Dr. Edwards'

Portfolio

PhD in Biochemical Engineering at the University of Maryland, Baltimore County (2025), preceded by an M.S. in Biochemical Engineering (2020) and dual B.S. degrees in Chemical Engineering and Molecular Biology (2018). Currently an Informatics Scientist doing product research for my company's new multiplex immunoassay platform, where I integrated machine learning into NGS workflows.

01 Machine Learning

Machine Learning

Implemented unsupervised machine learning via consensus clustering into R Shiny app for novel multiplex immunoassay platform (MSD, 2026) RAG/LLM Platform deploying latest open source LLMs via a Local 3090, Ollama and OpenWebUI Sequence-to-structure protein modeling platform using PyTorch via OpenFold3, the open sourced AlphaFold Generalizable metamaterials design techniques inspire efficient mycelial materials inverse design (Zavorskas 2025, ACS Biomaterials) Used genetic programming to infer descriptive models about transcriptomic and phosphoproteomic signal transduction pathways (Edwards 2025, ProQuest) Incremental Inverse Design of Desired Soybean Phenotypes (Zavorskas 2024, ACS Omega) 2018 AAAI Conference proceeding about "Parameter Identification in Coupled Differential Systems via Finite Element Discretization and Neural Network Analysis" (Edwards 2018, AAAI)

02 NGS/Proteomics

NGS/Proteomics

Built a suite of GUI apps for time-dynamic NGS and proteomic data — anchored by DPoP, a derivative-profiling method built on flux theory that flags differentially changing signals standard differential analysis misses.

Wrote the accompanying bioinformatic pipeline end to end and validated it against existing methods and RT-qPCR-confirmed ground truth in A. nidulans.

Flux theory in dynamic omics data sets: identifying differential signals with DPoP — BMC Bioinformatics (2024) DPoP — Derivative Profiling of Omics Package (MATLAB File Exchange) Omics Clustering / GO-Term Analysis App Dynamic Volcano Plotting App

03 Systems Biology

Systems Biology

Genetic programming and kinetic modeling (MATLAB, GPTIPS) for network inference — from an unstructured kinetic model of an arabinose switch that decouples cell growth from metabolite production, to how cell wall integrity signaling governs mycelial mechanics in Aspergillus nidulans.

Collaborated across fungal drug resistance, systems biology, and biomaterials work, connecting proteomic and transcriptomic dynamics back to whole-organism phenotype.

Unstructured kinetic models to simulate an arabinose switch — Synthetic and Systems Biotechnology (2020) MpkA cell wall integrity kinase and mycelial mechanical properties — preprint (2024) Fungal drug resistance phenotyping — Microbiology Spectrum

04 Synbio

Synthetic Biology

Engineering microbial hosts at the genetic level — multiplex CRISPR/Cas12 genome editing and a colorimetric genetic marker built by engineering sulfur metabolism, both in Yarrowia lipolytica.

Also applied metamaterials-inspired inverse design to tune the mechanical properties of mycelial biomaterials in Aspergillus nidulans.

Multiplex CRISPR/Cas12 gene editing in Yarrowia lipolytica Colorimetric genetic marker via engineered sulfur metabolism Mycelial materials design via metamaterials-inspired inverse design — preprint, ACS Biomaterials (in review)