About Me

I’m a naturopathic physician and former professor, now training to be a data analyst and data scientist.

My interests include the application of data visualization, analysis, and predictive modeling to improve medical practice and health outcomes, and to advance medical knowledge.

In particular, I have interests in precision medicine (especially using metabolomic data) and real-time monitoring of physiology through non-invasive biosensors (using thermal, electrical, and optical data).

Background

I have practiced as a primary care naturopathic physician since 1998, focusing primarily on offering integrative care for patients with a variety of chronic health conditions.

For much of that time, I served as an Assistant Professor of Naturopathic Medicine at the National University of Natural Medicine (NUNM) in Portland, Oregon. There, I taught various courses in the basic medical sciences (physiology, pharmacology and herbal pharmacology, pathology), evidence-based medicine, and neuroendocrinology. I also attended to patients and supervised students in the university’s outpatient teaching clinics. And through NUNM’s Helfgott Research Institute, I participated in evidence-based research and mentored students.

My primary clinical and research activities have been focused around whole-systems biology and integrative medicine, specifically in understanding how physiological self-regulation (salutogenesis) is mediated through the neuroendocrine-immune and organ systems. I have explored the potential of micronutrient (vitamins, minerals, therapeutic metals) and herbal therapies (both “Western” and East Asian herbal therapies) in helping to restore homeodynamic balance in chronic diseases.

For the past 2 years, I have completed additional training in data analytics/data science, machine learning, software engineering, and database management.

From my years of practice as a primary care physician, I have come to believe that advances in biosensing, real-time monitoring of physiology, and machine learning have the potential to revolutionize clinical care. I have felt a strong desire to pivot from clinical practice to data science so that I can directly contribute to this developing field of study.

Skills: Data Analysis & Data Science

Data gathering, storage, cleaning, visualization, and stastistical analysis; predictive modeling using supervised and unsupervised machine learning and AI

  • Python (including NumPy, Pandas, Matplolib, Seaborn, Scikit-learn, TensorFlow)
  • R
  • SQL
  • Tableau
  • Git/Github (version control)

Software Development

Full stack web development

  • HTML5/CSS3
  • Javascript (including Node.js, Express.js, Mongoose/MongoDB, React.js)