School of Medicine
Showing 21-40 of 40 Results
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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. -
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 about 400 times per month from the CRAN platform, and the first paper published in 2020 has garnered around 40 citations. His research interests include machine learning, clinical informatics and decision-making, predictive models, electronic health records, and risk stratification in acute care settings.
