Students

Ph.D Student

Analyzing interictal spikes to rigorously locate and predict the SOZ in patients with drug-resistant epilepsy.

Carlos' CV

M.D/Ph.D Student 

I grew up in St. Louis and graduated from Washington University in St. Louis in 2020 with a B.A. in Physics and Chemistry. I plan to get my MD/Ph.D. in Bioengineering at Penn. My research interests include developing novel materials and technologies for neural interfaces.

Ph.D Student

Zach Goldblum is a Bioengineering PhD student whose research focuses on creating AI-powered systems for personalized epilepsy management and advancing neuroinformatics to support large-scale, collaborative research.

Ph.D Student

Juri Kim is a Ph.D. student researching AI/ML applications in EEG-based epilepsy lateralization. Her work focuses on using interictal spike asymmetry in scalp EEG to improve the localization of temporal lobe epilepsy (TLE), aiding surgical planning. By leveraging automated spike detection and AI-driven analysis, she aims to enhance accuracy while reducing the need for prolonged monitoring. Her research bridges non-invasive and invasive methodologies for improved patient outcomes.

Visiting Research Scholar

Sarah Lavelle is a visiting researcher from Dublin, Ireland, with a research focus on localizing epileptogenic networks using intracranial EEG and neuroimaging. Her work aims to improve treatment strategies for patients with epilepsy by integrating electrophysiological and imaging data. She is also interested in advancing large-scale, collaborative epilepsy research through the multi-center sharing of high-quality datasets.

Sarah's CV

Ph.D Student

Georgios (George) Mentzelopoulos is a Ph.D student in the Department of Bioengineering. He completed his undergraduate studies at the University of Michigan, Ann Arbor, earning his Bachelor’s degrees in Biomedical Engineering and Electrical Engineering in April 2020.

He is interested in improving neural interfaces with both the peripheral and the central nervous systems. He is currently assisting the development of dry, super-nyquist density EEG arrays to investigate the prospect of phase-guided neuromodulation using transcranial magnetic stimulation. He is also assisting the development of EMG arrays to improve the neural interface of upper and lower limb prostheses.

In his free time, George enjoys tasting local brews, playing volleyball, and reading.

View Georgios' CV

Ph.D Student

Ph.D. Student

"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."

Ph.D Student

Haoer Shi is a Ph.D student in Bioengineering with a  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.

Ph.D Student

My primary research interest is centered around closed-loop neuromodulation devices that use biomolecules as a biomarker.

View Placid's CV

Ph.D. Student

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.

Katie's CV

Ph.D. Student

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.