BMIR Labs and Staff

Musen Lab

Mete Ugur Akdogan

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.

Yan Cao

Yan holds two MS degrees - one in Computer Science from Georgia Institute of Technology and another in Food Science from the University of Minnesota. She joined the Musen Lab as a Software Developer I after graduating from Georgia Tech. Her passion lies in software development for the Bioscience fields, and she had been contributing to the CEDAR project since joining the team and now working on RADx (Rapid Acceleration of Diagnostics for Covid) project.

Michael Dorf

Misha Dorf has been the Chief Software Architect at the Stanford Center for Biomedical Informatics Research for more than 10 years. Misha has an extensive software development background in both industrial and academic environments. He has played the principle role in devising the architecture of the BioPortal and OntoPortal technologies and has extensive experience in Web-based application development with a heavy focus on back-end frameworks and programming languages. He also contributes to RADx (Rapid Acceleration of Diagnostics for Covid) project.

Attila L. Egyedi

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.

Josef Hardi

Josef is a senior software developer at Stanford University with over 12 years' experience developing software and managing complex data. His interest in Ontology and Semantic Web Technologies began during his first job at a data lab where the team was doing a research and development in relational databases and ontologies where they discovered a novel method to connect both. Later this knowledge and skill set eventually led him to Musen Lab at Stanford University where he began working on developing modules for Protégé, an ontology editor software known widely by the semantic web communities. Subsequently, he became more involved in bigger projects such as CEDAR and FAIRware, which focus on addressing challenges in metadata management in biomedicine. Currently, he is part of a multi-institutional team working towards the creation of a human atlas at cellular level, called HuBMAP, and is responsible for shaping the reporting guidelines for cell research datasets.

Josef earned a Master's degree from the European Masters Programme in Software Engineering (EMSE), which he completed in Italy and Sweden.

Matthew Horridge

Matthew is a research software engineer in the Musen Lab. He works on the Protégé suite of tools for ontology engineering.

Christian Kindermann

Christian is interested in helping scientist manage their data with semantic technologies (ontologies, knowledge graphs, automated reasoning). He earned a PhD from the University of Manchester, England, where he developed a range of novel pattern-based ontology engineering techniques. He continued his research as a postdoc at the University of Oslo, Norway, before joining the Musen Lab at Stanford as a postdoctoral scholar, where he will apply his techniques in data integration efforts.

Marcos Martínez-Romero

Marcos Martínez-Romero is an experienced Research Software Engineer with a strong background in Knowledge Engineering and Semantic Technology. He earned his B.Sc., M.Sc., and Ph.D. in Computer Science and Artificial Intelligence from the University of A Coruña, Spain. Marcos started his journey with BMIR in 2013 and contributed to various projects, including BioPortal and CEDAR. After serving as an Ontology and Knowledge Engineer at Acubed, Airbus’ Innovation Center in Silicon Valley, Marcos returned to BMIR, currently focusing on the RADx project among other key initiatives.

Martin O'Connor

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.

Alex Skrenchuk

Alex is an experienced Linux Systems/DevOps Engineer specializing on High Performance computing, configuration management frameworks, CI/CD pipelines and support of in-house developed web applications.

Sowmya S Sundaram

Sowmya is a postdoctoral scholar, working with Prof. Mark Musen at the Center for Biomedical Informatics Research. She was previously working as a postdoc at the intersection of natural language processing and biomedical project at L3S Research Centre, Hannover. During that phase, she worked with real medical datasets and interacted with doctors and clinicians from MHH, Hannover, for dental implants and cochlear implants. She earned her PhD from Indian Institute of Technology, Madras, India, which was on different representations for NLP problems for the use case of school level word problems. At the Musen Lab, she will be the NLP expert for devising algorithms that clean up ontology metadata and enhance user experience.

Jennifer Vendetti

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.

Hernandez-Boussard Lab

Tushar Mungle

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.

Ashley Lewis

Ashley is a PhD student focused on understanding the role of race in clinical algorithms. She is interested in leveraging social determinants of health data and robust machine learning methods to decrease health disparities exacerbated by race-based algorithms.

Behzad Naderalvojoud

Behzad is a Biostatistician at Stanford University. He received his Ph.D. in Computer Science from Hacettepe University in Turkey, with specializations in machine learning, deep learning, natural language understanding, neural sentiment analysis, and healthcare intelligence. His research in the Boussard lab focuses on prolonged opioid use prediction models, and he develops descriptive, predictive, and analytical tools for postoperative pain research using OMOP CDM to promote the timely generation of evidence across multiple populations and settings.

