Hi! I'm Colby...

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About Me

I'm Colby T. Ford, Ph.D.

I'm a Computational Biomathematician and Data Scientist. I have a passion for Mathematics and Genomics research. So, I use Machine Learning and Visual Analytics to help along the way.

Originally from Lenoir, North Carolina, I studied Applied Mathematics, Data Science, and Computational Biology at the University of North Carolina at Charlotte.
I currently work as a researcher in the Department of Bioinformatics and Genomics at UNCC, as a Data Scientist/Artificial Intelligence Solution Architect for BlueGranite, a Microsoft partner consulting firm focused on delivering Data and AI solutions, and as the Director of Data Science for HydraFacial, a dermatology medical device company. I am currently located in Charlotte, North Carolina.


  • Connect:
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Skills
  • Mathematics

    Statistical Modeling
    Bayesian Nets
    Operations Research
    Numerical Logic

  • Machine Learning

    Algorithm Design
    Natural Language Processing
    Deep Learning
    Artificial Intelligence

  • Computational Biology

    Human Genomics
    Phylogenetics
    Molecular Sequence Analysis
    Infectious Diseases

  • Distributed Computing

    Apache Spark (Databricks)
    Cluster Computing (MPI/SNOW)
    GPU-Based Processing (CUDA)
    High Performance Computing

  • Programming

    R/SparkR
    Python/PySpark
    SQL
    Visual Basic

  • Web and Design

    HTML5+CSS
    LaTeX
    API Development
    Visualization

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Research
  • Genomics, Machine Learning, Infectious Diseases

    Machine Learning in Malaria

    Parallel Processing and Ensemble Machine Learning Modeling for the Prediction of Artemisinin Resistance in Malaria (Malaria DREAM Challenge 2019 Submission).

    The Malaria DREAM Challenge is open to anyone interested in contributing to the development of computational models that address important problems in advancing the fight against malaria. The overall goal of the first Malaria DREAM Challenge is to predict Artemisinin (Art) drug resistance level of a test set of malaria parasites using their in vitro transcription data and a training set consisting of published in vivo and unpublished in vitro transcriptomes. The in vivo dataset consists of ~1000 transcription samples from various geographic locations covering a wide range of life cycles and resistance levels, with other accompanying data such as patient age, geographic location, Art combination therapy used, etc. [Mok et al., (2015) Science]. The in vitro transcription dataset consists of 55 isolates, with transcription collected at two timepoints (6 and 24 hours post-invasion), in the absence or presence of an Art perturbation, for two biological replicates using a custom microarray at the Ferdig lab. Using these transcription datasets, participants will be asked to predict three different resistance states of a subset of the 55 in vitro isolate samples.

  • Genomics, Phylogenetics, Infectious Diseases

    StrainHub

    Shiny web application for visualizing disease transmittion networks from phylogenetic trees.

    Strainhub is designed as a web-based software to generate disease transmission networks and associated metrics from a combination of a phylogenetic tree and a metadata associated file. The software maps the metadata onto the tree and performs a parsimony ancestry reconstruction step to create links between the associated metadata and enable the construction of the network.

  • Genomics, Infectious Diseases

    Genetic Capitalism in E. coli

    Persistence of Genes that Confer Antimicrobial Resistance in the History of Escherichia coli Genomes

  • R Package

    sourcerr

    Easily output package citations and tidy session information for reproducibility in research publications.

  • R Package, Genomics

    msgen

    R functions for interfacing with the Microsoft Genomics service in Azure.

  • R Package

    quilt

    A patchwork of efficient and tidy multidimensional data operations.

  • Human Genomics, Phylogenetics

    An Integrated Phylogeographic Analysis of the Bantu Migration

    Using phylogenetic analysis, dimensionality reduction, and machine learning, a combined migratory model of the Bantu migration was formed with heterogeneous data.

  • R Package, Genomics

    parEBEN

    Parallel Implementations of the Empirical Bayesian Elastic Net Cross-Validation in R.

  • Human Genomics, Phylogenetics, Linguistics

    Visualizing Linguistic Disparity of Uto-Aztecan Languages and Bantu Languages

    Using unsupervised learning for dimensionality reduction and visualization.

  • Phylogenetics, Epidemiology, Infectious Diseases

    Spread of Middle East Respiratory Coronavirus: Genetic versus Epidemiological Data

    Analysis and visualzation of the WHO reports of MERS infections versus genomic data.

  • Human Genomics, Machine Learning

    MPS-IIIB/NAGLU Prediction

    PolyPhen2 + Machine Learning prediction of the effects of genetic mutations on mucopolysaccharidosis IIIB.

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Let's Connect!

Want to collaborate, consult, or connect? Let's chat and see how we can work together to do awesome things...

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