Celebrating GGEB Graduates

Please join us in our celebration as we highlight our GGEB graduates.
The profiles are sectioned by degree type:


Doctor of Philosophy

Kyle Coleman

Kyle Coleman
 Mentor: Mingyao Li, PhD
Biostatistics Program
Saul Winegrad Award for Outstanding Dissertation

Thesis Title: Machine learning methods for the analysis of multi-modal spatial omics data

Thesis Title: Machine learning methods for the analysis of multi-modal spatial omics data

Emily Getzen

Emily Getzen
 Mentor: Qi Long, PhD
Biostatistics Program
Dr. Andy Binns Award for Outstanding Service to Graduate and Professional Student Life

Thesis Title: Advancing trustworthy statistical and machine learning methods for complex electronic health records data
Mentor Comment: Through her phd training, Emily has grown into an exceptional independent researcher and critical thinker. She has exceled in data science and ML/AI research towards the goal of advancing health for all and has also been dedicated to exerting positive impact on our community. She is one of the most generous and kind persons that I have ever known. Emily is poised to become a leader in health data science and AI who cares deeply about her work, colleagues and community. I eagerly look forward to hearing her continued success in the next phase of her career and beyond.

Thesis Title: Advancing trustworthy statistical and machine learning methods for complex electronic health records data
Mentor Comment: Through her phd training, Emily has grown into an exceptional independent researcher and critical thinker. She has exceled in data science and ML/AI research towards the goal of advancing health for all and has also been dedicated to exerting positive impact on our community. She is one of the most generous and kind persons that I have ever known. Emily is poised to become a leader in health data science and AI who cares deeply about her work, colleagues and community. I eagerly look forward to hearing her continued success in the next phase of her career and beyond.

Gary Hettinger

Gary Hettinger
 Mentor: Nandita Mitra, PhD
Biostatistics Program

Thesis Title: Causal Inference Methods to Evaluate Health Policies with Spillover
Research and Lab Description: My research focuses on developing causal inference methodology for observational data with applications in health policy and medical decision making.​​​​​​​
Post PhD Plans: Assistant Prof. of Biostatistics, NYU
Mentor Comment: I have had the distinct pleasure of working with Gary over the past several years and have been continually impressed by his intellectual curiosity, technical rigor, and collaborative spirit. Gary is a rare blend of theoretical depth and applied insight—he brings strong methodological training in statistics to bear on pressing, real-world problems in public health and medicine. His substantial contributions to the field of causal inference, particularly in the development of innovative methods for health policy evaluations, mark him as a rising star in our discipline. Thoughtful, generous, and endlessly inquisitive, Gary exemplifies the very best of our field. I’m thrilled to see him launch his independent research career as an Assistant Professor of Biostatistics at NYU!

Thesis Title: Causal Inference Methods to Evaluate Health Policies with Spillover
Research and Lab Description: My research focuses on developing causal inference methodology for observational data with applications in health policy and medical decision making.​​​​​​​
Post PhD Plans: Assistant Prof. of Biostatistics, NYU
Mentor Comment: I have had the distinct pleasure of working with Gary over the past several years and have been continually impressed by his intellectual curiosity, technical rigor, and collaborative spirit. Gary is a rare blend of theoretical depth and applied insight—he brings strong methodological training in statistics to bear on pressing, real-world problems in public health and medicine. His substantial contributions to the field of causal inference, particularly in the development of innovative methods for health policy evaluations, mark him as a rising star in our discipline. Thoughtful, generous, and endlessly inquisitive, Gary exemplifies the very best of our field. I’m thrilled to see him launch his independent research career as an Assistant Professor of Biostatistics at NYU!

