BMIR Labs and Staff
Dr. Timothy Redmond (a graduate of Princeton University in mathematics) is working on the BioPortal project. He is interested in ontologies from a logical perspective, which served him well in a previous incarnation at BMIR where he headed up the Protege project.
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.
Jennifer Leigh 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.
Josef is a software engineer with in-depth experience in building software systems. His expertise lies within back-end system development, and the main languages in his tech stack are Java, Python and SQL. He is interested in semantic web technologies for biomedicine and a lifelong learner (currently taking a course on front-end development with React to become a full-stack developer!).
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.
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.
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.
Matthew is a research software engineer in the Musen Lab. He works on the Protégé suite of tools for ontology engineering.
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.
Becky is a health policy researcher and a postdoc in the School of Medicine at Stanford University. Her research combines approaches from healthcare economics, health policy, and health services research to better understand how vulnerable patients access and experience healthcare, particularly through their relationships with providers. In her dissertation and in ongoing work, Becky uses Medicaid administrative claims data (MAX, TAF, and others) to explore the role of the physician-patient relationship in coproducing health among Medicaid managed care enrollees.
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 is a postdoctoral research fellow 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.
Nilpa Shah is a Program Manager at the Stanford Center for Biomedical Informatics Research in the Boussard Lab. As a public health professional, she studies healthcare services with a focus on how sociocultural and biobehavioral determinants impact the health of vulnerable and marginalized populations. Her interests include improving healthcare equity, patient outcomes, and healthcare access and delivery for marginalized communities.
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.
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.
Elia Saquand received her Bachelor's degree in Communication Systems at the EPFL in Lausanne and is pursuing her Master's degree in Data Science at the ETH Zurich. Her research focuses on computational biomedicine, and she has applied her skills in several Bio AI projects, including cancer analysis from scRNA-seq data and AlphaFold simplification. In the Boussard Lab, she is working on her Master’s thesis, a 6-month research project, on symptom extraction from patients’ clinical notes after chemotherapy.
Gabriela is a fourth-year undergraduate student in Human Biology with a concentration in Data science and Public Health. She is interested in applying interdisciplinary techniques to the study of structural health disparities with a particular focus on using these insights to drive the development of effective social and policy level interventions.
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.
Ines is a MS student in computational mathematics at Stanford, with a specialization in data science. Ines and Joachim's research in the Boussard lab focuses on developing natural language processing models to identify postoperative falls and delirium in the elderly population from clinical notes and then predict patients at risk for these outcomes.
Joachim is a MS student in computational mathematics at Stanford, with a specialization in data science. Joachim and Ines's research in the Boussard lab focuses on developing natural language processing models to identify postoperative falls and delirium in the elderly population from clinical notes and then predict patients at risk for these outcomes.
Shai Waldrip is a postdoctoral scholar in the Boussard Lab. Her research aims to develop a digital twin for breast cancer patients that will ultimately support the advancement of precision oncology and improve clinical decision making and patient centered care. Specifically, she will use machine learning and mechanistic modeling using multimodal (e.g., genomic, EHR, imaging) and multiscale data. She will also create a framework to evaluate bias and fairness of the algorithms as well as their clinical feasibility and utility.
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 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.
Yizhe (Ee-jah) Xu (shoo) is a postdoc scholar at BMIR, advised by Dr. Nigam Shah. She graduated from the Division of Biostatistics, Department of Population Health Sciences at the University of Utah in 2020. Her dissertation focused on developing methods for estimating individualized treatment rules from a cost-effectiveness perspective, which was published in Biometrics.
Yizhe’s primary research interests are in causal inference and machine learning, and she is passionate about applying advanced statistical methods to answer meaningful questions in biomedical research using large-scale EHR, or claims data, as well as data from clinical trials.
Ethan Steinberg is a Ph.D. student in Computer Science at Stanford University, co-advised by Jure Leskovec and Nigam Shah. He is interested in the intersection of machine learning and healthcare, and how we can better use representation learning to improve risk prediction and causal inference.
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.
