CV for Michael C. Hughes

PDF | source | Last updated: July 17 2018


  • Brown University


    Ph.D., Computer Science.

  • Brown University


    M.S., Computer Science.

  • Olin College of Engineering


    B.S. Electrical & Computer Engineering

Research Experience

  • Assistant Professor of Computer Science

    2018 - present

    Tufts University, Medford, MA

    • Conduct research, advise students, and teach classes.
  • Postdoctoral fellow: Machine learning to improve clinical decisions in healthcare

    2016 - 2018

    Adviser: Prof. Finale Doshi-Velez (Harvard)

    • Developed semi-supervised models for characterizing and treating depression (with Dr. Perlis and Dr. McCoy).
    • Applied time-series models to predict ventilator interventions in the ICU for public dataset of >36,000 patients.
    • Created methods for training deep models so they are more interpretable to clinicians or other users.
  • Postdoc project: Estimating carbon biomass from LiDAR waveforms


    Adviser: Prof. Erik Sudderth & Prof. Jim Kellner (Brown U., Ecology & Evolutionary Biology)

  • Ph.D. thesis: Reliable and scalable variational inference for Bayesian nonparametrics


    Adviser: Prof. Erik Sudderth

    • Thesis Title: Reliable and scalable variational inference for nonparametric mixtures, topics, and sequences
    • Developed optimization algorithms for Bayesian nonparametric models that scale to millions of examples.
    • Optimized lower bound on marginal likelihood, thus penalizing too simple and too complex explanations.
    • Escaped local optima via data-driven proposals that add useful new clusters and remove redundant ones.
    • Applied to topic models of 2 million NY Times articles and sequential models of the whole human genome.
    • Implemented algorithms in open-source package: Bayesian Nonparametrics for Python (BNPy).
  • Master's project: Sequential Models for Video and Motion Capture


    Adviser: Prof. Erik Sudderth

    • Developed methods to discover common actions from many videos of humans performing household exercises.
    • Improved existing inference algorithms with data-driven Metropolis-Hastings proposals.

Honors and Awards

Highlighted Publications

Superscripts indicate mentored student's role: u = undergraduate, m = masters, d = doctoral.

Highlighted Preprints

Industry Experience

  • Google , Mountain View, CA

    Summer 2013

    Software Engineering Intern

    • Improved walking/biking/running classifier using smartphone accelerometer data.
    • Led collection of dataset from dozens of individuals for classifier evaluation via custom Android app.

Teaching and Mentorship

Outreach Experience

  • Harvard Humanitarian Initiative , Cambridge, MA


    Signal Program Fellow

    • Developed prototype detector for common housing structures in sub-Saharan Africa from satellite images.
    • Intended for humanitarian oversight of conflict areas where burning structures is common attack pattern.
    • Featured in TEDx talk:
  • TEALS and Boston Latin Academy , Roxbury, MA


    Volunteer AP Computer Science Instructor

    • Taught 1-2 classes / week for 2 years via TEALS "CS in every high school" initiative sponsored by Microsoft.
    • Developed hands-on lessons to excite students from diverse backgrounds about computational thinking.
    • Mentored full-time teacher Ingrid Roche as she transitioned from media arts to AP computer science (Java).
  • Olin College Engineering Discovery , Needham, MA


    Co-Founder and Curriculum Director

    • Managed 15 undergrads in developing hands-on lessons for 4th-8th graders.
    • Hosted workshops for 30 children to design, build, and launch bottle rockets.
    • Pioneered green energy workshop which earned over \$750 in outside funding.

Professional Service

  • 2017

    Workshop Organizer

    • Machine Learning for Health workshop at NIPS '17 (NIPS ML4H 2017)
    • Full-day workshop with invited keynotes and panels involving clinicians, statisticians, and computer scientists.
    • Organized peer-review process for 118 submitted papers.
  • 2016

    Workshop Organizer

    • Practical Bayesian Nonparametrics workshop at NIPS '16.
    • Full-day workshop with invited speakers, contributed talks, two panel discussions, and lively poster session.
    • Led decisions on >25 submitted papers based on peer review.
  • 2016

    Invited Panelist

  • Program Committee / Reviewer

    • 2018 - ICLR, AAAI, AMIA CRI
    • 2017 - NIPS, ICML, AAAI
    • 2016 - NIPS
    • 2015 - NIPS, ICML
    • 2014 - NIPS, ICML
    • 2013 - NIPS (reviewer award)

All Publications (in chronological order)