Principal Investigator:

Lorin Crawford is a Senior Researcher at Microsoft Research New England. He also holds a faculty position as the RGSS Assistant Professor of Biostatistics at Brown University with an affiliation in the Center for Computational Molecular Biology. Prior to joining both MSR and 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. [Personal Website] [CV]

Graduate Students:

Ashley Mae Conard is a Computational Biology PhD candidate and NSF Graduate Research Fellow at Brown University. Her research focuses on building accessible temporal and multi-omics Bayesian inference methods to study gene regulation. During her Brown Master’s in Computer Science, she focused on modeling subclonal drivers in cancer. During that time, she served on the Board of Trustees for the Anita Borg Institute and as Finance Chair for the ISCB Student Council. Ashley was a 2015 Fulbright Research Scholar using game theory to identify protein binding preferences. She is on the Board of Trustees for the Fulbright Association. She holds a Computer Science and Biochemistry degree from DePauw University, and spent her summers doing research projects at MIT Lincoln Laboratory, Vanderbilt University, and Eli Lilly.

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.

Wai Shing Tang is a PhD student in the Department of Physics at Brown University. Prior to starting grad school, Wai Shing graduated from the Chinese University of Hong Kong with a BSc in Physics. Wai Shing has broad experiences in experimental and computational biophysics, with ongoing research work related to bacterial motility and protein MD simulations. His current research interests include topological data analysis of biological systems such as protein and intercellular structures.

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.

Patricia Vera-González is an ScM student in the Department of Biostatistics at Brown University’s School of Public Health. She received her undergraduate degree in Mathematics at the University of Puerto Rico, Río Piedras Campus. Patricia's research interests include mathematical modeling in biology, applying statistical and machine learning methods to genome wide association studies (GWAS), and pharmacogenomics. She’s particularly interested in applying these methods to multi-ethnic and minority populations.

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]

Alexandra (Alex) Wong is an MD-PhD candidate at the Warren Alpert Medical School of Brown University. Alex graduated from Brown University with a ScB in Computer Science and an AB in Biology. Before matriculating to medical school, she worked as a software engineer at Yelp and managed her nonprofit, Fiction for Kids. Alex's research interests broadly include developing statistical models and applying machine learning algorithms towards cancer genomics and pharmacology.

Undergraduate Researchers:

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 and Affiliated Alumni: