Principal Investigator

 

 

Olivier Gevaert

Assistant Professor of Medicine and Biomedical Data Science

Affiliated Faculty - Integrative Biomedical Imaging Informatics at Stanford (IBIIS)

Member of BioX, Stanford Cancer Institute, Cardiovascular Institute, Wu Tsai Neurosciences Institute and Maternal & Child Health Research Institute (MCHRI)

Dr. Olivier Gevaert is an assistant 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.

 

 

Instructor

Kevin Brennan

Kevin has a rare combination of skills from his prior undergraduate and PhD training, which encompasses both (wet-lab based) molecular genetic/epigenetic and (dry-lab) bioinformatics and biostatistics expertise. Kevin is working on advanced computational analysis using TCGA data. 

Kevin is part of the Gentles & Gevaert lab. 

Postdocs

Marie Humbert-Droz

Marie obtained her PhD in chemistry from the University of Geneva, Switzerland, in 2017. After a first postdoc in photon science at SLAC simulating photo-properties of fluorescent proteins, she joined the group of Prof. Gevaert at BMIR in October 2019 to work towards solving problems using real-world data. Her research interests include the development of NLP tools for enhanced phenotyping of chronically ill patients. More specifically, the extraction of information such as symptoms, patient reported outcomes, functional status and mental health from clinical notes.

Pritam Mukherjee

Pritam received his B. Tech(Hons) with a major in Electronics and Electrical Communication Engineering and a minor in Computer Science and Engineering from Indian Institute of Technology (IIT), Kharagpur in 2010. In 2016, he obtained a Ph.D in Electrical and Computer Engineering at the University of Maryland, College Park under the guidance of Prof. Sennur Ulukus. From January to December 2017, he was a postdoctoral researcher in the Electrical Engineering department at Stanford University with Prof. Tsachy Weissman and Prof. Ayfer Ozgur. From January 2018, he joined the Gevaert lab at BMIR in the Stanford School of Medicine where he is pursuing research into the application of machine learning and deep learning to medical imaging, primarily focusing on cancer. Recently, he has also been interested in mining electronic health records data for patient stratification and temporal modeling of patient outcomes.

Heather Selby

Heather Selby is a post-doctoral BD-STEP fellow at the Stanford Center for Biomedical Informatics Research (BMIR) and the Palo Alto VA working with both Dr. Olivier Gevaert and Dr. Rajesh Shah. Heather earned her PhD in Bioinformatics from Boston University in 2020, and her dissertation is entitled “Know Thy Cells: Inferring Phenotype from Genotype”. While her PhD research focus was in genomics, Heather’s post-doctoral research focus is in radiomics. Radiomics is an emerging translational research field of research aiming to extract mineable high-dimensional data from clinical images. Heather is now working to (i) classify lung nodules as benign, small cell lung cancer, or non-small cell lung cancer using only radiomic features, (ii) predict immunotherapy response using both radiomic and genomic signatures, and (iii) determine resistance to treatment from follow-up CT imagery using radiomics combined with deep learning. The ultimate aim of this work is to successfully perform multi-scale biomedical data fusion in cancer.

Graduate Students

Shaimaa Bakr

Shaimaa is a Ph.D. student in the Department of Electrical Engineering. Shaimaa is a member of the Gevaert and RIIPL labs. 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.

Xianghao "Sam" Zhan

Xianghao Zhan is a Ph.D. student in the Department of Bioengineering. He obtained his 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 from Prof. 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 involve the data mining of free-text clinical notes with natural language processing and biomedical data fusion for COVID-19 patient outcome prediction.

Bryce Bagley

Bryce Bagley is an MD candidate interested in the use of AI and machine learning for augmenting human abilities to use medical imaging and other medical data, especially as relates to brain pathologies.

 

Yiheng "Terry" Li

Yiheng "Terry" Li is a student in the Biomedical Informatics master program of the Department of Biomedical Data Science, School of Medicine. He obtained his B. Sci. in the Department of Resources and Environment in Shanghai Jiao Tong University, China, in 2019. He is interested in building machine learning models and developing tools for various types of data (tabular, image, free text, time series, etc.) for the optimization of patient health.

Undergraduates

Divya Nagaraj

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Current visitors

Alexander Thieme

Dr. med. M. Sc. Alexander H. Thieme is a board-certified specialist in Radiation Oncology from Charité - Universitätsmedizin Berlin and fellow and speaker of the Digital Clinician Scientist program of the Berlin Institute of Health (BIH), Germany. His research interests are voxel-based methods to evaluate and optimize radiation treatment plans and assess toxicity using electronic patient-reported outcomes. He is the author of the Open Source application "CovApp" which was used on a large scale by several million users to mitigate the Covid-19 pandemic in Germany and internationally in the USA and Italy. He was also involved to create a GPS-derived contact index to successfully predict new Covid-19 infection cases and the onset of the 2nd surge in Germany. He is currently working at Stanford University as a visiting scholar to develop machine learning models with a focus on clinical application in radiotherapy.

Internal and External collaborators

Haruka Itakura

Haruka’s research has focused on imaging genomics in brain tumors. She has developed an approach that flips the common analysis framework, by starting from the imaging phenotype of a solid tumor instead of it’s molecular characterization. She has used quantitative characterizations of human brain tumors using their MR images to define subgroups. This led to three brain tumor subgroups that she successfully validated in an external validation data set and was able to match with molecular pathway activities. This result showed thought-provoking implicates with regards to treatment for brain tumors. 

