School of Medicine
Showing 141-160 of 164 Results
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Ashley Styczynski
Adjunct Clinical Assistant Professor, Medicine - Infectious Diseases
BioAshley Styczynski, MD, MPH, is an Adjunct Clinical Assistant Professor in the Division of Infectious Diseases & Geographic Medicine and Global Health Faculty Fellow, and a Medical Officer in the International Infection and Control Program at the Centers for Disease Control and Prevention (CDC). Dr. Styczynski's research interests are in infectious disease epidemiology, global health, emerging infections, and antimicrobial resistance. She holds an MPH from Johns Hopkins Bloomberg School of Public Health and an MD from University of Illinois at Chicago. Prior to coming to Stanford for her infectious disease fellowship, she spent two years as an Epidemic Intelligence Service (EIS) Officer at the CDC. During her time as an EIS officer, Dr. Styczynski conducted outbreak investigations on Zika virus, vaccinia virus, and rabies. She is currently conducting research on antimicrobial resistance and interventions to reduce nosocomial infections within low-resource healthcare facilities.
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Minhui Su
Postdoctoral Scholar, Neurology and Neurological Sciences
BioMinhui Su, PhD is a postdoctoral fellow at the Neurology Department. She is investigating neuronal activity-regulated glioma growth, specifically how membrane depolarization regulates glioma growth in the tumor microenvironment.
She obtained her PhD in Molecular Biology, with a focus on neuroimmunology, at the International Max Planck Research School (IMPRS) at Georg August University Göttingen, Germany. Her PhD research discovered that inflammation is an essential early step of myelin regeneration, and uncovered the roles of microglia (the resident immune cells of the central nervous system) in myelin damage response.
She enjoys science, art and hiking in her free time. -
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. -
Han Sun
Postdoctoral Scholar, Genetics
Biostatistician 2, Pediatrics - EndocrinologyBioHan had been a postdoc with Dr. Steinmetz at the genetics department for five years, working on both cancers and heart diseases, trying to understand the mechanisms linking from variants to disease phenotypes. This led to a few very interesting findings of aberrant splicing regulation, such as splicing-mediated readthrough stabilization (SRS), one more mechanism for oncogene activation in multiple types of cancers, and tissue-specific splicing of a mitochondrial inner membrane protein, suggesting a molecular connection between deficiency in energy-supplying and dilated cardiomyopathy.
After being a senior computational biologist with Dr. Gloyn, who has been dedicated to the research of type 2 diabetes for decades, Han switched to the field of this multifactorial metabolic disease. It did take some courage to make such a switch at his post-postdoc stage, however, Han has a consistent interest in studying PG&E, which is not pacific gas and electric nearby, but the interaction between phenotype, genotype, and environment. With years of hands-on experience in statistical modeling and the analysis of next-generation sequencing and mass spectrometry data, in addition to a good understanding of disease genetics, cancer biology, and systems biology, Han is highly confident that he will enjoy the adventure and contribute to our understanding of diabetes. -
Liyan Sun
Postdoctoral Scholar, Radiation Physics
Current Research and Scholarly InterestsPhysics-driven deep learning algorithms for MRI/CT reconstruction and analysis:
(1) MRI acceleration with partial measurements.
(2) Medical image segmentation under limited data resources.
(3) Unsupervised/supervised medical image synthesis for MRI or CT.
(4) Longitudinal medical data analysis with deep learning models.
(5) PET image reconstruction and analysis. -
Samyuktha Suresh
Postdoctoral Scholar, Oncology
Current Research and Scholarly Interests- Exploring the crosstalk between DNA repair mechanisms and protein arginine methyltransferases in triple-negative breast cancer
- Understanding the role of DNA repair enzymes in the context of breast cancer
