Epidemiology & Biostatistics (GGEB)
Please join us in our celebration as we highlight our GGEB graduates.
The profiles are sectioned by degree type:
Doctor of Philosophy
Thesis Title: Statistical Techniques for Addressing the Clinico-Radiological Paradox in Multiple Sclerosis
Post PhD Plans: Assistant Professor, Columbia University
Mentor Comment: Collaborating with Jordan over the past few years has been an incredible adventure. I’ve learned so much, and have enjoyed working together immensely. The broad, innovative, and highly impactful nature of Jordan’s work, as well as his remarkable facility with communicating to diverse audiences will ensure his continued success.
Thesis Title: Statistical Methods for Allele-Specific Expression Analysis by RNA Sequencing
Post PhD Plans: Mathematical Statistician, USDA
Mentor Comment: We were fortunate to have the opportunity of working with Jiaxin in the last few years and seeing her growing into an independent researcher. She is always calm and patient, highly motivated and responsible, and is fearless in learning new things. We wish her all the best for her job at the FDA!
Thesis Title: Bayesian Balance Regression and Mediation Analysis for Microbiome Compositional Data
Post PhD Plans: Machine Learning Scientist, Uber
Mentor Comment: Lu worked hard to balance her dissertation work and being a great mother. I can imagine how difficult it was to be excellent in both. However, I am so happy that Lu made it! She made a great progress in developing complex Bayesian inference methods for balance regression and balance mediation analysis for analysis of microbiome compositional data. Wish you all the best, Lu.
Thesis Title: Statistical Inference for High Dimensional Models in Genomics and Microbiome
Post PhD Plans: Principal Statistical Consultant, Novartis Pharmaceuticals
Mentor Comment: I truly enjoyed working with Jiarui during his PhD study at Penn and seeing him making progress in his research. He made a significant contribution in statistical inference of regression models with coefficient constraints and high dimensional instrumental regression models for integrative genomics analysis. Jiarui is well-rounded with a charming personality. Best of luck, Jiarui.
Thesis Title: Investigating the Effect of Individual-Level and Neighborhood-Level Exposures on Delivery Outcomes
Research and Lab Description: I investigated the effect of individual-level and neighborhood-level exposures and their roles on adverse delivery outcomes, including severe maternal morbidity and post-induction cesarean delivery. I also developed a novel algorithm for large Electronic Health Record datasets to determine whether a patient has experienced residential mobility during pregnancy, or other time period of interest, solving a methodological problem in geo-spatial studies.
Post-PhD Plans: Epidemic Intelligence Service Officer, The CDC
Mentor Comment: It has been my great pleasure working with Jessi (now Dr. Meeker) these past three years. She has all of the characteristics of both an outstanding academic and public health expert. She is an excellent scientist, innovative, highly motivated and passionate with an intense devotion to improving women's health for future generations. She has a very bright future ahead of her and I wish her all the best as a new Fellow of the Epidemic Intelligence Service at the CDC!
Thesis Title: Bayesian Nonparametric Models for Causal Inference and Clustering under Dirichlet Process Priors
Research and Lab Description: My dissertation combines causal reasoning, nonparametric modeling, and efficient computing to build practical data science tools for researchers. We develop a set of flexible Bayesian models tailored for estimating causal effects with observational data. Unlike classical statistical models, these do not impose rigid, unrealistic assumptions on the data.
Post-PhD Plans: Assistant Professor, Brown University
Mentor Comment: It was a great pleasure working with Arman these past few years. He has all the characteristics of a great academic. He’s a deep and critical thinker, innovative, a great communicator, and highly motivated. He has a very bright future ahead of him and I wish him all the best in his new job as an Assistant Professor at Brown.
Thesis Title: Statistical Approaches to Address Correlated Measurement Error in a Failure-Time Outcome and Covariates
Post PhD Plans: Assistant Research Professor, University of Virginia
Thesis Title: Statistical Methods for Multi-Modal Image Analysis with Applications in Multiple Sclerosis and Neurodevelopment
Research and Lab Description: My research lies in the development of statistical methods for analyzing neuroimaging data sets with the goal of understanding disease processes in the brain. My projects involved the analysis of multi-sequence structural MRI to study neurodevelopment and multiple sclerosis. I collaborated regularly with experts in bioengineering, radiology, and neurology. Beyond my statistical work in neuroimaging, Ilove building software tools that make data science better, faster, and more accessible.
