Erik Ames Burlingame

Erik Ames Burlingame

BME PhD Candidate

Oregon Health & Science University


I am a PhD candidate advised by Dr. Young Hwan Chang in the Computational Biology Program and Biomedical Engineering Department at Oregon Health & Science University. My research focuses on the development of reproducible, scalable, and robust computational applications for digital pathology.

Spatial biology and digital pathology today are what clinical genomics was twenty years ago: rapidly evolving technologies with profound implications for precision medicine, but with much still to prove. To maximize their positive impact on both basic and clinical research, we must not only develop best-in-class algorithms for image analysis, but also invent data integration approaches which synergize biomarkers across imaging and molecular assays. Further, we must do it all in a way that scales up to billions of pixels and millions of cells, or more. These concerns have been central to my graduate research.

As a graduate trainee under the auspices of the Knight Cancer Institute and the National Cancer Institute’s Human Tumor Atlas Network and Cancer Systems Biology Consortium, I have helped lead digital pathology and image analysis projects from conception to publication and provisional patent filing. These projects include the development of a mathematical morphology-based epidermis segmentation algorithm; a GPU-boosted, machine learning-based framework which enables normalization, compilation, and phenotyping of millions of single-cell measurements from multiplex imaging data in minutes (see BCTMA paper); and deep learning-based algorithms for optimal sample selection and histological-to-immunofluorescent transformation of whole slide images (see SHIFT paper).

Some of these projects have been undertaken in the context of SMMART, an ongoing clinical trial focused on breast cancer precision oncology, through which I have learned to become an active collaborator with an interdisciplinary team of clinicians, pathologists, and other scientists.

With this skill set, experience, and the ability and desire to learn new things, I am poised to make immediate impact in a scientific role in industry.

Please find my CV here.


  • Deep learning
  • Digital pathology
  • Computational biology


  • PhD in Biomedical Engineering, 2021

    Oregon Health & Science University

  • MSc in Biology, 2017

    University of Oregon

  • BSc in Biochemistry, 2016

    University of Oregon

Skills at a glance

Data Science

Image Analysis







MSc Student

Bioinformatics and Genomics Masters Program | University of Oregon

Jun 2016 – Sep 2017 Eugene, OR
Internship in Chang Lab at OHSU.

BSc Student

Biochemistry major | University of Oregon

Sep 2013 – Jun 2016 Eugene, OR
Top graduate in biochemistry major.

Recent Posts

Making a cell state sankey diagram with holoviews

WIP: mostly just an exploration of embedding interactive figures in posts. Multiplex imaging platforms like cyclic multiplexed immunofluorescence (cmIF) enable us to characterize cells by the proteins they express.

Recent Work

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Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms

The emergence of megascale single-cell multiplex tissue imaging (MTI) datasets necessitates reproducible, scalable, and robust tools …

An Integrated Clinical, Omic, and Image Atlas of an Evolving Metastatic Breast Cancer

Metastatic cancers often respond to treatment initially but almost universally escape therapeutic control through molecular mechanisms …

DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

Deep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete …

The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution

Crucial transitions in cancer—-including tumor initiation, local expansion, metastasis, and therapeutic resistance—-involve complex …