Max Schuessler

Max is an MD specializing in medical oncology and a PhD student in Biomedical Informatics. He completed his medical degree at the University of Heidelberg, a Master of Public Policy, and an MSc in Pharmacology at the University of Oxford. His research focuses on the development and translation of machine learning and causal inference methods into clinical impact and better understanding of heterogeneous treatment effects.

Yuqing Wang

Yuqing is a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research (BMIR). She received her BS in Mathematics and PhD in Computer Science from the University of California, Santa Barbara. Her research focuses on enhancing the reliability and trustworthiness of machine learning and language models in healthcare, aiming to ensure their positive and ethical contribution.

Malvika Pillai

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.

Cesar Baeta

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

Madelena Ng

Madelena is a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research. Her research aims to illuminate the evolving equity and ethical challenges in digital and emerging technologies (e.g., web- and app-based population health research, clinical AI solutions, blockchain for health data). Her work in the Boussard Lab focuses on discerning key factors for clinical AI solutions to flourish in practice—from the readiness of the datasets for machine learning research to the operational principles that are required for successful deployment.

Shah Lab

Alison Callahan

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.

Ben Viggiano

Ben is currently a second-year academic master’s student in the Biomedical Informatics program. Prior to Stanford, Ben completed undergraduate degrees in computer science and biomedical engineering at the University of Wisconsin-Madison, specializing in artificial intelligence and medical imaging.

Research Interests: Multimodal approaches to enable clinical decision support from diverse data sources, and fairness/bias analysis of healthcare focused models.

Jason Fries

Jason is a staff research scientist in the Shah Lab. His interests fall in the intersection of computer science and medical informatics. His research interests include:
• Machine learning with limited labeled data, e.g., weak supervision, self-supervision, and few-shot learning.
• Multimodal learning, e.g., combining text, imaging, video and electronic health record data for improving clinical outcome prediction
• Human-in-the-loop machine learning systems.
• Knowledge graphs and their use in improving representation learning

Akash Chaurasia

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.

Michael Wornow

Michael is a 3rd year computer science PhD student focused on developing and operationalizing large-scale AI models in healthcare systems. He is interested in developing methods that integrate multimodal data from electronic health records (particularly clinical text and structured codes) into "foundational models" for clinical data, as well as developing a better understanding for how we can more effectively evaluate and benchmark the performance of such models and measure their real-world utility for patients.

Zepeng 'Frazier' Huo

Zepeng 'Frazier' Huo as a Postdoctoral Scholar to the Center for Biomedical Informatics Research as part of Shah Lab. He earned his Ph.D. and master's degree in computer science from Texas A&M University. In his previous work, he investigated the heterogeneity aspect of medical AI method in terms of population level and individual level and how that might have affected machine learning models in different ways. The approaches he took included uncertainty quantification, Mixture-of-Experts, domain adaptation and continual learning. At Stanford, he is interested in investigating the potential benefits of Foundation Models in healthcare, which have shown amazing results in text generation, dialogue, and even production of art.

Chen Lab

Fatemeh Amrollahi

Post-Doctoral Scholar

Sajjad completed his PhD in Computer Science at the Institute of Biomedical Informatics at the University of Kentucky (UK) and is conducting AI and healthcare data science research.

Bryan Bunning

PhD student

Bryan is a PhD student in Biomedical Informatics Research interesting in bridging informatics solutions to advancing the access to and execution of modern clinical trials.

Minh Nguyen

Ph.D. Student

Minh is a Ph.D. student in the Biomedical Informatics program. She is interested in clinical informatics, causal inference, and measurement/label bias.

Ethan Goh

Ethan is an internal medicine physician working toward a Clinical Informatics and Management MS at Stanford. He is interested in the research and design of clinical decision-making tools aimed at improving health equity and clinical outcomes.

Iván López

Iván is a medical student dedicated to advancing the field of machine learning by exploring and implementing methods for extraction of unstructured data from electronic health records.

Grace Kim

Computer Science Student

Grace is currently working towards her B.S. and M.S. in Computer Science at Stanford and is interested in the intersection of CS and medicine. Her research focuses on how machine learning and data science can be used to promote efficient, affordable, and high-quality healthcare.

Gevaert Lab

Bryce Allen Bagley

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.

Rohan Bareja

Rohan Bareja completed his masters in bioinformatics at New York University and an additional masters in data science at Columbia University. He was a bioinformatics analyst at Weill Cornell and most recently a bimoedical software engineer at Case Western Reserve University.