Nathalie Pochet

Nathalie is an assistant professor at Harvard University and the Broad Institute. 

Idoia Ochoa

Idoia is an Assistant Professor of Electrical and Computer Engineering. 

Mikel Hernaez

Mikel is the Director of Computational Genomics at the IGB, University of Illinois

Adrien Depeursinge

Adrien is an assistant professor at EPFL, Lausanne. 

Interns & past interns

Natasha Palamuttam

Natasha is an undergraduate at University of California Irvine. She is pursuing a B.S. in Biomedical Engineering and a minor in Bioinformatics. She is interested in applying computational skills to model and solve biological problems. 

Anika Cheerla

Anika Cheerla is a high school student at Monta Vista High School in Cupertino, CA

Nikhil Cheerla

Nikhil Cheerla is an undergrad at Stanford university. 

Past visitors

Robin Vandaele

Robin was a PhD student at University of Ghent at the time of his visit, and has now transitioned to a postdoc position at the department of applied mathematics, computer science & statistics. 

Tina Smets

Tina is a PhD student in Engineering Sciences at ESAT-STADIUS, KU Leuven, Belgium

Murilo Cintra

Murilo is a head and neck radiologists visiting from Sao Pualo Brazil. Murilo is interested in modeling head and neck cancers and how quantitative imaging can be used to diagnose and treat patients. 

Chao Huang

Chao Huang is a visiting student from Zhejiang University in Hangzhou supported by the China Scholarship Council. She is interested in biomedical data fusion and applying advanced statistics on large biomedical data.

Zichen Wang

Zichen Wang is a visiting student from Zhejiang University in Hangzhou. He is interested in machine learning analysis on quantitative image features and deep learning with applications to healthcare. His research focuses on developing deep learning models on pathology images.

Shuo Wang

Shuo Wang is a student at the Chinese Academy of Sciences in Beijing. 

Dondong Yu

Dondong Yu is a student at the Chinese Academy of Sciences in Beijing. 

Alumni

Alumni - Graduate students

Lina Qiu

Lina is a graduate student in electrical engineering. 

Majed Magzoub

Majed is an undergrad at Stanford. 

Marc Thibault

Marc is a master student ICME.

Katie Planey

Katie finsihed her phd focusing on developing algorithms on meta-analysis of biomedical data. She developed CoINcIDE, a method for meta-clustering across multiple biomedical data sets. She currently is a co-founder & CTO at Mantra Bio

 

 

Alexandre Momeni

Alex is a visiting master student. 

Guillaume Chhor

Guillaume is a master student at Stanford. 

Marcos Prunello

Marcos focuses on the statistical analysis of DNA methylation in cancer. He uses and extends, MethylMix, a method which identifies differentially methylated and transcriptionally predictive genes, in samples from single or combined cancer sites. He is currently pursuing a PhD in Buenos Aires, Argentina. 

Julie Koenig

Julie is focusing her medical studies on informatics and data driven medicine. She is intersted in how epigenomics defines subtypes of cancer patients and how this can impact precision medicine. She joined the lab as a Stanford Med Scholar, and after finishing continued her medical studies. 

Pierre-Louis Cedoz

 

Romain Sauvestre

Romain is a master student at Stanford.

Alumni-postdocs

Hong Zheng

Hong is a Postdoc in the Gevaert lab. Hong got her PhD from The University of Hong Kong, Department of Clinical Oncology, under the supervision of Prof. Maria Lung. She studied bioinformatics and cancer genomics and her thesis was on Identification of Genetic Susceptibility Genes and Characterization of Somatic Mutations in Nasopharyngeal Carcinoma. Her interests focus on big data in genomics and precision medicine. She works with multiple omics datasets, including whole-genome, whole-exome, transcriptome, methylome, etc. She is proficient in several programming languages (R, Python, Linux/Bash, and Perl), statistical analysis, and machine learning methods. Her research has been focused on understanding the genetic and genomic basic of cancer by integrating and digging into the massive amount of sequencing datasets in cancer genomics. Currently, she works on developing novel algorithms for high throughput sequencing data. She focuses on studying long non coding RNAs and epigenomics.

Jayendra Shinde

Jay is a postdoc in computational biology working on developing novel algorithms for multi omics data fusion. 

Mu Zhou

Mu Zhou earned Ph.D. degree in Computer Science and Engineering, advised by Dr. Lawrence Hall and Dr. Dmitry Goldgof at the University of South Florida in 2015. His research interests are focused on the intersection of precision medicine and data mining. During his Ph.D. study, he has been involved in cross-disciplinary research projects, directed by Dr. Robert Gatenby and Dr. Robert Gillies at H. Lee. Moffitt Cancer Research Institute, Tampa. In particular, he worked on the field of quantitative cancer imaging for tumor response assessment in various domains (e.g., brain, breast, sarcoma, and lung cancers). His research aims to develop computational models that integrate clinical information (e.g., imaging, genomic, and clinical records) to predict cancer treatment outcomes and improve personalized healthcare.

Magali Champion

Magali Champion was a postdoc in the lab focusing on the integration of multi-omics data for cancer investigation. She is currently continuing her postdoc studies at Paris Descartes