Funded Projects

A collaborative project funded by the Simons Foundation Autism Research Initiative (SFARI) to integrate neuroscience data into standardized NWB format and Spyglass database systems. The project focuses on converting complex behavioral and electrophysiology datasets from multiple experiments, enabling better data sharing and analysis across the neuroscience community. This work includes developing robust conversion pipelines that ensure compatibility with both NWB standards and Spyglass analysis frameworks.

Supported by the National Institutes of Health U01 program, we assist researchers studying sensory processing in Drosophila to standardize their data using the NWB format. This project involves five labs (Cohen, Dickerson, Fox, Kim, and Suver) that use diverse experimental approaches including electrophysiology, calcium imaging, and behavioral tracking. By creating standardized data pipelines, this initiative facilitates cross-lab collaboration and integrated analysis of sensory processing in the fly nervous system.

Through funding from NYU Libraries, we assist NYU neuroscience labs in standardizing their data using the NWB format. This project currently supports two labs: Dr. Christine Constantinople's lab, which studies the neural circuits underlying economic decision-making, and Dr. David Schneider's lab, which investigates the neural basis of vocal learning and auditory processing. This collaboration facilitates data sharing and reuse within the NYU research community and beyond, making valuable neuroscience datasets more accessible to researchers worldwide.

Through funding from the Simons Collaboration on Plasticity and the Aging Brain (SCPAB), we assist researchers in standardizing their aging-related neuroscience data using the NWB format. This project facilitates data sharing and collaboration among SCPAB labs, enabling more effective cross-lab analyses of aging-related neural data, as well as utilization by the global neurophysiology community.

Contracted by Dr. Jorge Palop's laboratory at the Gladstone Institute, we are developing VAME (Variational Animal Motion Embedding), a deep learning framework for analyzing animal behavior. This project focuses on creating tools that enable researchers to automatically track and analyze complex behavioral patterns in laboratory animals, facilitating the study of neurological conditions and potential therapeutic interventions.

Led by Luiz Tauffer in collaboration with Dr. Mika Rubinov's laboratory at Vanderbilt University, we are integrating modern software engineering practices with Voluseg, a specialized tool for volumetric segmentation of calcium imaging data. This project focuses on creating advanced algorithms and tools that enable neuroscientists to efficiently process and analyze three-dimensional calcium imaging datasets, facilitating the study of neural activity patterns in complex brain structures.