He is now a research engineer in the Gevaert lab working on multi-modal data fusion of biomedical data for complex disseases.

Yuanning Zheng

Yuanning works as a postdoctoral scholar in Dr.Gevaert’s lab. He received his Ph.D. in Medical Science in 2021 from Texas A&M University, where his research studied gene and environment interaction and breast cancer prevention. His postdoc work in the lab focuses on developing machine learning approaches to model high-dimensional, multi-modal and multi-omics data, with a goal of improving cancer classification and predicting treatment response. His current work includes (1) integrating histology and genomic data to resolve brain cancer heterogeneity and predict survival outcomes; (2) developing bioinformatic workflows that integrate epigenomic and transcriptomic data to discover biomarkers and therapeutic targets for cancer precision medicine.

Christoph Sadee

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.

Xianghao Zha

Xianghao Zhan is a 4th-year Ph.D. candidate in the Department of Bioengineering. He obtained his M.S in Bioengineering at Stanford University in 2021, and he is pursuing his M.S in Statistics (2023). Before that he got B. Eng. in control science and engineering and his B. Art in English language and literature with Summa Cum Laude at Chu Kochen Honors College, Zhejiang University, China, in 2019. Under the guidance of Prof. Oliver Gevaert and Prof. David B. Camarillo, he mainly focuses on the optimization of computational modeling of traumatic brain injury with machine learning based on biomechanical and radiological data. His research interests and projects also extend to the data mining of free-text clinical notes with natural language processing, biomedical data fusion for COVID-19 patient outcome prediction, conformal prediction and domain adaptation for biomedical sensory systems (with artificial olfaction systems and surface electromyography systems).

Shaimaa Bakr

Shaimaa is a Postdoctoral researcher at the Gevaert lab. Shaimaa completed her Ph.D. in Electrical Engineering from Stanford, supervised by Sandy Napel in the RIIPL lab. Prior to Stanford, Shaimaa received her B.Sc. (Summa Cum Laude) from the American University in Cairo, where she studied Electronics Engineering and Computer Science. She obtained her MS degree in Electrical Engineering from Rensselaer Polytechnic Institute, working in the Cognitive and Immersive Systems lab, and advised by Professor Richard Radke. Shaimaa is interested in applying and developing machine learning methods for medical imaging and molecular data.

Qinmei Xu

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.

Humaira Noor

Humaira obtained a Ph.D in Medicine from the University of New South Wales (UNSW), Sydney, Australia in 2022. Her Ph.D research focused on investigating the effects of lower-grade glioma genomic aberrations on patient prognosis and therapeutic response. Previously, she has completed M.Phil from the University of Sydney, where she worked on understanding the immunological effects of a naturally derived marine compound. She also taught multiple undergraduate courses at a leading private University in Bangladesh for two years. She is currently a postdoc at the Gevaert Lab, where she is working on developing machine learning approaches for brain cancer and other diseases.

Khatri Lab

Michael Scott Freddman

Mike is a pediatric critical care fellow and biomedical informatics postdoc in the Khatri Lab. He is interested in using clinical and gene expression data to predict onset and severity of critical illness. His current research-in-progress expands on the Khatri lab’s previous discovery of a host response-based gene signature to predict the severity of viral infections and to identify endotypes of critically ill patients. He has demonstrated that there is a common host response to both bacterial and viral infections in critically ill patients that can predict the severity and endotype of the patient.

Yiran Liu

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.

Hong Zheng

Dr. Hong Zheng is a staff scientist in Khatri lab. She completed her Ph.D. in cancer genomics and clinical oncology from The University of Hong Kong. Her research focuses on using computational biology and machine learning approaches to understand the multi-omics landscape (genomics, epigenomics, transcriptomics, etc.) and immune responses in human diseases (cancer, aging, infectious diseases, etc.), and identify robust gene signatures and targets for disease diagnostics, prognostics, and therapeutics.

Simone Thair

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!

Denis Bebek

Denis is a research engineer focusing on applications of systems biology approaches in immune-epigenomics. His multidisciplinary scientific career interconnects genetics, cancer biology, epigenetics, immunology, and computational systems biology. He has leveraged cutting-edge technologies and innovative computational approaches to study human physiology and disease. The algorithms I developed have revealed novel concepts in cancer, immunology, and epigenetics. He is pioneering systems approaches in the field of epigenetics.

Administrative Staff