People

Administrative Coordinator to Nilanjan Chatterjee, PhD
Bloomberg Distinguished Professor
Johns Hopkins University
Bloomberg School of Public Health
Department of Biostatistics

kphilpo3@jhmi.edu
(Phone) 410-955-3067

Yuzheng Dun is a second-year Ph.D. student in the Department of Biostatistics at Johns Hopkins University. He received his Bachelor's degree in statistics from Huazhong University of Science and Technology. He is interested in Bayesian statistics, statistical computing, and statistical genetics. Currently, I am working on developing statistical methods for multi-ethnic polygenetic risk score.

I am a second-year PhD student in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, where I am advised by Dr. Nilanjan Chatterjee. Before starting my PhD, I earned a B.S. in Applied Mathematics and Statistics, with a second major in Computer Science, from the Johns Hopkins Whiting School of Engineering. My research interests focus on the development and application of statistical methods and computational tools for genetic risk prediction across diverse populations. My current project involves conducting genome-wide association studies (GWAS) in admixed populations by integrating GWAS summary statistics with individual-level data. Additionally, I am exploring methods that leverage functional annotations to enhance the performance and portability of polygenic risk scores (PRSs). My goal is to apply and adapt existing statistical methods to contribute to the development of more precise and equitable healthcare solutions.

Yujie (Phyllis) Wei is a Ph.D. student in Biostatistics at Johns Hopkins University. At Chatterjee lab, she hopes to develop methods to help understand the genetic basis of complex traits and diseases and enhance disease risk prediction models through data integration. She received her bachelor's degree in Mathematics with a minor in Computer Science from Wellesley College.

I am a postdoctoral fellow in Dr. Chatterjee’s lab. I want to find out the mechanisms behind complex human diseases using genetics and genomics data. Specifically, I currently focus on improving the interpretability and applicability of polygenic scores. My research include developing novel statistical methods to jointly estimate gene-environment interactions and correlations in case-control studies, and methods for direct and indirect polygenic effects and GxE interactions in family-based studies. My work also involves the integration of genetics with multi-omics data. Apart from being at my desk in the office, you can also find me in ballet studios. Here is my website: https://ziqiaow.github.io/

Xinyu (Brian) Guo is an ScM student in the Department of Biostatistics at Johns Hopkins University. He obtained his Bachelor’s degree from Washington University in St. Louis. His current research with Nilanjan Chatterjee focuses on integrating the single-cell data with results from genetic studies to understand cell-type-specific effects on diseases. He also developed a subset-based method to detect associations between tissue-set and complex traits. His research interests include genetics and genomics data integration, statistical learning, and genomic reasoning in cancer biology.

I am currently a second-year Ph.D. candidate in the Department of Biostatistics, working with Professors Nilanjan Chatterjee and Elizabeth Ogburn, and my primary research interests are Statistical Genetics, Causal Inference, and Machine Learning. I also have a keen interest in the intersection of Health and Linguistics, exploring how language can influence health outcomes, which I pursue as a hobby. Before beginning my doctoral studies, I completed both my Bachelor’s (BStat) and Master’s (MStat) degrees in Statistics at the Indian Statistical Institute, in 2021 and 2023, respectively. For those interested in connecting, my website at anaghchattopadhyay.github.ioM.

Emily is a third year Ph.D. candidate in Biostatistics. Her research focuses on building better risk prediction models that may ultimately inform clinical decision-making to promote precision prevention equitably across diverse populations. She currently works on building and validating cancer risk prediction models using very large and diverse datasets in order to combine classical risk factors (e.g. age, sex, lifestyle, environment, etc.) and polygenic risk scores into a single model. To support this work, she will also validate new and existing transfer learning techniques - methods that are applied when large data gaps exist - to facilitate combining information across many different data sources. Outside of research, Emily enjoys swing dancing, camping, and crafting.

Charissa is a junior studying biomedical engineering and computer science. She is interested in using data-driven methods to find solutions to complex biomedical problems. Her research aims to analyze large datasets to uncover insights into disease pathways and improve health outcomes.

Hi! I’m Sameer, a sophomore at Johns Hopkins from Northern Virginia majoring in biomedical engineering. I am interested in applying AI and statistical methods to study the function of regulatory elements in the genome and genetic variation.