Principal Investigator:Lorin Crawford is the RGSS Assistant Professor of Biostatistics, and a core member of the Center for Statistical Sciences (CSS) and Center for Computational Molecular Biology (CCMB) at Brown University. He is also affiliated with the Robert J. and Nancy D. Carney Institute for Brain Science. Before joining Brown, Lorin received his PhD from the Department of Statistical Science at Duke University where he was formerly co-advised by Sayan Mukherjee and Kris C. Wood. As a Duke Dean’s Graduate Fellow and NSF Graduate Research Fellow he completed his PhD dissertation entitled: "Bayesian Kernel Models for Statistical Genetics and Cancer Genomics." Dr. Crawford received his Bachelors of Science degree in Mathematics from Clark Atlanta University. [Faculty Profile] [CV]
Postdoctoral Fellows:Greg Darnell completed his PhD in Quantitative and Computational Biology at Princeton University in 2019 under the mentorship of Professor Barbara Engelhardt. Greg's research interests within statistical genetics include creating machine learning algorithms for understanding the genetic basis of disease. He has developed both Bayesian and deep learning based methods for scaling and adapting traditional genetic association study techniques to high-dimensional traits. Starting September 2019, Greg will be working in the both the Ramachandran lab and the Crawford lab, and will be at the Institute for Computational and Experimental Research in Mathematics (ICERM) from September 2019 to May 2020 as an Institute Postdoctoral Fellow. [Professional Website]
Graduate Students:Pinar Demetci is a Computational Biology PhD student (on the Computer Science track). She graduated from Olin College of Engineering with a BS in Bioengineering and worked on bioinformatic analyses and mathematical models for microbial communities under environmental perturbation. Upon graduation, Pinar spent a year in the Gene-Wei Li Lab at the Broad Institute of Harvard and MIT, working on various quantitative biology projects. Her current research interests broadly include interpretable statistical learning and algorithm design with applications in human genomics and personalized medicine.
Alan DenAdel is a Computational Biology PhD student (on the Applied Mathematics track) and a Brown Graduate School Presidential Fellow. Prior to attending Brown, Alan worked at Illumina as a bioinformatics scientist. As an undergrad, he majored in mathematics (with minors in statistics and biology) at Pacific Lutheran University; and he now holds an MS in Bioinformatics and Genomics from the University of Oregon. Alan's current research interests include statistical theory development, machine learning applications in genomics, and reproducible research.
Chibuikem (Chib) Nwizu is an MD-PhD student at the Warren Alpert Medical School of Brown University, and is currently working to complete his PhD in Computational Biology (on the Computer Science track). Chibuikem graduated from Brown University with an ScB in Applied Mathematics-Biology. His current research interests broadly include topological data analysis and the application of machine learning to clinical diagnostics, cancer genomics, and stem cell biology.
Kexin Qu is a PhD student in the Department of Biostatistics in the School of Public Health. Previously, she graduated from Emory University with a BS in applied math with high honors and an MSPH in biostatistics. Before coming to Brown, Kexin worked as a biostatistician working on various real world health outcome projects. Her research interests include developing computational methods for longitudinal genome-wide association (GWA) studies and building statistical machine learning tools for applications in personalized medicine.
Dana Udwin is a PhD student in the Department of Biostatistics in the School of Public Health. Before moving to Providence, she worked as a data scientist at MassMutual Financial Group while pursuing an MS in Statistics from the University of Massachusetts Amherst. Dana graduated from Smith College with a BA in Mathematics and a minor in Chinese. Her research interests include statistical machine learning and Bayesian methodology.
Emily Winn is a PhD candidate in the Division of Applied Mathematics at Brown University and a National Science Foundation (NSF) Graduate Research Fellow. Emily received her Bachelor of Arts in Mathematics with High Honors from the College of the Holy Cross. She also completed a year in the Visiting Students Programme at St. Edmund Hall at the University of Oxford. Her research interests include statistical topology, topological data analysis, and developing new methods using tools from probability and information theory. [Personal Website]
Undergraduate Researchers:Gabrielle Ferra is a current Goldwater Scholar who is concentrating in Applied Mathematics-Biology at Brown University. Her research interests include using statistical methods to analyze epistatic interactions, and developing computational algorithms to answer problems sitting at the intersection of topological data analysis (TDA) and genetics.
Ashwin Veeramani is a current undergraduate concentrating in Applied Mathematics at Brown University. His research interests include applying statistical/machine learning methods to genome-wide association (GWA) studies and identifying genetic mechanisms that explain how the architecture of complex traits vary across individuals of different ethnic ancestry and social economic backgrounds.
Former Lab Members:
- Zachary Kaplan (Applied Mathematics Undergrad 2019) → Machine Learning Engineer at Schrödinger
- Timothy Sudijono (Applied Mathematics Undergrad 2019) → Portfolio Implementation Analyst at AQR Capital Management
- Isaac Zhao (Biostatistics Masters 2019) → Cancer Genomic Researcher at the National Institutes of Health (NIH)
- Bruce Wang (Data Science Initiative Masters 2018) → PhD Student in Quantitative and Computational Biology at Princeton University