CV for Michael C. Hughes

PDF | source | Last updated: April 04 2019


  • 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 in statistical machine learning methods and applications to health informatics.
    • Advise Ph.D., M.S., and B.S. students in machine learning research projects.
    • Teach machine learning courses targeted at advanced undergraduates (COMP 135 Intro to ML) and graduate students (COMP 150 Bayesian Deep Learning).
  • 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.

Highlighted Publications

Superscripts indicate mentored student's role: u = undergraduate, m = masters, d = doctoral. Complete publication list at end of this document.

Highlighted Preprints

Honors and Awards

  • 2018

    Top 200 Reviewer Award, NeurIPS 2018

    • Recognized as one of top 200 of more than 3500 expert reviewers at the top international machine learning conference.
  • 2018

    Best Paper Award, SoCal NLP Symposium 2018

  • 2017

    Nominee for AMIA Clinical Informatics Research Award

  • 2011

    NSF Graduate Research Fellowship Award

    • Three year award to fund Ph.D. studies. Covers tuition and provides research stipend.
  • 2011

    NDSEG Graduate Research Fellowship Award

    • Three year funding award. Declined to accept NSF fellowship.

Professional Service

Teaching and Mentorship

Outreach Experience

  • 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).
  • 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:
  • 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.

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.

All Conference Publications (in reverse chronological order)

All Journal Publications (in reverse chronological order)

All Workshop Papers (in reverse chronological order)