School of Medicine
Showing 1-10 of 18 Results
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Shaimaa Bakr
Postdoctoral Scholar, Biomedical Informatics
Masters Student in Biomedical Informatics, admitted Autumn 2020BioShaimaa is a graduate of the Ph.D. program, the Department of Electrical Engineering at Stanford. 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.
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Thomas Barba, MD, PhD
Postdoctoral Scholar, Biomedical Informatics
BioI am 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, I am mainly interested in rare autoimmune diseases such as systemic lupus erythematosus (SLE).
My postdoctoral 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 Görkem Er
Graduate Visiting Researcher Student, Biomedical Informatics
BioAhmet Görkem Er is a visiting student researcher as a Fulbright Ph.D. Dissertation Research Grantee at Stanford. He holds an M.D. degree with a double specialty of internal medicine and infectious diseases and clinical microbiology and is pursuing a Ph.D. in medical informatics at Middle East Technical University (Turkey). He is interested in machine learning approaches in healthcare and working on multi-scale data fusion and radiogenomics in Gevaert's Lab.
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Sajjad Fouladvand, PhD, MSc
Postdoctoral Scholar, Biomedical Informatics
BioSajjad Fouladvand, PhD, MSc is a postdoctoral scholar at Stanford Center for Biomedical Informatics Research. Dr. Fouladvand's research career thus far has been focused on developing and applying artificial intelligence (AI) algorithms to solve real-world healthcare problems. Prior to Stanford, he worked at the Institute for Biomedical Informatics at the University of Kentucky (UK) while completing his PhD in Computer Science. During this time, he also received training at Mayo Clinic’s Department of Artificial Intelligence and Informatics as an intern.
While at UK as a PhD candidate, he developed a deep learning model based on transformer and Long Short-Term Memory (LSTM) models to analyze multi-stream healthcare data for prediction of opioid use disorder (OUD). While at Mayo Clinic as an intern, he created a LSTM based framework to predict progression from cognitively unimpaired to mild cognitive impairment in an aging population. In his new role at Stanford, Dr. Fouladvand is involved in conducting AI and healthcare data science research in close collaboration with clinicians, scientists, and healthcare systems with access to deep clinical data warehouses and broad population health data sources. -
Zepeng Huo
Postdoctoral Scholar, Biomedical Informatics
BioConducting research on Foundation Models for medicine
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Behzad Naderalvojoud
Postdoctoral Scholar, Biomedical Informatics
BioBehzad Naderalvojoud is a Postdoctoral Research Fellow at Stanford Center for Biomedical Informatics Research. He received his Ph.D. degree in Computer Science at Hacettepe University, Turkey, in 2020. He is immersed in the fields of machine learning, deep learning, natural language understanding, and Big data analytics and works on health knowledge discovery platforms that transfer Big Health data from volume-based to value-based by generating relational knowledge leading to innovative treatments, predictive therapeutic outcomes, and early diagnosis. He was the leader of many industrial AI projects in the fields of healthcare intelligence and information management in the Eureka cluster programs.
Dr. Naderalvojoud has published several papers in the field of natural language understanding by working on word sense disambiguation, sentiment analysis, neural word embeddings, and deep learning models through national and international projects.
He is currently working on the funded NLM grant project "Advancing Knowledge Discovery for Postoperative Pain Management" under the supervision of Dr. Tina Hernandez-Boussard. He develops descriptive, predictive, and analytical tools using OMOP CDM for postoperative pain research to facilitate timely generation of evidence across multiple populations and settings.
