Brian Litt, M.D., is Professor of Neurology, Neurosurgery and Bioengineering at the University of Pennsylvania and Director of the University of Pennsylvania Epilepsy Center. He is one of the world's leading authorities on Neuroengineering, implantable brain devices and Neurotechnology, particularly for epilepsy. A clinician, scientist, inventor and entrepreneur, Dr. Litt divides his time between directing the Penn Epilepsy Center, Penn's Center for Neuroengineering and Therapeutics, Penn Health-Tech, a new university wide effort to bring new technologies from Penn scientists to patients, and his Philadelphia startup, Blackfynn. Dr. Litt has an Engineering degree from Harvard, received his MD and training in Medicine and Neurology from Johns Hopkins, and has served on the faculty at Hopkins, Emory University, Georgia Tech and the University of Pennsylvania. Dr. Litt's research focuses on Neuroengineering, specifically hardware, algorithms, machine learning, and high speed computing for implantable devices. He is a co-founder of Blackfynn, MC10 and IntelliMedix, and has contributed enabling technology to NeuroPace, NeuroVista, Medtronic, LevoNova among others. Dr. Litt has authored over 130 peer-reviewed papers, 17 patents, and received numerous honors and awards, including Dana, Klingenstein, Whitaker, Brain Research and Brain and Behavior Research Foundation Fellowships; and research innovation awards from the leading professional organizations in Neurology, Epilepsy and Bioengineering. He serves as an advisor to the NIH/NSF/White House BRAIN Initiative and is on the Editorial Board of Science Translational Medicine. He has mentored over 50 graduate students and postdoctoral Fellows who serve as leaders in academia, industry and government worldwide.
Core Collaborators
Department of Neurology
Department of Neurology
Smit Scholar
Research Administration
Clinical Scientists
Department of Neurology
Mirowski Scholar
Department of Radiology
Department of Neurology
Department of Neurology
Post-Doctoral Fellows
MD/PhD Students
PhD Students
"I am a visiting researcher from Dublin, Ireland with an interest in investigating intracranial EEG biomarkers to localize seizure onset zones and enhance treatment strategies for epilepsy patients Additionally, I am also interested in facilitating multi-center analyses and promoting the sharing of high-quality, well-annotated datasets to advance epilepsy research."
"My research focuses on developing novel tools and computational methods to improve diagnostics and treatment for neurological disorders, with a primary focus on epilepsy. Working at the intersection of neuroscience, engineering, and informatics, I aim to advance translational research that bridges quantitative approaches with improved patient care. Currently, I am using multimodal neuroimaging and electrophysiologic data to predict neuromodulation outcomes and optimize therapeutic strategies."
"My research focuses on creating AI-powered systems for personalized epilepsy management and advancing neuroinformatics to support large-scale, collaborative research."
"My research is focused on developing novel applications of neuromodulation to understand and treat Epilepsy. I'm interested in the intersection of machine learning, neurostimulation, and network science. I'm currently developing unsupervised models to map seizure onset and propagation, exploring the utility of neurostimulation to identify the seizure onset zone, and integrating imaging and electrophysiology modalities to better understand how disrupting seizure networks affects patient outcomes. My goal is to ultimately develop stimulation methods to modify abnormal functional networks and create lasting therapeutic changes for a variety of network disorders. In addition to neurostimulation, I'm working on a suite of machine learning projects using the electronic medical record and scalp EEG data to improve our understanding of Epilepsy prevalence and develop novel diagnosis tools."
"My research focuses on developing automated algorithms to analyze prolonged interictal periods during Epilepsy Monitoring Unit (EMU) stays, identifying biomarkers that dynamically evolve over time. A core objective involves detecting interictal spikes and quantifying their morphological features to investigate the drivers of their spontaneous and stimulus-evoked activation patterns. By optimizing the detection and interpretation of these biomarkers, this work aims to reduce the duration of EMU stays while preserving diagnostic accuracy, thereby accelerating clinicians' ability to map epileptogenic networks and select surgical targets."
"My primary research focuses on reducing the data review burden in ICU EEG monitoring through automated seizure detection. EEG monitoring is crucial for detecting seizures and neurological changes in critically ill patients, but its interpretation requires hours of manual review by experienced neurophysiologists. We aim to achieve a 70-80% data reduction while ensuring seizures are not missed. I’m also working on a collaborative project with Penn Medicine’s ICU to evaluate the performance of intracranial pressure morphological features as biomarkers for intracranial hypertension."
"My research explores machine learning and deep learning techniques to analyze EEG data, with a focus on integrating multimodal patient recordings—such as EEG signals, clinical text, and medical images—to help clinicians make more informed decisions. I am particularly interested in enhancing model explainability and safety, as well as applying causal analysis, to better understand and improve the role of these advanced tools in supporting epilepsy patient care."