The Crawford research lab consists of a group of statisticians, computational biologists, and applied mathematicians developing novel and efficient methodologies to address complex problems in quantitative genetics, cancer pharmacology, molecular genomics, and radiomics (e.g. cancer imaging studies). Our current works are often motivated by issues that arise when attemping to statistically model massive data sets with high-throughput molecular information, particularly as it pertains to functional genomics and cellular-based biological processes. As a group, we look to continue to provide innovative tools that better address key computational hurdles faced when analyzing and dissecting genotypic and phenotypic variation.

1. COBRE Center for Central Nervous System Function
   P20GM103645 (PI Sanes)
   NIH/NIGMS
   Summary: The proposed center will investigate the mechanisms of higher brain function, with a focus on attention, decision-making, action, and disorders that modify these key systems, using a combination of genetic, behavior, and systems neuroscience approaches.
   Role: Core B Co-Investigator
   Date: 08/13 - 07/23

2. PCRP Idea Development Award
   W81XWH-18-1-018 (PI Somarelli)
   DoD/PCRP
   Title: Targeting the p38/Snail/PD-L1 axis in Hormone-therapy Resistance and Metastasis
   Summary: The proposed work is to develop technology for both civil and military application. This subcontract involves providing expertise in developing statistical methodology.
   Role: Co-Investigator
   Date: 10/18 - 09/21

3. COBRE CBHD Institutional Pilot Award (IDeA Cost Share Award)
   P20GM109035 (PI Rand)
   NIH/NIGMS
   Project Title: Deep learning Methods for Fine Mapping and Discovery in Genomic Association StudiesSummary: During this time period, we will develop a suite of novel probabilistic machine learning and deep neural networks tools for fine mapping and discovery in genomic sequencing studies. Specifically, (i) develop an interpretable significance measure for probabilistic machine learning methods, (ii) develop a unified deep learning framework for gene-level and pathway enrichment analysis in genome-wide association studies, and (iii) create distributable software and use it to characterize nonlinear genetic effects at multiple genomic scales in real data applications.
   Role: Principal Investigator of Project
   Date: 03/19 - 02/21

4. Alfred P. Sloan Research Fellowship
   Computational & Evolutionary Molecular Biology (PI Crawford)
   Alfred P. Sloan Foundation
   Project Title: Interpretable Machine Learning Methods for Genome-wide Association Mapping
   Summary: The goal of this project is to build machine learning algorithms and statistical tools that aid in the understanding of how nonlinear interactions between genetic features affect the architecture of complex traits and contribute to disease etiology. A key theme of this work will be to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles.
   Role: Principal Investigator
   Date: 09/19 - 09/21

Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award
International Association for Intelligent Biology and Medicine (IAIBM) Eminent Scholar Award
Mathematical Association of America & National Association of Mathematicians Lecturer
Kavli Fellow of the National Academy of Sciences, Frontiers of Science Symposium
Cell Press: 1000 Inspiring Black Scientists in America
David & Lucille Packard Foundation Fellowship for Science and Engineering
Mathematically Gifted & Black: Black History Month Honoree 2020
The Root 100: Most Influential African Americans in 2019
Endowed Assistant Professorship (Brown University) 
Alfred P. Sloan Research Fellowship
Forbes 30 Under 30 Class of 2019: Science
ISBA Savage Award in Applied Methodology

Center for Statistical Sciences (CSS)

Center for Computational Molecular Biology (CCMB)

Robert J. & Nancy D. Carney Institute for Brain Science