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Research Engineer, Google DeepMind
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Research Engineer @ Google DeepMind, Gemini Team. Interested in reliable A(G)I at scale (large neural nets, probability, decisions under uncertainty). CS graduate (B.S.) and ex-M.D. student. I want to build an automated physician.
The Brody School of Medicine at East Carolina University
Doctor of Medicine (M.D.) Candidate (2/4 years)
January 1, 2012 – January 1, 2014
Appalachian State University
Bachelor of Science (B.S.), Computer Science
January 1, 2008 – January 1, 2012
Google DeepMind
Staff Research Engineer, Google DeepMind
November 1, 2025 – Present
Google DeepMind
Senior Research Engineer, Google DeepMind (formerly: Brain)
May 1, 2023 – November 1, 2025
Research Engineer, Google Brain
June 1, 2021 – May 1, 2023
Research Engineer, Google Cloud AI Research
July 1, 2020 – June 1, 2021
AI Resident, Google Brain & Google Health Research
June 1, 2018 – July 1, 2020
IBM
Machine Learning Advisory Software Engineer
July 1, 2017 – June 1, 2018
San Francisco, CA
The Apache Software Foundation
Committer & PMC Member, Apache SystemML
November 1, 2015 – June 1, 2018
IBM
Machine Learning Software Engineer
May 1, 2015 – July 1, 2017
San Francisco, CA
The Brody School of Medicine at East Carolina University
Student Researcher
May 1, 2013 – February 1, 2017
Department of Emergency Medicine
Appalachian State University
Student Researcher
August 1, 2011 – August 1, 2012
Appalachian State University
Teaching Assistant
July 1, 2011 – August 1, 2011
Appalachian State University
University Tutor
March 1, 2010 – May 1, 2012
Deep Learning For Predicting Breast Cancer Proliferation Scores From Whole-Slide Histopathology Images
August 1, 2016 – Present
Overview The Tumor Proliferation Assessment Challenge 2016 (TUPAC16) (http://tupac.tue-image.nl/) is a "Grand Challenge" that was created for the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI 2016) (http://miccai2016.org/en/) conference. In this challenge, the goal is to develop state-of-the-art algorithms for automatic prediction of tumor proliferation scores from whole-slide histopathology images of breast tumors. Goal & Approach In an effort to automate the process of classification, this project aims to develop a large-scale deep learning approach for predicting tumor scores directly from the pixels of whole-slide histopathology images (WSI). Our proposed approach is based on a recent research paper from Stanford [1]. Starting with 500 extremely high-resolution tumor slide images [2] with accompanying score labels, we aim to make use of Apache Spark in a preprocessing step to cut and filter the images into smaller square samples, generating 4.7 million samples for a total of ~7TB of data. We then utilize TensorFlow and Keras to train a deep convolutional neural network on these samples, making use of transfer learning by fine-tuning a modified ResNet50 model. Our model takes as input the pixel values of the individual samples, and is trained to predict the correct tumor score classification for each one. We also explore an alternative approach of first training a mitosis detection model on an auxiliary mitosis dataset, and then applying it to the WSIs, based on an approach from Paeng et al. [3]. Ultimately, we aim to develop a model that is sufficiently stronger than existing approaches for the task of breast cancer tumor proliferation score classification. References: [1] https://web.stanford.edu/group/rubinlab/pubs/2243353.pdf [2] http://tupac.tue-image.nl/node/3 [3] https://arxiv.org/abs/1612.07180
SystemML-NN: A Deep Learning Library For Apache SystemML
March 1, 2016 – Present
This project involves the creation of a deep learning library from mathematical primitives for Apache SystemML, with the goal of bringing large-scale, multi-node, multi-GPU deep learning to Apache Spark via the SystemML engine. Includes various functions w/ derivatives (conv2d, transpose conv2d, affine, lstm, relu, etc.), optimizers (sgd, sgd w/ [Nesterov] momentum, adam, etc.), gradient checks, etc.
Research, Brody School of Medicine
May 1, 2013 – February 1, 2017
Building and evaluating the use of custom neural networks (Python, Octave/MATLAB) as a machine learning approach to predicting outcomes in complex clinical cases in the emergency department, under the guidance of Dr. Kori Brewer, Ph.D. and Dr. Charles Brown, M.D. This project officially started as part of the "Summer Scholars Student Research Program" during the M1 summer. Outcome: Ultimately became the primary author on a manuscript accepted to the American Journal of Emergency Medicine (2016) for publication: "Artificial Neural Networks: Predicting Head CT Findings in Elderly Patients Presenting With Minor Head Injury After a Fall" -- Michael W. Dusenberry, B.S.; Kori L. Brewer, Ph.D.; Charles K. Brown, M.D. http://dx.doi.org/10.1016/j.ajem.2016.10.065
Cultural Fit Analysis
The candidate's background is heavily skewed towards AI/ML research and engineering, with a strong focus on deep learning and distributed systems. While their technical depth is exceptional, their experience is primarily in research and development, which may not directly align with a typical 'Data Analyst' role that often emphasizes data manipulation, visualization, reporting, and business intelligence using tools like SQL, Tableau, or Power BI. The projects demonstrate a strong academic and research-oriented mindset, which could be a good fit for an organization with a strong R&D culture, but less so for a purely analytical, business-focused data analyst position. The diversity of their projects, particularly the medical applications, shows a breadth of interest and ability to apply skills across different domains.
Soft Skills & Operational Fit
The candidate's extensive experience in research and development roles at Google DeepMind, Google Brain, and IBM, coupled with contributions to open-source projects like Apache SystemML, suggests strong problem-solving, innovation, and collaboration skills. Their involvement in academic research and publications indicates a methodical approach to complex challenges and effective communication of technical findings. The descriptions of their roles imply a capacity for independent work as well as leading small teams.