CV for Michael C. Hughes

PDF | source | Last updated: May 21 2025

Current Role

  • Assistant Professor of Computer Science

    2018 - present

    Tufts University, Medford, MA

    • Tenure-track role focused on research, teaching, mentorship, and service
    • Research in statistical methods for machine learning (ML), published in ICML, AISTATS, NeurIPS, TMLR
    • Research in applied ML for health, published in MLHC, CHIL, clinical journals
    • Active Funding: NSF CAREER as Single PI; Co-I role as ML lead: NSF GCR, NIH R01, ADDF foundation
    • Past Funding: NSF small, DARPA, U.S. Army, pharma
    • Mentoring: 3 full-time Ph.D. students and 1 postdoc in 2025. Past: 1 postdoc (now faculty), 2 Ph.D.
    • Teaching: Core ML courses: CS 135 Intro to ML, CS 136 Statistical Pattern Recog.
    • Teaching: Project courses for grad students: Bayesian Deep Learning, Learning from Limited Labeled Data

Research Expertise

  • 2012-

    Machine Learning

    • Learning from limited labeled data: semi-supervised and self-supervised learning, transfer learning
    • Probabilistic models: latent variable models like HMMs, deep generative models, Bayesian nonparametrics
    • Estimation algorithms for Bayesian posteriors: variational methods, Markov chain Monte Carlo
    • Decision-aware ML: top-K where-to-intervene problems, false alarm constraints, semi-supervised HMMs

  • 2017-

    Applied ML for Healthcare

    • Image analysis / computer vision for heart disease and cerebrovascular disease
    • - Predicting aortic stenosis (valve disease) from echocardiograms (ultrasound of the heart)
    • - Predicting risk of stroke/dementia from CT/MRI images
    • Spatiotemporal forecasting of opioid overdose events
    • Early warning systems for predicting mortality/deterioration in hospitalized patients
    • Modeling demand for hospital resources over time due to COVID-19 pandemic

  • 2012-

    Applied ML for Human Activity Analysis

    • Modeling sensor time series, motion capture, or video for activity recognition
    • Modeling teams of students in STEM classrooms to understand group dynamics

Education

  • Brown University

    2016

    Ph.D., Computer Science

  • Brown University

    2012

    M.S., Computer Science

  • Olin College of Engineering

    2010

    B.S. Electrical & Computer Engineering

Honors and Awards for Research

Honors and Awards for Peer Review

  • 2023

    Top 10 Percent Reviewer Award, AISTATS 2023

    • Recognized as one of top 10 percent of all 2500 expert reviewers at a top international machine learning conference.

  • 2022

    Highlighted Reviewer Award, ICLR 2022

    • Recognized as a top reviewer at ICLR, a top-tier international machine learning conference.

  • 2022

    Top 10 Percent Reviewer Award, AISTATS 2022

    • Recognized as one of top 10 percent of all 2500 expert reviewers at a top-tier international machine learning conference.

  • 2020

    Top 10 Percent Reviewer Award, NeurIPS 2020

    • Recognized as one of top 10 percent of more than 3500 expert reviewers at the top international machine learning conference.

  • 2019

    Top 400 Reviewer Award, NeurIPS 2019

    • Recognized as one of top 400 of more than 3500 expert reviewers at the top international machine learning conference.

  • 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.

Current Funding Support

  • Understanding Dynamics of Uncertainty in STEM Education

    09/15/24 - 09/14/29

    NSF Growing Convergence Research award \#2428640

    • Full title: Towards a Convergent Understanding of the Dynamics of Uncertainty In Individuals and Groups with a Focus on STEM Education
    • Goal: Develop ML methods to help students embrace uncertainty constructively in STEM education
    • My role: co-Investigator, along with 9 other Tufts faculty
    • News Article: Tufts Now
    • Total Amount: \$1,200,000 per year for 2 years, chance to renew

  • Decision-Aware Adaptive Probabilistic Models from Limited Supervision

    07/01/24 - 06/30/29

    NSF CAREER award \#2338962

    • