Colby Ford

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Colby Ford
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Born (1993-01-28) January 28, 1993 (age 28)
Lenoir, North Carolina
NationalityAmerican
CitizenshipUnited States
Alma materUniversity of North Carolina at Charlotte
Scientific career
Fields
  • Computational Biology
  • Bioinformatics
  • Genetics
  • Data Science
  • Machine Learning
ThesisAn Integrated Phylogeographic Analysis of the Bantu Migration (2018)
Doctoral advisorDaniel Janies
Websitewww.colbyford.com

Colby Tyler Ford (born January 28, 1993) is an American data scientist and computational biologist who has made significant scientific contributions to the field of infectious diseases on malaria and the current COVID-19 pandemic.[1][2].

Education and career

Colby Ford attended the Caldwell Early College High School on the campus of Caldwell Community College & Technical Institute [3] and then received a B.A. in applied mathematics, a M.Sc. in data science and business analytics, and a Ph.D. in Bioinformatics and Computational Biology from the University of North Carolina at Charlotte.

Ford is currently a consultant for Microsoft partner, BlueGranite Inc., in artificial intelligence.[4] and a researcher at UNC Charlotte[5].

Scientific work and contributions

Along with former advisor Daniel Janies, Ford works in infectious disease research, using machine learning techniques in the modeling of diseases. Initially, Ford worked on Middle East Respiratory Syndrome, comparing the progression of the coronavirus from a genetic standpoint against the World Health Organization reports. He has performed research in human genomics in the reconstruction of the Bantu expansion[6] and in rare diseases such as Mucopolysaccharidosis iiib

Recently, Ford has focused his genomics research toward understanding multi-drug resistant strains of Escherichia coli and in modeling the genetic factors associated with artemisinin resistance in Plasmodium falciparum-based Malaria infections[7]

In addition to traditional genomics research, he has developed several scalable data science and bioinformatics tools including:

  • StrainHub - A phylogenetic tool for the construction and visualization of pathogen transmission networks.[8]
  • parEBEN - An R (programming language) for the scalable detection of multicollinearity (or epistasis in genetic contexts) using parallel computing frameworks.

Ford also educates and contributes to research in the fields of parallel computing and cloud computing, specifically using the Apache Spark framework on Microsoft Azure

During the Coronavirus disease 2019 outbreak, Ford provided professional interviews on epidemiological disease modeling to WBTV[9] and CBS News[10] outlets.

In September 2020, Ford was a speaker in the TEDxUNCCharlotte[11] event.

References

  1. de Bernardi Schneider, Adriano; Ford, Colby T.; Hostager, Reilly; Williams, John; Cioce, Michael; Çatalyürek, Ümit V.; Wertheim, Joel O.; Janies, Daniel (2020-02-01). "StrainHub: a phylogenetic tool to construct pathogen transmission networks". Bioinformatics. 36 (3): 945–947. doi:10.1093/bioinformatics/btz646. ISSN 1367-4803. PMID 31418766.
  2. Janies, Daniel A.; Ford, Colby; Damodaran, Lambodhar; Witter, Zachary (2017-05-01). "Spread of Middle East Respiratory Coronavirus: Genetic versus Epidemiological Data". Online Journal of Public Health Informatics. 9 (1): e004. doi:10.5210/ojphi.v9i1.7581. ISSN 1947-2579. PMC 5462161.
  3. Ford, Colby (January 17, 2020). "LinkedIn Profile".
  4. "Colby Ford". Business Impact. BlueGranite. Retrieved 2020-03-03.
  5. "Colby Ford | School of Data Science | UNC Charlotte". datascience.uncc.edu. Retrieved 2021-01-17.
  6. {{Cite thesis |author=Colby Tyler Ford |title=An Integrated Phylogeographic Analysis of the Bantu Migration |year=2018 |language=en |
  7. Ford, Colby T.; Janies, Daniel (2020-01-29). "Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria". F1000Research. 9: 62. doi:10.12688/f1000research.21539.1. ISSN 2046-1402.
  8. de Bernardi Schneider, Adriano; Ford, Colby T.; Hostager, Reilly; Williams, John; Cioce, Michael; Çatalyürek, Ümit V.; Wertheim, Joel O.; Janies, Daniel (2020-02-01). "StrainHub: a phylogenetic tool to construct pathogen transmission networks". Bioinformatics. 36 (3): 945–947. doi:10.1093/bioinformatics/btz646. ISSN 1367-4803. PMID 31418766.
  9. Ochsner, Nick. "Limited data makes it tough to model coronavirus's spread, experts say". wbtv.com. Retrieved 2020-06-17.
  10. "Local leaders weigh in on COVID uncertainty surrounding national conventions". www.cbsnews.com. Retrieved 2020-06-17.
  11. "TEDxUNCCharlotte | TED". www.ted.com. Retrieved 2020-09-17.

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