I am a Senior Researcher at Microsoft Research New England. I also maintain a faculty position in the School of Public Health as the RGSS Assistant Professor of Biostatistics with an affiliation in the Center for Computational Molecular Biology at Brown University. The central aim of my research program 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. An overarching theme of the research done in the Crawford Lab group is to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles. Some of my most recent work has landed me a place on Forbes 30 Under 30 list and recognition as a member of The Root 100 Most Influential African Americans in 2019. I have also been fortunate enough to be awarded an Alfred P. Sloan Research Fellowship and a David & Lucile Packard Foundation Fellowship for Science and Engineering.
Prior to joining both MSR and Brown, I received my PhD from the Department of Statistical Science at Duke University where I was co-advised by Sayan Mukherjee and Kris C. Wood. As a Duke Dean’s Graduate Fellow and NSF Graduate Research Fellow I completed my PhD dissertation entitled: "Bayesian Kernel Models for Statistical Genetics and Cancer Genomics." I also received my Bachelors of Science degree in Mathematics from Clark Atlanta University.
Interpretability in Machine Learning Methods
Machine learning algorithms have become frequently used in genomic studies because they typically exhibit high predictive accuracy. However, recently, these same algorithms have also become criticized as “black box” techniques. We look to build methods that over this challenge.
Dissecting Genetic Architecture of Complex Traits
The explosion of large-scale genomic datasets has provided the unique opportunity to move beyond the traditional LMM framework within GWAS. We build novel ML methods that exhibit power for complex traits that are driven by non-additive genetic variation (e.g., gene-by-gene interactions).
Modeling 3D Variation with Topological Summaries
It has been a longstanding challenge to implement an analogue of variable selection with 3D shapes as the covariates in a regression model. Here, we develop novel statistical and topological data analytic (TDA) pipelines for sub-image selection where the goal is to identify the physical features of 3D shapes that best explain the variation between two phenotypic classes.
Statistical Methods for Cancer Pharmacology
Targeted therapies aimed to inhibit oncogenic signaling within many cancer subtypes have been proven to have high initial clinical responses, but relapse in these patients is almost inevitable. To better understand this phenomenon, we develop algorithms that define rigorous transcriptional signatures of cancer recurrence and therapeutic resistance.
Key: * authors contributed equally; # corresponding author(s); advisee
- M.C. Turchin#, G. Darnell, L. Crawford#, and S. Ramachandran#. Pathway analysis within multiple human ancestries reveals novel signals for epistasis in complex traits. bioRxiv. 2020.09.24.312421. [Preprint] [Software]
