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Eric Cramer

Biomedical Data Analyst, Stanford School of Medicine

Stanford University

Biography

I am a Data Analyst and researcher in the Systems Neuroscience and Pain Laboratory at the Stanford University School of Medicine. My educational background is in biomedical computation and engineering, and I have experience working in in a variety of fields and research laboratories from clinical informatics with electronic health records to single-cell data analysis and image processing. Currently, I develop machine learning pipelines for finding patterns in EHR to improve patient care, and work on electronic data capture (EDC) tools to improve research infrastructure (among other projects).

I completed my Master of Science (M.S.) in Biomedical Informatics at Stanford University School of Medicine, and I received my Bachelor’s of Science (B.S.) in Biomedical Computation from the Stanford University School of Engineering. My research interests include biomedical data mining, developing tools to improve bio-imaging, and creating novel machine learning algorithms for the biomedical research space.

While I was an undergraduate, I was a member of Stanford’s NCAA Division I wrestling team, I tutored chemistry courses for underclassmen, and I led Stanford Pre-Orientation Trips (SPOT) for incoming freshmen. I currently help out as an assistant coach for the local high school and middle school wrestling teams, and I enjoy outdoor activities in my spare time. My long term goal is to attend a medical school dual degree program to earn a MD-PhD and continue my explorations in the patient-centered bioinformatics space.

Interests

  • Clinical Data Mining
  • Federated Computing
  • Computational Biology
  • Bioimaging
  • Language (French, Spanish)

Education

  • MS in Biomedical Informatics, 2018

    Stanford University School of Medicine

  • BS in Biomedical Computation, 2018

    Stanford University School of Engineering

Skills

R

Python

Statistics

JavaScript

Biomedicine

Research

Experience

 
 
 
 
 

Research Data Analyst

Systems Neuroscience and Pain Lab, Stanford Medicine, Department of Anesthesiology

Nov 2019 – Present California
I work on a variety of clinical research and computational research infrastructure projects such as analysis of symptom clusters in chronic pain patients, predicting patient satisfaction with tapering off of their opioid medications, and development of distributed computing platforms to enable multi-institution collaborations.
 
 
 
 
 

Bioinformatician

Institut Curie

Oct 2018 – Oct 2019 Paris, France
Conducted experiments in the Bava lab on tissues and cell cultures. Ran computational analysis of experimental data including processing microscopy images (confocal, fluorescence, spinning disk), RNAseq analysis, flow and mass cytometry analysis. Developed novel algorithms, machine learning pipelines, and data visualizations.
 
 
 
 
 

Research Assistant, Data Manager

Systems Neuroscience and Pain Lab, Stanford SOM

Jun 2015 – Aug 2018 California
Assisted with data collection and programmatic analysis. Helped to write and revise study protocols and grants. Assisted in clinic with patient treatments for clinical trials. Conducted independent research as an undergraduate (Bio X). Managed the Stanford Medical School’s Collaborative Health Outcomes Information Registry (CHOIR) database

Recent Posts

Multiple linear regression in a distributed system

In a previous post I talked about adapting a linear regression algorithm so it can be used in a distributed system. Essentially, a …

Starting distributed computing

Quick Intro As hospitals, care providers, and private companies collect more data, they develop rich databases that can be used to …

Projects

COVID-19 Tracker

A simple tool to calculate county-level change rates during the COVID-19 pandemic.

Predicting CRPS Limb Affectation

Using machine learning to predict CRPS limb affectation from psychophysical factors.

Modeling Calmodulin

Using molecular dynamics to model the calmodulin protein.

Exon Mutability Score

Developing a metric to measure how mutable a given human exon may be.

Recent Publications

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Acute Pain Predictors of Remote Postoperative Pain Resolution After Hand Surgery

The prevalence of severe chronic postsurgical pain (CPSP) is 10% across a range of operations, and 22% of patients undergoing hand …

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