School of Medicine
Showing 1-44 of 44 Results
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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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Philip Chung
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioI am a general anesthesiologist and physician-scientist with prior training as an engineer. My areas of research include clinical informatics, natural language processing, machine learning, and artificial intelligence applied to perioperative medicine and anesthesiology. Currently I am a postdoctoral fellow in Nima Aghaeepour's laboratory. See my CV and Biosketch for more information.
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Karlyn Edwards
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
Current Research and Scholarly InterestsAdapting and testing psychological interventions for chronic pain and opioid misuse, identifying patients factors related to psychological and medication treatment responsiveness, latent variable modeling, brief digital psychological interventions, Acceptance and Commitment Therapy, Mindfulness
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Dorien Feyaerts
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
Current Research and Scholarly InterestsBiomedical scientist and immunologist with a strong background in fetal-maternal immunology that aims to conduct impactful translational research in women’s health to improve the health of mothers and their children.
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Marc Ghanem
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
Current Research and Scholarly InterestsData-driven healthcare and AI research in a translational setting.
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Debapriya Hazra
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioI did my Ph.D. in Machine Learning Lab at Jeju National University, South Korea. After pursuing master’s in Computer Application, I worked as a software engineer at Atos Global IT Solutions and Services Pvt. Ltd. in India and Germany. I obtained bachelor’s degree in Computer Science Honors from University of Calcutta, India.
My specialization is in generating synthetic data using generative adversarial networks (GAN) and enhancing classification or prediction accuracy for disease diagnosis. During my Ph.D. studies, I have worked with biomedical signals, microbiomes, microscopic cell images, nucleic acid sequences and also with data from other domains.
I am currently a postdoctoral fellow at Nima Aghaeepour Lab working on machine learning analysis of biomedical data with a focus on generative and predictive modeling. -
Tuuli Maria Hietamies
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
Marketing Intern, Office of Technology Licensing (OTL)BioTuuli Hietamies, PhD, is a postdoctoral researcher at the Department of Anaesthesiology, Perioperative and Pain Medicine. Her research interests include studying psychedelics and utilising these in the context of brain injury and rehabilitation.
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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.
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Theresa Lii, M.D., M.S.
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
Current Research and Scholarly InterestsEvaluating the analgesic and antidepressant effects of ketamine in humans
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Ayesha Sujan
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioAyesha Sujan, PhD, is a postdoctoral scholar in the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University School of Medicine. Before joining Stanford University, she completed a year-long postdoctoral fellowship in the Kaiser Permanente Division of Research, her doctoral training in the Department of Psychological and Brian Sciences at Indiana University – Bloomington, her clinical internship at the Medical University of South Carolina, her master’s degree in Human Development from Cornell University, and her bachelor’s degree from Tulane University. Though her training has focused on psychological science, her training spans multiple disciplines, including epidemiology and pharmacology.
Broadly speaking, she conducts translational research focused on preventing early exposure to risk factors from having adverse consequences on child development. Her research initially focused on early-life adversities, particularly abuse and neglect, and then expanded to include the prenatal period. Though she studies the consequences of a number of pregnancy-related risk factors, her work mainly focuses on prenatal exposure to psychoactive substances (e.g., opioids and antidepressants) and risk for adverse birth outcomes (e.g., preterm birth) and neurodevelopmental problems (e.g., autism spectrum disorder and attention-deficit/hyperactivity disorder). She uses real-world health care data because women cannot be randomly assigned to use psychoactive substances during pregnancy due to ethical concerns about exposing developing offspring to potentially harmful substances. Given that people who use psychoactive substances during pregnancy differ from those who do not, she uses innovative methods that help account for these differences and seeks converging evidence across multiple methods. For example, one method she uses compares children who were exposed during pregnancy to their own siblings who were not exposed. This method accounts for all genetic and environmental factors shared by the siblings and, thus, provides a strong test of the consequences of substance exposure during pregnancy. Her research has important clinical implications. For example, a paper she published in JAMA suggests that adverse outcomes associated with prenatal exposure to antidepressants are largely due to background factors rather than medication exposure itself. This finding could provide reassurance to people considering antidepressant use during pregnancy. Her hope is that her research will inform policies and practices and will, thereby, help improve the health and wellbeing of mothers and their children. -
Erico Tjoa
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
Current Research and Scholarly InterestsI'm working on explainable artificial intelligence (explainable AI, XAI). In particular, I design and study deep learning models that incorporate humanly understandable concepts and conduct research on understanding these complex models. Objectives: to achieve transparency and responsible use of automated systems.
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Feng Xie
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioFeng Xie is currently a postdoctoral scholar at Stanford University School of Medicine, and he recently graduated with a joint Ph.D. degree from Duke University and the National University of Singapore. He previously obtained his bachelor’s degree from Tsinghua University, Beijing, China, in 2017. During his Ph.D. study, he utilized interpretable machine-learning tools in acute and emergency care settings and published eight first-author research papers in high-impact journals with total impact factors of over 60. Specifically, he developed a novel informatics framework called AutoScore, which automatically generates interpretable clinical scores from electronic health records. This open-source software package has been used by local and international researchers, downloaded more than 10,000 times from the CRAN platform, and the first paper published in 2020 has garnered over 60 official citations. His research interests include machine learning, clinical informatics and decision-making, predictive models, electronic health records, and risk stratification in acute care settings.
