Matthew Oberhardt Email & Phone Number

Provo, Ut And Seattle, Wa, Us

New York, New York, Us

Alongside my primary role in NewYork Presbyterian analytics, i am an adjunct faculty member in the obstetrics and gynecology department at Columbia University Irving Medical Center. In this role I collaborate closely with clinicians to develop novel ways to track postpartum hemorrhage and severe maternal morbidity, and I serve as a liaison between NYP analytics and the clinical department.

I am part of the evaluations core in NYP analytics. We are a small group that works extensively with the hospital's data, providing statistical, research, data science and analysis support to improve the hospital's quality across the board. In this role, I collaborate with clinician leaders for initiatives in women's health, psychiatry, infectious disease, heart disease, and other areas. I perform translational research using EHR data, with a main focus on tracking and improving patient outcomes. I am also the technical lead on a project to help with clinical documentation in the hospital via automation. From an initial concept to a production system that processes all visits across 5 inpatient facilities daily (>8000 visits per month), I co-conceived, architected, and continue to manage development of an internal product with $6.9 million annual financial impact.

San Francisco, Ca, Us

As an insight fellow I analyzed data from an i-phone app that tracks Parkinson's disease patient progress, and built a predictor of disease status using logistic regression models.

College Park, Md, Us

Continued research in how to culture difficult to culture organisms and predicting promiscuous functions of genes.

Tel Aviv, Il

Most new classes of antibiotics are developed by studying bacteria in nature that produce their own antibiotics. However, most bacteria defy our attempts to grow them in the lab, so we are missing out on many possible classes of antibiotics. To attack this problem, I developed new methods aimed at understanding the growth patterns and at growing currently unculturable organisms. I built a database of known growth media in collaboration with one of the largest bacterial repositories on earth, and used machine learning to predict growth patterns of bacteria. My work resulted in a key publication that got some press:https://www.nature.com/articles/ncomms9493(selected press here:https://www.realclearscience.com/journal_club/2015/10/20/a_revolution_in_basic_microbiology_109416.htmlhttps://www.natureasia.com/en/life-sci/research/10262https://www.technology.org/2015/10/21/new-tool-could-help-scientists-grow-the-99-of-bacteria-currently-ungrowable-in-the-lab/)I also developed a novel theory for predicting growth rate of cells, found a new secondary function of an enzyme in E. coli, and applied machine learning to predict drug side effects.

developed a genome-scale metabolic model of an opportunistic pathogen (Pseudomonas aeruginosa), and studied factors contributing to its virulence through computational modeling

Brentford, Middlesex, Gb

Analyzed factors related to aging in muscle tissue, and built a repository of muscle aging data

Doctor Of Philosophy - Phd, Biomedical/Medical Engineering

Post-Doctoral Research, Computational Biology

Bachelor'S Degree, Physics