Dr. Liam Paninski. I am a member of the International Brain Laboratory (IBL) and the NSF AI Institute for Artificial and Natural Intelligence (ARNI).

I build scalable machine learning models that connect neural activity to behavior at single-cell and single-spike resolution. My work spans multimodal transformers, contrastive/self-supervised learning, and probabilistic latent-variable/state-space modeling to advance large-scale electrophysiology and brain-wide decoding. I also co-created SpikeInterface, a widely-used open-source framework for creating flexible and robust spike sorting pipelines.

Recent News

• I co-organized the workshop Building a foundation model for the brain at Cosyne 2025 in Mont Tremblant, Canada.
• Our work Neural Encoding and Decoding at Scale was accepted at ICML 2025 as a spotlight.
• Our work In vivo cell-type and brain region classification via multimodal contrastive learning was accepted at ICLR 2025 as a spotlight.
• Our work Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution was accepted at NeurIPS 2024.
• Our work Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools was published in Nature Methods.
• Our work Towards robust and generalizable representations of extracellular data using contrastive learning was accepted at NeurIPS 2023.
• Our work Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes was accepted at NeurIPS 2023 as a spotlight paper.