Scott Fleming is a fifth-year Ph.D. student in Biomedical Data Science working at the intersection of causal inference and machine learning. His current research focuses on training foundation models with electronic health record data to estimate treatment effects, predict patient outcomes, and synthesize patient discharge summaries. Other/past research interests include offline reinforcement learning for healthcare applications, AI/ML for psychiatry, and mitigating temporal drift in machine learning models.
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.
Rahul is a Data Scientist at Stanford Medicine, working in close collaboration with engineers from Stanford Health Care (SHC) to evaluate and deploy machine learning models into clinical practice. He works on building ML infrastructures on cloud platforms, that will allow researchers to seamlessly deploy ML models into Stanford hospital. He is also part of a data science group spearheaded by Dr. Nigam Shah, where they conduct multi-faceted evaluation of models such as clinical utility, ethics, and IT feasibility assessments. Additionally, he collaborates with PhD students in Dr. Shah's lab to investigate ways to use foundation models in clinical practice. Most recently, he has been working on integrating clinical notes into structured EHR data representations to improve the downstream clinical prediction tasks.
Sajjad Fouladvand PhD MSc
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.
PhD Student, Biomedical Informatics
Conor Corbin is a PhD student in the Biomedical Informatics Program. His interests lie in the application of machine learning to clinical workflows - maximizing patient outcomes and minimizing cost.
Minh is a Ph.D. student in the Biomedical Informatics program. She is interested in clinical informatics, causal inference, and measurement/label bias.
Sergio "Checo" Gonzales
PhD Candidate in Biomedical Informatics
Checo is a PhD Candidate in Biomedical Informatics and researches how EHR design and representation impact health equity for people who are systematically oppressed and underserved.
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.
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.
MD, Clinical Informatics Fellow
Naveed is an electrical engineer-turned physician who studies how to use medical data and healthcare IT to improve clinical operations and quality of care. Current projects include data analytics for improving clinical processes around diagnostic testing, implementation of clinical decision support machine learning models into practice, and using the EHR to promote safe and value-based care.
Matt is a Postdoctoral Medical Fellow and Hematology
Masters Student in Biomedical Informatics.
M.D. Stanford Intermountain Fellow
Vivian is a vascular surgery resident working toward a Biomedical Informatics MS at Stanford. She is interested in the design and deployment of clinical decision-making tools aimed at improving surgical care.
Raphael is an Undergraduate in both Computer Science and Human Biology.
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 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.
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.
Sandra received a Master in Bioscience Engineering in 2012 and Doctor of Applied Biological Sciences, cell and gene biotechnology in 2016 from Ghent University in Belgium. She has frequently contributed to books and peer-reviewed research articles on (personal) genomics and epigenetics. Sandra is a Research Engineer at BMIR. Here, she focuses on biomedical data fusion of complex diseases, primarily oncology and cardiovascular diseases. Using novel AI approaches that digest multi-omics, multi-modal or multi-scale data she aims to enhance disease understanding with applications for precision medicine. Previously, Sandra worked for (bio)tech startups and companies in the US healthcare space deploying the power of biomedical data for next generation diagnostics and therapeutics.
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.
Thomas is a postdoctoral scholar with a medical background in Internal Medicine and a degree in Immunology from the University of Lyon (France). As a practitioner in hospital medicine, he is mainly interested in rare autoimmune diseases such as systemic lupus erythematosus (SLE). Hisnpostdoctoral project in Prof Olivier Gevaert's laboratory aims at developing deep learning tools that take advantage of data fusion procedures to assist clinical decision-making in the management of complex diseases.
Ahmet Gorkem Er
Ahmet Görkem Er is a visiting student researcher as a Fulbright Ph.D. Dissertation Research Grantee at Stanford. He is pursuing a Ph.D. in medical informatics at Middle East Technical University (Turkey) and holds an M.D. degree with a double specialty of internal medicine and infectious diseases and clinical microbiology. He is interested in machine learning approaches in healthcare and working on multi-scale data fusion and radiogenomics in Gevaert's Lab.
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 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).
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.
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 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.
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.
Sanjana Gupta is a postdoc in the Khatri Lab at Stanford. Her research interests include statistical and machine learning approaches to identify and evaluate biomarkers of disease and disease progression from transcriptomic data.
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.
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’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 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.
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.