BMIR Labs and Staff

Mete received his B.Sc. from Istanbul Technical University, M.Sc. and Ph.D. in Computer Engineering from Dokuz Eylul University, Izmir, Turkey. He worked as a Research Assistant at Dokuz Eylul University and in the Laboratory of Quantitative Imaging at Stanford University before joining the Mussen Lab. His main research subjects are big data analytics, parallel algorithms, biomedical imaging and informatics.

Attila is a full-stack software developer. He received his MS in Computer Science (Machine Learning) from Georgia Tech after his BS from the Technical University of Cluj-Napoca, Romania. He worked at several companies in Romania, Hungary, previously he was part of the CEDAR team for five years. After 1.5 years of AWS experience, he returned to continue the work on CEDAR and other projects of the Musen Lab.

Matthew is a research software engineer in the Musen Lab.  He works on the RADx (Rapid Acceleration of Diagnostics for COVID) project, and he also works on other Lab initiatives including the Protégé suite of tools for ontology engineering.

With over two decades of experience as a research software engineer, Martin O'Connor specializes in constructing tools that facilitate the integration of semantic technologies into software systems. Currently, his primary emphasis lies in enhancing metadata representations by incorporating rich semantic content through the use of ontologies. Martin played a pivotal role in architecting and overseeing the development of CEDAR, a web-based platform designed for the management of metadata related to scientific experiments.

Jennifer is a senior software engineer at the Stanford Center for Biomedical Informatics Research in the Musen Lab. Her focus is developing the BioPortal software for accessing and sharing biomedical ontologies. She additionally holds the role of the primary Stanford representative within the OntoPortal Alliance, an international consortium comprised of research teams dedicated to promoting and collaboratively advancing the OntoPortal open-source software.

Cesar is Ph.D. Student in Epidemiology and Clinical Research, Admitted Autumn 2022.

Tushar received bachelor's and master's degree in computer science followed by Ph.D. in clinical informatics. His work involves deducing inferences from electronic health records (EHRs)/clinical data and provide feasible solutions to existing bedside or physician encountered clinical problems. Previously at Tata Medical Center Kolkata, India, he was involved in analyzing pediatric acute lymphoblastic leukemia maintenance therapy (MT) data and developed tools to standardize the MT practice. His work in the Boussard lab as postdoctoral scholar will involve use of EHRs to identify and classify common ocular diseases followed by cluster analysis to identify novel subtypes of these conditions that have not been recognized before.

Malvika received her PhD in Health Informatics from the University of North Carolina at Chapel Hill and previously received her BS in Quantitative Biology from there as well. Her dissertation research was on computational phenotyping and drug repurposing from electronic medical records, specifically for breast and oral cancer patients. Her primary interest is in developing clinical decision support tools, and in the Boussard lab, she focuses on moving models to point of care.

Alison Callahan is an Instructor and Clinical Data Scientist in the Center for Biomedical Informatics. In collaboration with Nigam Shah's group, her work involves research and development of informatics methods for the analysis of biomedical and clinical data to derive insights and inform medical decision making. Her current research focuses on using informatics to improve the breadth and quality of data available from EHRs for studying perinatal and reproductive health.

Akash is a 2^nd year Master’s student studying Computer Science focusing on training and evaluating clinical foundation models. He has undergraduate degrees in Biomedical Engineering and Computer Science from Johns Hopkins University. His current research focuses on evaluating generative models for clinical workflows, as well as building new generative models by combining structured and unstructured electronic health record data. His previous work includes computer vision, multimodal modeling, and meta-learning.

Hejie completed her Ph.D. in Computer Science and Informatics at Emory University in May 2024. Her research focuses on the intersection of machine learning, data mining, and medical informatics. Her dissertation work centers on AI-assisted healthcare with Multimodal Structural Knowledge Extraction and Augmented Inference. She has worked on two main technical themes: (1) multimodal structured knowledge extraction, which involves integrating knowledge from various data sources and pre-trained models for comprehensive data understanding, and (2) augmented inference, which focuses on improving the domain-specific reasoning capabilities and reliability of AI models by incorporating extracted or external knowledge resources. During her Ph.D., she interned at Research Labs in Microsoft Research and Amazon Science. At Stanford, she is interested in exploring LLM/LMM model tuning, adaptation, and alignment for medical domains.

Post-Doctoral Scholar

Fatemeh earned her Ph.D. in Bioinformatics and Systems Biology from the University of California, San Diego. She is exploring the potential benefits of fine-tuning large language models for healthcare, which have shown significant advancements in text and image generation, data summarization, and conversational AI applications. 

Dr. Ethan Goh is a research fellow with a background in informatics, digital health transformation, and strategic innovation. His research at Stanford focuses on leading multi-site, grant-funded evaluation of Large Language Model applications within healthcare.

Bryce Allen Bagley is an MD student in the Physician-Scientist Training Program at Stanford Medical School, and his research focuses on the development of mathematical, computational, and machine learning methods for accelerating biomedical research and improving medical care. He is particularly interested in problems related to complex systems science, neuroscience, and neurological medicine. His past work in the Gevaert lab has primarily been on applications in medical imaging, while his current work is on complex systems physiology in brain tumors, epilepsy, and other neurological diseases - a collaboration between the Petritsch Lab and the Gevaert Lab. Prior to medical school he completed an MS in Theoretical Biophysics at Stanford University along with a BS in Systems Science & Engineering, BS in Computer Science, and BS in (Bio)Physics through the Washington University in St. Louis dual-degree program.

Chris is a data scientist in the Gevaert lab working on the fusion of different biomedical data modalities. His research previously was focused on molecular biology and physics based simulations. He is currently looking into combining different modeling techniques to generate a medical digital twin.

Qinmei Xu previously was a student visiting researcher in the Gevaert lab, and now is a postdoctoral scholar. She received her Ph.D. in Clinical Medicine in 2022 from Nanjing University, where her studies focus on the fusion of multi-scale data and the use of machine learning and deep learning models for disease classification and prognosis prediction. She now works primarily in quantitative imaging data of complex diseases.

Yiran is a PhD student in Epidemiology studying the incarceration-associated burden of infectious diseases and mortality. In the Khatri lab, she has done research identifying immunological biomarkers of risk and protection following infection and vaccination. 

Simone’s educational background is in genetics, epigenetics and immunology, specifically regarding their role in the pathophysiology of septic shock and other severe outcomes in infectious diseases. Simone completed her PhD at the University of British Columbia, Canada, focused on the immunology and genetics of septic shock. Her first post-doctoral research fellowship at Stanford was in the Dept of Emergency Medicine using ATAC-seq to study the epigenetics of neutrophils exposed to TLRs and live organisms common in sepsis. She then joined the Khatri lab with BMIR for a second post-doc to train specifically to use computational biology to study infectious diseases, concentrating on influenza diagnostics and prognostics. She worked for just over 2 years in industry as Sr. Computational Biologist at Inflammatix, Inc with a focus on infectious diseases specializing in COVID-19 and other viral diseases. She is very excited to have recently transitioned back to Stanford as a Sr. Data Scientist in the Khatri Lab!