Gevaert Lab
Olivier Gevaert, PhD.
Associate Professor of Medicine, Computational Medicine
Dr. Olivier Gevaert is an associate professor at Stanford University focusing on developing machine-learning methods for biomedical decision support from multi-scale data. He is an electrical engineer by training with additional training in artificial intelligence, and a PhD in bioinformatics at the University of Leuven, Belgium. He continued his work as a postdoc in radiology at Stanford and then established his lab in the department of medicine in biomedical informatics. The Gevaert lab focuses on multi-scale biomedical data fusion primarily in oncology and neuroscience. The lab develops machine learning methods including Bayesian, kernel methods, regularized regression and deep learning to integrate molecular data or omics. The lab also investigates linking omics data with cellular and tissue data in the context of computational pathology, imaging genomics & radiogenomics. Dr. Gevaert joined BMIR in 2015 as an Assistant Professor of Medicine.
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.
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.
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.
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.
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.