Stanford University
Showing 21-30 of 113 Results
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Pascal Geldsetzer
Assistant Professor of Medicine (Primary Care and Population Health) and, by courtesy, of Epidemiology and Population Health
BioPascal Geldsetzer is an Assistant Professor of Medicine in the Division of Primary Care and Population Health and, by courtesy, in the Department of Epidemiology and Population Health. He is also affiliated with the Department of Biomedical Data Science, Department of Health Policy, King Center for Global Development, and the Stanford Centers for Population Health Sciences, Innovation in Global Health, and Artificial Intelligence in Medicine & Imaging.
His research focuses on identifying and evaluating the most effective interventions for improving health at older ages. In addition to leading several randomized trials, his methodological emphasis lies on the use of quasi-experimental approaches to ascertain causal effects in large observational datasets, particularly in electronic health record data. He has won an NIH New Innovator Award (in 2022), a Chan Zuckerberg Biohub investigatorship (in 2022), and two NIH R01 grants as Principal Investigator (both in 2023). -
Michael Genesereth
Associate Professor of Computer Science
BioGenesereth is most known for his work on Computational Logic and applications of that work in Enterprise Management, Computational Law, and General Game Playing. He is one of the founders of Teknowledge, CommerceNet, Mergent Systems, and Symbium. Genesereth is the director of the Logic Group at Stanford and the founder and research director of CodeX - the Stanford Center for Legal Informatics.
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Michael Gensheimer
Clinical Associate Professor, Radiation Oncology - Radiation Therapy
Current Research and Scholarly InterestsIn addition to my clinical research in head and neck and lung cancer, I work on the application of computer science and machine learning to cancer research. I develop tools for analyzing large datasets to improve outcomes and safety of cancer treatment. I developed a machine learning prognostic model using data from around 13,000 patients with metastatic cancer which performs better than traditional models and physicians [PubMed ID 33313792]. We recently completed a prospective randomized study in thousands of patients in which the model was used to help improve advance care planning conversations.
I also work on the methods underpinning observational and predictive modeling research. My open source nnet-survival software that allows use of neural networks for survival modeling has been used by researchers internationally. In collaboration with the Stanford Research Informatics Center, I examined how electronic medical record (EMR) survival outcome data compares to gold-standard data from a cancer registry [PubMed ID 35802836]. The EMR data captured less than 50% of deaths, a finding that affects many studies being published that use EMR outcomes data. -
Andrew Gentles
Assistant Professor (Research) of Pathology, of Medicine (BMIR) and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsComputational systems biology
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Paul George, MD, PhD
Associate Professor of Neurology (Adult Neurology) and, by courtesy, of Neurosurgery
Current Research and Scholarly InterestsCONDUCTIVE POLYMER SCAFFOLDS FOR STEM CELL-ENHANCED STROKE RECOVERY:
We focus on developing conductive polymers for stem cell applications. We have created a microfabricated, polymeric system that can continuously interact with its biological environment. This interactive polymer platform allows modifications of the recovery environment to determine essential repair mechanisms. Recent work studies the effect of electrical stimulation on neural stem cells seeded on the conductive scaffold and the pathways by which it enhances stroke recovery Further understanding the combined effect of electrical stimulation and stem cells in augmenting neural repair for clinical translational is a major focus of this research going forward.
BIOPOLYMER SYSTEMS FOR NEURAL RECOVERY AND STEM CELL MODULATION:
The George lab develops biomaterials to improve neural recovery in the peripheral and central nervous systems. By controlled release of drugs and molecules through biomaterials we can study the temporal effect of these neurotrophic factors on neural recovery and engineer drug delivery systems to enhance regenerative effects. By identifying the critical mechanisms for stroke and neural recovery, we are able to develop polymeric technologies for clinical translation in nerve regeneration and stroke recovery. Recent work utilizing these novel conductive polymers to differentiate stem cells for therapeutic and drug discovery applications.
APPLYING ENGINEERING TECHNIQUES TO DETERMINE BIOMARKERS FOR STROKE DIAGNOSTICS:
The ability to create diagnostic assays and techniques enables us to understand biological systems more completely and improve clinical management. Previous work utilized mass spectroscopy proteomics to find a simple serum biomarker for TIAs (a warning sign of stroke). Our study discovered a novel candidate marker, platelet basic protein. Current studies are underway to identify further candidate biomarkers using transcriptome analysis. More accurate diagnosis will allow for aggressive therapies to prevent subsequent strokes. -
Margot Gerritsen
Professor of Energy Resources Engineering, Emerita
Current Research and Scholarly InterestsResearch
My work is about understanding and simulating complicated fluid flow problems. My research focuses on the design of highly accurate and efficient parallel computational methods to predict the performance of enhanced oil recovery methods. I'm particularly interested in gas injection and in-situ combustion processes. These recovery methods are extremely challenging to simulate because of the very strong nonlinearities in the governing equations. Outside petroleum engineering, I'm active in coastal ocean simulation with colleagues from the Department of Civil and Environmental Engineering, yacht research and pterosaur flight mechanics with colleagues from the Department of Mechanical and Aeronautical Engineering, and the design of search algorithms in collaboration with the Library of Congress and colleagues from the Institute of Computational and Mathematical Engineering.
Teaching
I teach courses in both energy related topics (reservoir simulation, energy, and the environment) in my department, and mathematics for engineers through the Institute of Computational and Mathematical Engineering (ICME). I also initiated two courses in professional development in our department (presentation skills and teaching assistant training), and a consulting course for graduate students in ICME, which offers expertise in computational methods to the Stanford community and selected industries.
Professional Activities
Senior Associate Dean, School of Earth, Energy and Environmental Sciences, Stanford (from 2015); Director, Institute for Computational and Mathematical Engineering, Stanford (from 2010); Stanford Fellow (2010-2012); Magne Espedal Professor II, Bergen University (2011-2014); Aldo Leopold Fellow (2009); Chair, SIAM Activity group in Geosciences (2007, present, reelected in 2009); Faculty Research Fellow, Clayman Institute (2008); Elected to Council of Society of Industrial and Applied Mathematics (SIAM) (2007); organizing committee, 2008 Gordon Conference on Flow in Porous Media; producer, Smart Energy podcast channel; Director, Stanford Yacht Research; Co-director and founder, Stanford Center of Excellence for Computational Algorithms in Digital Stewardship; Editor, Journal of Small Craft Technology; Associate editor, Transport in Porous Media; Reviewer for various journals and organizations including SPE, DoE, NSF, Journal of Computational Physics, Journal of Scientific Computing, Transport in Porous Media, Computational Geosciences; member, SIAM, SPE, KIVI, AGU, and APS -
Olivier Gevaert
Associate Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsMy lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. We primarily use methods based on regularized linear regression to accomplish this. We primarily focus on applications in oncology and neuroscience.
