Description of Clinical Expertise

- Presurgical evaluation of epilepsy by combining electrophysiology (invasive and non-invasive) and multimodal brain imaging.
- Development of computational biomarkers for individualized therapy.
- Normative atlases to leverage collective experience of large numbers of patients to inform individualized therapy.
- Digital twins to simulate treatment outcomes.
- Data-driven targeting of epileptic networks using large multicenter datasets.
- Personalized prediction of surgical and neurostimulation outcomes.

Description of Other Expertise

- Cloud engineering for neuroscience: containerization, APIs, deployment of reproducible tools.
- Multicenter data harmonization, robust statistics, and effect-size–driven inference.
- Education and mentorship; team-science collaborations across neurology, neurosurgery, radiology, engineering, and biostatistics.
- Program building: packaging methods as scalable apps and cross-site standards to accelerate clinical translation.

Description of Research Expertise

I am a neuroscientist with expertise in engineering, informatics, biostatistics, and neurology, specializing in translational and clinical research. The mission of my research program is to develop translational methods that quantify disease severity, progression, and outcomes; remove barriers to clinical translation; and guide personalized treatments for neurological disorders.

My research focuses on three core areas: (1) discover novel methods to integrate multimodal data, (2) develop scalable tools that generalize across multiple sites, and (3) deploy translational products for rigorous retrospective and prospective validation in clinical trials.

To achieve these goals, I develop methods and analytical approaches to process multimodal neural data at scale with four objectives: (a) to develop better therapies through quantitative biomarkers; (b) to understand the natural history of human disease for patient classification, outcome prediction, and treatment selection; (c) to advance understanding of brain networks by integrating multimodal data to bring new insights into both basic and translational neuroscience; and (d) to build a neural-data ecosystem at Penn and expand this initiative nationally and internationally to foster collaboration, data sharing, and innovation in neuroscience.