Jeremy Rubin

Jeremy Rubin
 Mentor: Jarcy Zee, PhD
Biostatistics Program

Thesis Title: Statistical Methods for Variable Selection and Prediction with Pathomic Features
Research and Lab Description: For his dissertation, Dr. Rubin focused on developing and applying statistical models to predict kidney function outcomes using image features from kidney biopsy scans. In Fall 2025, he will be starting as a Clinical Assistant Professor of Biostatistics at the University of Maryland, College Park.
Post PhD Plans: Research Scientist, Institute of Health Metrics and Evaluation (IHME) at the University of Washington
Mentor Comment:​​​​​​​ It has been a great pleasure to observe Jeremy’s incredible development as a researcher over the past few years. His unique talents in forging connections, identifying key resources, and synthesizing information have enriched his work and inspired his abundant and insightful ideas. He has been impressively productive with contributions to numerous research projects in a short time, and he makes every presentation and interaction engaging and enjoyable. I wish him all the best in his new role as Clinical Assistant Professor of Biostatistics at the University of Maryland College Park.

Thesis Title: Statistical Methods for Variable Selection and Prediction with Pathomic Features
Research and Lab Description: For his dissertation, Dr. Rubin focused on developing and applying statistical models to predict kidney function outcomes using image features from kidney biopsy scans. In Fall 2025, he will be starting as a Clinical Assistant Professor of Biostatistics at the University of Maryland, College Park.
Post PhD Plans: Research Scientist, Institute of Health Metrics and Evaluation (IHME) at the University of Washington
Mentor Comment:​​​​​​​ It has been a great pleasure to observe Jeremy’s incredible development as a researcher over the past few years. His unique talents in forging connections, identifying key resources, and synthesizing information have enriched his work and inspired his abundant and insightful ideas. He has been impressively productive with contributions to numerous research projects in a short time, and he makes every presentation and interaction engaging and enjoyable. I wish him all the best in his new role as Clinical Assistant Professor of Biostatistics at the University of Maryland College Park.

Jenny Shen

Jenny Shen
 Mentor: Kristin Linn, PhD; Rebecca Hubbard, PhD
Biostatistics Program

Thesis Title: Robust and Replicable Statistical Methods for Incomplete Data with Complex Observational Patterns
Post PhD Plans: Senior Statistician, Spark Therapeutics, Inc.
Mentor Comment: I had the great pleasure of working with Jenny for the past several years on her dissertation research. Jenny brought a combination of independence, curiosity, and determination to her work, and she consistently made progress towards her goals. Jenny took on several complex, real-world problems for which she developed thoughtful, impactful solutions. I wish her all the best in her next steps as a PhD biostatistician!

Thesis Title: Robust and Replicable Statistical Methods for Incomplete Data with Complex Observational Patterns
Post PhD Plans: Senior Statistician, Spark Therapeutics, Inc.
Mentor Comment: I had the great pleasure of working with Jenny for the past several years on her dissertation research. Jenny brought a combination of independence, curiosity, and determination to her work, and she consistently made progress towards her goals. Jenny took on several complex, real-world problems for which she developed thoughtful, impactful solutions. I wish her all the best in her next steps as a PhD biostatistician!

Dazheng Zhang

Dazheng Zhang
 Mentor: Yong Chen, PhD
Biostatistics Program

Thesis Title: Empowering RCT with multi-site multi-source RWD: a statistical learning perspective

Thesis Title: Empowering RCT with multi-site multi-source RWD: a statistical learning perspective


Combined Degree, MD-PhD

Fengling Hu

Fengling Hu
 Mentor: Taki Shinohara, PhD
Biostatistics Program

Thesis Title: Statistical Methods for Analysis of High-Dimensional Neuroimaging Data

Thesis Title: Statistical Methods for Analysis of High-Dimensional Neuroimaging Data


Master of Science

Nina Alfaro

Nina Alfaro
 Mentor: Alisa Stephens-Shields, PhD
Biostatistics Program

Meghan Gerety

Meghan Gerety
 Mentor: Jarcy Zee, PhD
Biostatistics Program

Mentor Comment: It has been truly inspiring to witness Meghan’s exceptional capabilities as a biomedical researcher. Her intellectual curiosity, deep engagement with technical literature, level of independence, meticulous and rigorous implementation of complex statistical techniques, and outstanding oral and written communication skills are remarkable. Her research will directly impact clinical care and shape the trajectory of future research, underscoring her exceptional talent. I am so excited to follow her work throughout her biostatistics PhD and beyond.