- S. Raghavan, P.S. Winter#, A.W. Navia, H.L. Williams, A. DenAdel, R.L. Kalekar, J. Galvez-Reyes, K.E. Lowder, N. Mulugeta, M.S. Raghavan, A.A. Borah, S.A. Väyrynen, A. Dias Costa, R. W.S. Ng, J. Wang, E. Reilly, D.Y. Ragon, L.K. Brais, A.M. Jaeger, L.F. Spurr, Y.Y. Li, A.D. Cherniack, I. Wakiro, A. Rotem, B.E. Johnson, J.M. McFarland, E.T. Sicinska, T.E. Jacks, T.E. Clancy, K. Perez, D.A. Rubinson, K. Ng, J.M. Cleary, L. Crawford, S.R. Manalis, J.A. Nowak, B.R. Wolpin, W.C. Hahn, A.J. Aguirre#, A.K. Shalek#. Transcriptional subtype-specific microenvironmental crosstalk and tumor cell plasticity in metastatic pancreatic cancer. bioRxiv. 2020.08.25.256214. [Preprint] [SI]
- W. Cheng, G. Darnell, S. Ramachandran, and L. Crawford#. Generalizing variational autoencoders with hierarchical empirical Bayes. arXiv. 2007.10389. [Preprint] [Software]
- P. Demetci*, W. Cheng*, G. Darnell, X. Zhou, S. Ramachandran, and L. Crawford#. Multi-scale inference of genetic architecture using biologically annotated neural networks. bioRxiv. 2020.07.02.184465. [Preprint] [SI] [Software]
- D.E. Runcie#, J. Qu, H. Cheng, and L. Crawford. Mega-scale linear mixed models for genomic predictions with thousands of traits. bioRxiv. 2020.05.26.116814. [Preprint] [SI] [Software]
- A.N. Spierer#, J.A. Mossman, S.P. Smith, L. Crawford, S. Ramachandran, and D.M. Rand#. Natural variation in the regulation of neurodevelopmental genes modifies flight performance in Drosophila. bioRxiv. 2020.05.27.118604. [Preprint]
- K.E. Ware, S. Gupta, J. Eng, G. Kemeny, B.J. Puviindran, W.C. Foo, L. Crawford, R.G. Almquist, D. Runyambo, B.C. Thomas, M.U. Sheth, A. Agarwal, M. Pierobon, E.F. Petricoin, D.L. Corcoran, J. Freedman, S.R. Patierno, T. Zhang, S. Gregory, Z. Sychev, J.M. Drake, A.J. Armstrong#, J.A. Somarelli#. Convergent evolution of p38/MAPK activation in hormone resistant prostate cancer mediates pro-survival, immune evasive, and metastatic phenotypes. bioRxiv. 2020.04.22.050385. [Preprint]
- B. Wang*, T. Sudijono*, H. Kirveslahti*, T. Gao, D.M. Boyer, S. Mukherjee, and L. Crawford#. A statistical pipeline for identifying physical features that differentiate classes of 3D shapes. bioRxiv. 701391. [Preprint] [SI] [Software]
- J. Ish-Horowicz*, D. Udwin*, K. Scharfstein, S.R. Flaxman, L. Crawford#, and S.L. Filippi#. Interpreting deep neural networks through variable importance. arXiv. 1901.09839. [Preprint] [Software]
- L. Crawford# and X. Zhou#. Genome-wide marginal epistatic association mapping in case-control studies. bioRxiv. 374983. [Preprint] [SI] [Software]
- B.A. Borden, Y. Baca, J. Xiu, F. Tavora, I. Winer, B.A. Weinberg, A.M. VanderWalde, S. Darabi, W.M. Korn, A.P. Mazar, F.J. Giles, L. Crawford, H. Safran, W.S. El-Deiry, and B.A. Carneiro# (2020). The landscape of glycogen synthase kinase-3 beta (GSK-3b) genomic alterations in cancer. Molecular Cancer Therapeutics. In Press.
- L. Crawford#, A. Monod#, A.X. Chen, S. Mukherjee, and R. Rabadán (2020). Predicting clinical outcomes in glioblastoma: an application of topological and functional data analysis. Journal of the American Statistical Association. 115(531): 1139-1150. [PDF] [SI] [Software]
- J.S. Sadick, L. Crawford, H.C. Cramer, C. Franck, S.A. Liddelow, and E.M. Darling# (2020). Generating cell type-specific protein signatures from non-symptomatic and diseased tissues. Annals of Biomedical Engineering. 48: 2218-2232. [Link]
- W. Cheng, S. Ramachandran#, and L. Crawford# (2020). Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits. PLOS Genetics. 16(6): e1008855. [PDF] [SI] [Software]
