Eric Cramer

About

I am an MD-PhD candidate in the Medical Scientist Training Program (MSTP) at Oregon Health & Science University. My PhD research in Biomedical Engineering focuses on mathematical and computational modeling for precision oncology using simulation and deep learning, advised by Dr. Young Hwan Chang and Dr. Laura Heiser.

My work spans agent-based modeling of the tumor microenvironment, computational analysis of multiplexed tissue imaging, and machine learning for cancer biology. I aim to develop tools that translate quantitative insights into clinically actionable strategies for cancer patients.

Before OHSU, I was a Data Analyst at the Stanford Systems Neuroscience and Pain Laboratory and a Bioinformatics Engineer at Institut Curie in Paris, where I developed image analysis algorithms and machine learning pipelines for cancer research.

Education & Training

PhD, Biomedical Engineering
Oregon Health & Science University, 2023–Present
Thesis: Mathematical and computational modeling for precision oncology using simulation and deep learning (working title)
Advisors: Dr. Young Hwan Chang & Dr. Laura Heiser
MD
Oregon Health & Science University (NIH-funded MSTP), 2021–Present
MS, Biomedical Informatics
Stanford University School of Medicine, 2016–2018
BS, Biomedical Computation
Stanford University School of Engineering, 2013–2017

Research

Agent-Based Modeling of the Tumor Microenvironment

Agent-Based Models Cancer Systems Biology Precision Oncology

Developing in silico agent-based models to map and control cancer progression in triple negative breast cancer (TNBC). By simulating tumor-immune interactions and therapeutic interventions, this work aims to identify actionable strategies for steering the tumor microenvironment toward favorable states.

Computational Analysis of Multiplexed Tissue Imaging

Spatial Proteomics Deep Learning Single-Cell Analysis

Building computational methods for high-plex immunofluorescence tissue images, including COEXIST (serial multiplexed image integration), UniFORM (immunofluorescence normalization), and SHIFT (histological-to-immunofluorescence translation). These tools enable robust spatial analysis of cancer tissue at single-cell resolution.

Temporal Analysis of 3D Cell Culture Systems

Time-Course Imaging Quality Control Organoids

Developing algorithms for temporal reassignment and correspondence evaluation in time-course imaging of 3D cell cultures, enabling robust longitudinal tracking with integrated quality control. This work supports drug response studies using patient-derived organoid models.

Selected Publications

Human interpretable grammar encodes multicellular systems biology models to democratize virtual cell laboratories

Johnson JAI, Bergman DR, Rocha HL, Zhou DL, Cramer E, Mclean IC, et al.

Cell, 2025. doi:10.1016/j.cell.2025.06.048

Temporal reassignment and correspondence evaluation with quality control for time-course imaging of 3D cell culture

Cramer EM, Lopez-Vidal T, Johnson J, Wang V, Bergman DR, Weeraratna A, Burkhart R, Fertig EJ, Zimmerman JW, Heiser LM, Chang YH.

Cell Reports Methods, 2025;5(12):101237. doi:10.1016/j.crmeth.2025.101237

COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

Heussner RT, Watson CF, Eddy CZ, Wang K, Cramer EM, Creason AL, Mills GB, Chang YH.

PLOS Computational Biology, 2025. doi:10.1371/journal.pcbi.1013325

UniFORM: Towards Universal Immunofluorescence Normalization for Multiplex Tissue Imaging

Wang K, Ait-Ahmad K, Kupp S, Sims Z, Cramer EM, Sayar Z, Yu J, Wong MH, Mills GB, Eksi SE, Chang YH.

Cell Reports Methods, 2024. doi:10.1016/j.crmeth.2025.101172

CHOIRBM: An R package for exploratory data analysis and interactive visualization of pain patient body map data

Cramer EM, Ziadni M, Scherrer KH, Mackey S, Kao MC.

PLOS Computational Biology, 2022. doi:10.1371/journal.pcbi.1010496

Classifying chronic pain using multidimensional pain-agnostic symptom assessments and clustering analysis

Gilam G, Cramer EM, Webber KA, Ziadni MS, Kao MC, Mackey SC.

Science Advances, 2021. doi:10.1126/sciadv.abj0320

Acute pain predictors of postoperative pain resolution, opioid cessation, and recovery: Secondary analysis of the START randomized clinical trial

Hah J, Cramer EM, Carroll I, Mackey S.

JAMA Network Open, 2019. doi:10.1001/jamanetworkopen.2019.0168

Predicting the incidence of pressure ulcers in the ICU using machine learning

Cramer EM, Seneviratne M, Sharifi H, Ozturk A, Boussard T.

eGEMs, 2019. doi:10.5334/egems.307

View complete list of publications →