School of Medicine
Showing 1-20 of 55 Results
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Anna Booman
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioAnna Booman, PhD, MS is a Postdoctoral Scholar in the Department of Anesthesiology, Perioperative, and Pain Medicine. She conducts perinatal pharmacoepidemiology research to study the safety and effectiveness of medication use during pregnancy, since most pregnant individuals cannot be included in clinical trials. She uses large observational datasets, such as the Merative MarketScan Database, and complex epidemiologic methods in her research.
Dr. Booman received her PhD in Epidemiology from the Oregon Health & Science University School of Public Health, her MS in Computational Biology and Quantitative Genetics from the Harvard T.H. Chan School of Public Health, and her BS in Mathematical Biology (minor: Computer Science) from the College of William & Mary. Her research has spanned many areas of perinatal epidemiology, including a focus on twin children, rare genetic disorders, gestational weight gain, and insurance discontinuity in pregnancy. -
Austen Brooks Casey
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioAusten Brooks Casey, PhD, is a postdoctoral scholar in the Department of Anesthesiology, Perioperative and Pain Medicine (advisor: Boris Dov Heifets, MD, PhD). He originates from western North Carolina, and has had a long-standing interest in drug discovery for major depression and schizophrenia, which was invigorated by initial coursework in organic chemistry and biochemistry. Austen trained at Northeastern University (advisor: Raymond G. Booth, PhD) where he studied the medicinal chemistry and pharmacology of novel ligands targeting serotonergic G protein-coupled receptors. Currently, he is investigating neural circuits activated by psychedelic drugs, with the long-term goal of using modern techniques in neuroscience to complement drug design efforts toward the development of novel antidepressant and antipsychotic medications.
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Bernard Mawuli Cobbinah
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioCobbinah Bernard Mawuli is a Postdoctoral Scholar at Stanford University in the Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine. He is passionate about the intersection of AI and medicine, focusing on developing robust and effective approaches for preventive and predictive healthcare. His research aims to deepen the understanding of high-dimensional multi-omics medical data using advanced machine learning techniques. By exploring innovative ways to analyze this data, his work contributes to improved treatments and enhanced patient care. Through the analysis of large patient datasets, his goal is to create tools that empower clinicians to make more informed decisions, ultimately improving healthcare outcomes for all.
Prior to joining Stanford, he pioneered robust federated learning techniques for evolving data streams and developed methods to reduce multi-center MRI variability in diagnosing brain disorders. -
Joel Fundaun
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioDr. Joel Fundaun, PT, DPT, PhD, is a physical therapist and researcher specializing in chronic pain, focusing on nerve injuries and neuropathic pain. He earned his PhD in Clinical Neurosciences from the University of Oxford and is now a Postdoctoral Research Fellow at Stanford University. His research combines clinical phenotyping, quantitative neuroimaging, and human biosamples to advance the understanding, diagnosis, and management of chronic pain conditions.
Joel also works clinically as a physical therapist, treating people with chronic pain at Stanford's Pain Management Center. -
Marc Ghanem
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
Current Research and Scholarly InterestsMarc's research focuses on leveraging deep learning to identify clinically relevant patterns within large medical datasets, aiming to deliver personalized and predictive healthcare solutions. Current projects include building comprehensive perioperative foundation model, optimizing neonatal total parenteral nutrition (TPN), analyzing anesthesiology research trends, and identifying differential responders and their characteristics in clinical trials.
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Alex J Goodell
Clinical Scholar, Anesthesiology, Perioperative and Pain Medicine
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain MedicineBioAnesthesiologist and internist interested in artificial intelligence and large language models in medicine. Currently, my primary focus is on developing and evaluating applications of large language models to improve the "user experience" of patients (who spend too much time fighting the system that is tasked with healing them) and doctors (who spend too much time fighting the system that is supposed to help them heal others).
Interests:
- Benchmarking LLMs as clinical calculators
- Medical summarization by LLMs
- Agentic /tool-using language models
- GenerativeAI for Medical Education and Simulation
- Data equity in LLMs
- Novel benchmarks for clinical LLMs, including simulation
- Participatory research, open-source software
I'm a Clinical Scholar in the Dept of Anesthesiology and a Post-Doctoral Fellow in Anesthesiology / Biomedical Data Science in the lab of Nima Aghaeepour.
I completed medical school at the UC Berkeley - UCSF Joint Medical Program, followed by the Combined Internal Medicine/Anesthesiology Residency at the Stanford School of Medicine, and a fellowship in Anesthesia Informatics at the Stanford AIM Lab. -
Tomin James
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioMy work involves designing and developing AI/ML-based algorithms to find answers for cutting-edge problems using multi-disciplinary data. This involves data from space-borne and ground-based instruments for astrophysics and space science studies, high-speed imaging data for behavioral neuroscience experiments, multi-omics data for finding biomarkers affecting population health, clinical data for detecting health anomalies, and EHR data for patient trajectory prediction and personalized medicine.
