Skills

Languages and Tools

Programming languages and tools that I use in my day-to-day.

Python, HTML, CSS, bash scripting, git, Illustrator, Indesign, Photoshop

Packages

Specific Python packages that I have proficiency with.

Pandas, PyMC3 (Bayesian statistics), Flask, scikit-learn, scikit-image, Keras, NetworkX, Mesa

ML/Stats

Concepts and ideas that I have mastery over.

network science, machine/deep learning, Bayesian inference, graphical models, agent-based models

Life Sciences

Domain expertise gained during my education.

Microbiology, virology, biochemistry, molecular biology


Experience

Investigator II, Novartis Institutes for Biomedical Research (NIBR)
September 2018-Present, Cambridge MA

  • Investigator in the Scientific Data Analysis (SDA) team reporting to Holger Hoefling.
  • Co-organized and developed teaching material for machine learning & deep learning workshops and seminars internally at NIBR with colleagues Sivakumar Gowrisankar, Yuan Wang, Laszlo Urban, and Sean Xiao.
  • Developed a massively parallelized engine for training, evaluating, and serving machine learning models on internal assay data, with an emphasis on serving prediction uncertainties with NIBR colleagues Nikolaus Stiefl and Gregori Gerebtzoff.
  • Developed Bayeisan graph deep learning models for chemical and protein property prediction with William J. Godinez
  • Co-mentored Arkadij Kummer with Richard Lewis in support of the development of machine learning and deep learning workflows for protein engineering.


Investigator I, Novartis Institutes for Biomedical Research (NIBR)
September 2017-August 2018, Cambridge MA

  • Investigator in the Scientific Data Analysis (SDA) team reporting to Mark Borowsky.
  • Performed internal consulting projects and expanded the SDA Statistical Learning initiative with colleagues.
  • Developed parameterized Bayesian agent-based models of internal project portfolio for scenario planning purposes, using PyMC3 and Mesa.
  • Assisted in the analysis of high throughput DROSHA cleavage data, with one paper currently in writing.
  • Assisted in mentoring two interns, Stacy Meichle (Computer Aided Drug Discovery with Clayton Springer) and Fritz Lekschas (SDA with Brant Peterson).


Health Data Science Fellow, Insight Data Science
June 2017-August 2017, Boston MA

  • 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.


ScD Candidate, Massachusetts Institute of Technology
August 2011-May 2017, Cambridge MA

  • 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.


Undergraduate Researcher, The University of British Columbia
June 2006-May 2010, Vancouver BC

  • 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., Massachusetts Institute of Technology
Department of Biological Engineering, August 2011-May 2017, Cambridge MA
B.Sc., The University of British Columbia
Integrated Sciences, June 2006-May 2010, Vancouver BC

Publications


Made with ❤️ using Flask, Jinja2, and Bootstrap 4. Source at GitHub. Color theme: Nord.