- K.H. Lin*, J.C. Rutter*, A. Xie, E.T. Winn, B. Pardieu, R. Dal Bello, Y.R. Ahn, Z. Dai, R.T. Sobhan, G.R. Anderson, K.R. Singleton, A.E. Decker, P.S. Winter, J.W. Locasale, L. Crawford, A. Puissant#, and K.C. Wood# (2020). Using antagonistic pleiotropy to design a chemotherapy-induced evolutionary trap. Nature Genetics. 52: 408-417. [PDF]
- T. Borgovan#, L. Crawford, C. Nwizu, and P. Quesenberry (2019). Stem cells and extracellular vesicles: biological regulators of physiology and disease. American Journal of Physiology-Cell Physiology. 317(2): C155-C166. [PDF]
- L. Crawford#, S.R. Flaxman, D.E. Runcie, and M. West (2019). Variable prioritization in nonlinear black box methods: a genetic association case study. Annals of Applied Statistics. 13(2): 958-989. [PDF] [SI] [Software]
- A. Monod#, S. Kališnik Verovšek, J.Á. Patiño-Galindo, and L. Crawford (2019). Tropical sufficient statistics for persistent homology. SIAM Journal on Applied Algebra and Geometry. 3(2): 337-371. [PDF] [Software]
- D.E. Runcie# and L. Crawford (2019). Fast and general-purpose linear mixed models for genome-wide genetics. PLOS Genetics. 15(2): e1007978. [PDF] [SI] [Software]
- L. Crawford#, K.C. Wood, X. Zhou#, and S. Mukherjee# (2018). Bayesian approximate kernel regression with variable selection. Journal of the American Statistical Association. 113(524): 1710-1721. [PDF] [SI] [Software]
- R. Soderquist, L. Crawford, E. Liu, M. Lu, A. Agarwal, G.R. Anderson, K.H. Lin, P.S. Winter, M. Cakir, and K.C. Wood# (2018). Systematic mapping of BCL-2 gene dependencies in cancer reveals molecular determinants of BH3 mimetic sensitivity. Nature Communications. 9(1): 3513. [PDF]
- K.R. Singleton*, L. Crawford*, E. Tsui, H.E. Manchester, O. Maertens, X. Liu, M.V. Liberti, A.N. Magpusao, E.M. Stein, J.P. Tingley, D.T. Frederick, G.M. Boland, K.T. Flaherty, S.J. McCall, C. Krepler, K. Sproesser, M. Herlyn, D.J. Adams, J.W. Locasale, K. Cichowski, S. Mukherjee, and K.C. Wood (2017). Melanoma therapeutic strategies that select against resistance by exploiting MYC-driven evolutionary convergence. Cell Reports. 21(10): 2796-2812. [PDF] [SI]
- L. Crawford#, P. Zeng, S. Mukherjee, and X. Zhou# (2017). Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits. PLOS Genetics. 13(7): e1006869. [PDF] [SI] [Software]
- G.R. Anderson*, P.S. Winter*, K.H. Lin, D.P. Nussbaum, M. Cakir, E.M. Stein, R. Soderquist, L. Crawford, J.C. Leeds, R. Newcomb, P. Stepp, C. Yip, S.E. Wardell, J.P. Tingley, M. Ali, M. Xu, M. Ryan, S.J. McCall, A. McRee, C.M. Counter, C.J. Der, and K.C. Wood# (2017). A landscape of therapeutic cooperativity in KRAS mutant cancers reveals principles for controlling tumor evolution. Cell Reports. 20(4): 999-1015. [PDF]
- G.R. Anderson, S.E. Wardell, M. Cakir, L. Crawford, J.C. Leeds, D.P. Nussbaum, P.S. Shankar, R.S. Soderquist, E.M. Stein, J.P. Tingley, P.S. Winter, E.K. Zeiser-Misenheimer, H.M. Alley, A. Yllanes, V. Haney, K.L. Blackwell, S.J. McCall, D.P. McDonnell, and K.C. Wood# (2016). PIK3CA mutations enable selective targeting of a breast tumor lineage survival dependency through MTOR-mediated control of MCL-1 translation. Science Translational Medicine. 8: 369ra175. [PDF]
- L. Crawford, V. Ponomarenko#, J. Steinberg, and M. Williams (2014). Accepted elasticity in local arithmetic congruence monoids. Results in Mathematics. 66: 227-245. [Link]
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lcrawford (at) microsoft (dot) com
lorin_crawford (at) brown (dot) edu