Post PhD Plans: Statistical Scientist, Genentech
Mentor Comment: Russell Shinohara: Working with Ali has been a great pleasure and wonderful learning experience for me. She is exceptionally bright and remarkably motivated, and was an incredibly collaborative member of our graduate program and research group. Her commitment to improving health by leveraging her quantitative talents and extensive research experience will ensure her bright future as a Statistical Scientist at Genentech.
Kristin Linn: Working with Ali over the past few years was a highlight of my time at Penn. Her enthusiasm for imaging and statistics was inspiring and she always brought creative ideas, computing wizardry, and humor to our weekly meetings. I am incredibly proud of Ali’s many achievements and wish her all the best in her new career at Genentech!
Thesis Title: Statistical Methods for Phenotyping with Positive-Only Electronic Health Record Data
Post PhD Plans: Senior Biostatistician, Johnson & Johnson
Mentor Comment: Lingjiao worked with me on both her MS thesis and dissertation research. It was a truly enjoyable experience. She was a highly motivated student, a great thinker, a hard worker, a best team player, and a good friend. She joined Johnson & Johnson as a senior biostatistician right after her graduation, and she recently shared with me the news that she already received a promotion within her first year there!
Combined Degree, VMD-PhD
Thesis Title: AntiMicrobial Use and Resistance: Intersections of Companion Animal and Public Health
Post PhD Plans: Completing her VMD at Penn Vet
Thesis Title: Enhancing Electronic Health Data for Population Health Studies
Research and Lab Description: In my dissertation research, I examined methods to enhance electronic health record (EHR)-derived data for population health studies, including through the integration of external sources of secondary data on the social and physical environment and the direct measurement of individual-level pollution exposure through personal sensing. We found that the integration of secondary data variables to EHR data is useful for providing health-related context, and, pending improvements to sensor design, personal pollution sensing has the potential to improve exposure capture in the EHR.
Post-PhD Plans: Completing her VMD at Penn Vet
Mentor Comment: Sherrie is an awesome scientist! Always ready to try something new with enthusiasm, very intelligent, careful in her work, committed to finishing projects, and she has a bright personality. It has been wonderful to watch her gain independence as a student that is usual for academics later in their careers. I hope Sherrie enjoys completing veterinary school and look forward to all she will accomplish in the future!
Master of Science
Research Project Title: Atlas-Based Intensity Normalization of the Thalamus on a 7-Tesla Magnetic Resonance Imaging
Research Project Title: Deep Learning for Segmentation of Multiple Sclerosis Brain Lesions
Research Project Title: Estimating the Effect Size of a Hidden Causal Factor Between SNPS and a Continuous Trait: A Mediation Model Approach
Research Project Title: Whole-Brain Analysis of a Crossover Study of Intermittent Theta Burst Stimulation (ITBS) for Affective Disorders
Research Project Title: Mining for Equitable, Intelligent Health: Assessing the Impact of Missing Data in RAW Electronic Health Records
Research Project Title: Applying Active Learning to Phenotyping Electronic Health Records
Research Project Title: Latency of the Bold Response Following Single Pulse Transcranial Magnetic Stimulation
Research Project Title: Modeling Post-Holiday Surge in COVID Cases in Pennsylvania
Research Project Title: Integrative Analysis of Single-Cell Multi-Omics Data: Evaluation of Surface Protein Imputation Methods Using SCRNA-SEQ
Research Project Title: Integrative Analysis of Single-Cell and Bulk RNA Sequencing Data for Cardiometabolic Diseases
Research Project Title: Advancing Timely and Reliable Evidence Synthesis for COVID-19 Research by Including Preprints in Systematic Reviews
Research Project Title: Statistical Modeling vs. Machine Learning in Forecasting SARS-COV-2 Transmission