Principal Investigator:Lorin Crawford is an Assistant Professor of Biostatistics, and a member of the Center for Statistical Sciences (CSS) and Center for Computational Molecular Biology (CCMB) at Brown University. Before joining Brown, he 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]
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 Massachusetts Institute of Technology (MIT), working on various quantiative 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.
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 student 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.
Tim Sudijono is currently a senior who holds a concentration in the Division of Applied Mathematics. His research interests broadly include probability theory, and developing methods at the intersection of topological data analysis (TDA) and statistics.
Isabella Ting is a current sophomore in the Department of Computer Science at Brown University. Her research interests broadly deal with developing scalable statistical and machine learning-based methods for genome-wide association studies (GWAS).