Mentor Comment: It has been truly inspiring to witness Meghan’s exceptional capabilities as a biomedical researcher. Her intellectual curiosity, deep engagement with technical literature, level of independence, meticulous and rigorous implementation of complex statistical techniques, and outstanding oral and written communication skills are remarkable. Her research will directly impact clinical care and shape the trajectory of future research, underscoring her exceptional talent. I am so excited to follow her work throughout her biostatistics PhD and beyond.

Noah Hillman

Noah Hillman
 Mentors: Taki Shinohara, PhD; Haochang Shou, PhD
Biostatistics Program

Research and Lab Description: My research focuses on developing statistical methods for neuroimaging data, with a specific emphasis on network enrichment testing and image harmonization.

Research and Lab Description: My research focuses on developing statistical methods for neuroimaging data, with a specific emphasis on network enrichment testing and image harmonization.

Xinyao Jian

Xinyao Jian
 Mentor: Yong Chen, PhD
Biostatistics Program

Post MS Plans: Biostatistics PhD Student at the University of Pennsylvania

Post MS Plans: Biostatistics PhD Student at the University of Pennsylvania

Chunyu Luo

Chunyu Luo
 Mentor: Mingyao Li, PhD
Biostatistics Program

Minh Phan

Minh Phan
 Mentor: Doug Schaubel, PhD
Biostatistics Program

Kewen Qu

Kewen Qu
 Mentor: Qi Long, PhD
Biostatistics Program

Post MS Plans: Biostatistics PhD Student at Columbia University
Mentor Comment: Kewen has exceled as a masters student in our biostatistics program. She has been highly productive in both statistical methods and collaborative research, due in no small part to her ability of taking initiative in learning and in research. I am very happy for her as Kewen was admitted to the phd program in Biostatistics at Columbia. There is no doubt in mind that she will thrive at Columbia and in your future career.

Post MS Plans: Biostatistics PhD Student at Columbia University
Mentor Comment: Kewen has exceled as a masters student in our biostatistics program. She has been highly productive in both statistical methods and collaborative research, due in no small part to her ability of taking initiative in learning and in research. I am very happy for her as Kewen was admitted to the phd program in Biostatistics at Columbia. There is no doubt in mind that she will thrive at Columbia and in your future career.

Emily Wang

Emily Wang
 Mentor: Doug Schaubel, PhD
Biostatistics Program

Ruofan Wang

Ruofan Wang
 Mentor: Jinbo Chen, PhD
Biostatistics Program

Binghao Yan

Binghao Yan
 Mentor: Hongzhe Li, PhD
Biostatistics Program

Research and Lab Description: Optimal transport and its application to single cell genomics.

Research and Lab Description: Optimal transport and its application to single cell genomics.

Thomas Yu

Thomas Yu
 Mentor: Li Shen, PhD, FAIMBE
Biostatistics Program

Research and Lab Description: My research focuses on developing machine learning and deep learning models to better understand and treat Alzheimer’s disease.

Research and Lab Description: My research focuses on developing machine learning and deep learning models to better understand and treat Alzheimer’s disease.

Yue Yu

Yue Yu
 Mentor: Jin Jin, PhD
Biostatistics Program

Mentor Comment: I am lucky to have Yue as the first student who completed the two-year Master training in my lab. I truly enjoyed working with such a kind, highly motivated, hardworking, and talented student with great potential for contributing to statistical genomic research during her future PhD training. I wish you all the best, Yue!

Mentor Comment: I am lucky to have Yue as the first student who completed the two-year Master training in my lab. I truly enjoyed working with such a kind, highly motivated, hardworking, and talented student with great potential for contributing to statistical genomic research during her future PhD training. I wish you all the best, Yue!