Zorian Thornton

Ph.D Student, Genome Sciences, Seattle, WA

Education

July 2021 - Present University of Washington, Seattle

• Ph.D candidate in Genome Sciences, advised by Erick Matsen.

Sep. 2019 - July 2021 University of Washington, Seattle

• Ph.D student in Genome Sciences, advised by Erick Matsen.
• GPA: 3.8/4.0

Aug. 2015-May 2019 Virginia Tech, Blacksburg

• B.Sc Statistics
• Minors: Mathematics and Computer Science
• GPA: 3.7/4.0

Aug. 2015-May 2019 Virginia Tech, Blacksburg

• B.Sc Computational Modeling & Data Analytics
• GPA: 3.6/4.0

Research Experience

June 2020-Present Fred Hutchison Cancer Research Center

• Predoctoral candidate
• Developing computational tools to predict viral protein phenotypes from deep mutational scanning experiments with the goal to accurately predict fitness of unseen protein variants and predict viral evolutionary trajectories.

March 2020-June 2020 University of Washington Department of Genome Sciences

• Lab rotation with Brian Beliveau
• Implemented a bioinformatics pipeline for the design of split-oligo probe pairs for fluorescence in situ hybridization (FISH) experiments to enable fast and affordable design of highly specific RNA FISH probes.

Jan. 2020-March 2020 Fred Hutchison Cancer Research Center

• Lab rotation with Erick Matsen
• See related entry above.

Sep. 2019-Dec 2019 University of Washington Department of Genome Sciences

• Lab rotation with Bill Noble
• Implemented a novel method for systematically finding potential functional inter-chromosomal contacts in Hi-C data to provide better understanding of genome organization.

Aug. 2018-May 2019 Virginia Tech Department of Statistics

• Undergraduate research with Allison N. Tegge
• Implemented Self-Organizing Maps and conducted survival analysis to identify genes and pathways associated with progression of colorectal cancer from patients included in The Cancer Genome Atlas Program.

Aug. 2017-May 2019 Virginia Tech Department of Statistics

• Undergraduate research with Leah R. Johnson
• Developed a new formula for the disease basic reproductive number, $R0$, to include temperature sensitive midge life history traits to predict potential regions for the spread of Bluetongue viral disease.

June 2017-Aug 2017 University of Washington Department of Genome Sciences

• Lab rotation with Jim Bruce
• Implemented software to estimate and visualize inter-surface regions of cross-linked proteins, enabling scientists to better characterize protein function, discover mutations, and discover protein-protein interactions to tackle molecular challenges such as cancer.

Research interests

Bayesian statistics, approximate inference, deep learning, adaptive immunology, viral evolution, phylogenetics,

Awards

2020 Honorable Mention, National Science Foundation Graduate Research Fellowship Program, Alexandria, VA

2019 Member, Mu Sigma Rho, National Honor Society, Virginia Tech

2018 Finalist, College of Science Dean’s Roundtable Scholarship, Virginia Tech

Recipient, Luther and Alice Hamlett Undergraduate Research Grant, Virginia Tech Academy of Integrated Sciences

2017 Fellow, Fralin Research Institute, Virginia Tech

2016 Recipient, Eckert Statistics Scholar, Virginia Tech

Publications

Journals

2022 Yu, T., Thornton, Z., Hannon, W., DeWitt, W.S., Radford, C., Matsen IV, F.A. and Bloom, J.D. (2022). A biophyscial model of viral escape from polyclonal antibodies Virus Evolution, 8(2), (https://doi.org/10.1093/ve/veac110)

2021 El Moustaid, F., Thornton, Z., Slamani, H. et al. Predicting temperature-dependent transmission suitability of bluetongue virus in livestock. Parasites Vectors 14, 382 (2021). https://doi.org/10.1186/s13071-021-04826-y

2018 Andrew Keller, Juan D. Chavez, Jimmy K. Eng, Zorian Thornton, and James E. Bruce Journal of Proteome Research 2, 753-758 (2019) https://doi.org/10.1021/acs.jproteome.8b00703

Research Presentations

2019 Predicting Disease Progression of Colorectal Cancer via Self-Organizing Maps, RECOMB 2019, George Washington University, Washington DC, May 2019

2018 Modeling Bluetongue Virus via Markov Chain Monte Carlo Methods, Student Experiential Learning Conference, Virginia Tech, Blacksburg, VA, Apr. 2018

2017 Viewing Molecular Interaction Interfaces Through Directed Computational Methods, Department of Genome Sciences Research Symposium, University of Washington, Seattle, WA, Aug. 2017

Work Experience

June 2022-Sept. 2022 Adaptive Biotechnologies

• Machine Learning Computational Biology Intern
  • Conducted benchmarking of semi-supervised learning methods for predicting T-cell receptor specificity from high-throughput sequencing data.

June 2019-Sept. 2019 Virginia Tech Statistical Applications and Innovations Group

• Associate Consultant
  • Statistical consultant to Virginia Tech graduate students and faculty.

June 2018-August 2018 Nielsen

• Data Science Intern
  • Implemented statistical framework to identify possible errors in scanned receipt data and imple- mented pipeline to attempt to correct errors.

Jan. 2017-Aug. 2018 Virginia Tech Math Emporium

• Instructional Assistant
  • Teaching assistant for introductory math courses including differential and integral calculus, graph theory, differential equations, and linear algebra.

Programming Languages, Tools, and Concepts

Preferred Programming Languages

• R, Python, C++

Dev Tools & Environments

• git, Jupyter

HPC Tools

• CUDA, OpenMP, MPI
  • I sparsely use any of these now due to PyTorch and TensorFlow making life easier

Other languages I rarely use now

• Java, Matlab, PHP, SQL, SAS

Proffesional Organizations

• The American Association of Immunologists member since 2020
• National Society of Blacks in Computing member since 2017
• American Statistical Association member since 2016
• National Society of Black Engineers member since 2016