Meghana Kamineni

Co-mentored student with Dr. Pradeep Natarajan

Bio: Meghana is a medical student at Harvard Medical School. She is working on using machine learning techniques to identify splenic features in abdominal MRIs that are associated with increased risk for coronary artery disease. She holds a bachelor’s degree in Computer Science from MIT.

Xinyu Wang

Co-mentored student with Dr. Ed Giovannucci

Bio: Xinyu is currently a master’s student in Department of Epidemiology, Harvard T.H. Chan School of Public Health. Her research focuses on integrating multi-omics data to explore the underlying mechanisms of cancers and cardiometabolic diseases. She is also passionate about translating epidemiological findings into clinical practice. She holds dual bachelor’s degrees in Medicine and Economics from Peking University.

Yixuan (Kathy) Liu

Co-mentored student with Dr. Michael Honigberg

Bio: Kathy is a master’s student in Department of Biostatistics, Computational Biology and Quantitative Genetics. Her research leverages multi-omics approaches to investigate proteomic profiles and methylation patterns associated with somatic mutations in clonal hematopoiesis of indeterminate potential (CHIP) and cardiovascular disease (CVD). She holds a dual degree in Molecular Bioscience, Genetics and Genomics from Duke Kunshan University and Duke University.

Zhanqing (Anthea) Hua

Co-mentored student with Dr. Vineet Raghu

Bio: Zhanqing (Anthea) Hua is a Master student in Genetic Epidemiology at the Harvard School of Public Health. Her research centers on integrating deep learning methodologies with genome-wide association studies (GWAS) to advance understanding of human disease. She is committed to exploring the associations between genomic factors and disease outcomes to inform risk prediction and potential interventions. She obtained her Bachelor of Science degree in Biological Sciences from Imperial College London.

Linke Li

Computational associate jointly with Dr. Michael Honigberg

Bio: Linke Li holds a Master’s degree in Biostatistics from Duke University. Her research focuses on integrating deep learning methodologies into statistical genetics to enhance disease risk prediction and identify at-risk populations. She is interested in comparing the disease prediction capabilities of deep learning methods with traditional approaches like genome-wide association studies (GWAS) and polygenic risk scores (PRS). By identifying novel genetic variations, she aims to improve risk prediction models and contribute to preventive healthcare for disease.

Lanyue Zhang

Thesis advisory committee member (advisor - Dr. Marios Georgakis)

Current: Ludwig Maximilian University of Munich