I'm a data scientist and software engineer from
and I live in Fort Collins, CO
⛰️ .
I am currently exploring new employment opportunities,
so if you think my skill set and experience is a match for your team,
please reach out!
With 12+ years in the Life Sciences proteomics (high dimensional) space, I have created a comprehensive R-based machine learning analysis ecosystem that standardizes and enables biomarker discovery and model development. I am strong proponent reproducible research and bioinformatics pipelines. Strong leadership and mentoring skills have lead to 40+ production level, predictive models resulting in significant revenue generation. My proficiency in data visualization and manipulation, coupled with a rigorous analytics approach, has been instrumental in driving biomarker discovery and model development within the life sciences proteomics domain.
| Machine Learning | Statistics | Open-Source | Software Tools |
|---|---|---|---|
| Random Forest | Logistic regression | R | Linux, MacOS |
| Naive Bayes | Linear regression | C++ | Git, GitHub |
| Lasso/ridge regression | GLMMs | Python | BASH, GNU |
| k-Nearest neighbour | Mixed-effects models | LaTeX | BitBucket |
| PCA | Survival analysis | CI/CD | Slack |
| Ensemble methods | Multivariate statistics | Docker | AWS |
| Maximum Likelihood | ANOVA | Kubernetes |
- Analysis of high-throughput, multi-plex, high-dimensional, proteomics assay data
- Accomplished leader driving small group projects to completion
- Proven record of accomplishment via publication in peer reviewed, international journals
- Project development and management, experimental design, and data analysis
- 😄 Pronouns: he/him
- 📫 How to reach me:
or any of the links on the ⬅️ sidebar
- 🔭 I’m currently open for employment opportunities!
- 📚 I am currently learning ... actually, I am constantly learning 😄
- 🤔 I’m looking for help with ... finding my next role!
- 💬 Ask me about ... bikes and
R... I'll talk your 👂 off 😄 - 💬 Favorite food: 🐟 🌮
- ⚡ Fun fact ...
- 🚴 I'm an avid cyclist ...
come say hi on
- I maintain several
Rsoftware libraries (📦) that implement statistical and machine learning techniques in biomarker discovery. Some of my popular published (CRAN) 📦 are: - These projects support analyses in the general health care (Life Sciences)
space to generate proteomic based clinical insights in health spaces such as:
- cardiovascular disease
- liver disease (NASH/NAFLD)
- alcohol effects
- biological aging
- exercise status
- metabolic disease
- Favorite techniques:
- logistic regression (ol' faithful)
- random forest
- naive Bayes
- KKNN (nearest neighbor)
- survival analyses
- ensemble methods
- I am a proponent of the open-source software, conducting the majority of my research/analysis via Linux toolkits, R, and the RStudio IDE.
- I promote conforming to the adherence of so-called "tidy" data, a philosophy of data science designed to share underlying data structure, grammar, and format which facilitates the generation of reproducible analyses.
