Eric J. Ma

Skills

  • Languages and Tools: Python, HTML, CSS, bash scripting, git, Illustrator, Indesign, Photoshop
  • Packages: Pandas, PyMC3 (Bayesian statistics), Flask, scikit-learn, scikit-image, Keras, NetworkX
  • ML/Stats: Variational autoencoders, random forests, Bayesian inference, graphical models
  • Life Sciences: Microbiology, virology, biochemistry, molecular biology

Experience

Novartis Institutes for Biomedical Research (NIBR), Investigator I, Cambridge MA

September 2017-Present

  • Investigator in the Scientific Data Analysis team reporting to Mark Borowsky
  • Performed internal consulting projects and launched new initiatives for NIBR

Insight Data Science, Health Data Science Fellow, Boston MA

June 2017-Present

  • Built Flu Forecaster, a machine learning-powered system that forecasts flu sequences six months out, to better prepare for manufacturing of vaccine strains.
  • Implemented a variational autoencoder (deep learning model) to learn a continuous representation of 14,455 influenza hemagglutinin protein sequences, and trained a Gaussian process model on the continuous representation to predict new flu sequences.
  • Developed interactive blog post using Flask and Bootstrap, and deployed to Heroku and GitHub.
  • Led peer workshops on web development, deep learning and code style.

Massachusetts Institute of Technology, ScD Candidate, Cambridge, MA

August 2011-May 2017

  • Developed a scalable, network-based phylogenetic heuristic algorithm for detecting reassortant influenza viruses using 18,632 fully sequenced virus genomes that improved our capacity to detect reassortment events by two orders of magnitude.
  • The algorithm was used in a lead-author study (published in PNAS) providing systematic evidence that genome shuffling is important for host switching, and a co-authored study (published in Ecology letters) that showed that reassortment is a strategy for viral gene persistence in wild animal reservoirs.
  • Contributed reproducible data analysis for colleagues through fluorescent image quantification and genomic analysis in studies that refuted binding properties of novel influenza viruses.
  • Performed Bayesian statistical modelling for colleagues testing the efficacy of phone sterilization tools.
  • Delivered tutorials and talks on Network Analysis and Bayesian statistical methods at annual Python conferences, including PyCon, SciPy, and PyData.

The University of British Columbia, Undergraduate Researcher, Vancouver BC

June 2006-May 2010

  • Investigated the role of T cells in intestinal inflammation and fibrosis in a Salmonella typhimurium-induced model of gut inflammation, leading to First Prize Poster (Rising Stars of Research 2008) and Best Talk (Multidisciplinary Undergraduate Research Conference 2009) awards.
  • Co-founded the first UBC International Genetically Engineered Machines (iGEM) team, where we achieved a Gold status standing in our first year of competition.
  • Served as a Peer Academic Coach, guiding first- and second-year students towards academic success through building good habits on learning strategies and time management.


Education

Sc.D, Biological Engineering, Massachusetts Institute of Technology, USA

August 2011-May 2017

B.Sc, Integrated Sciences, The University of British Columbia Vancouver, Canada

June 2006-May 2010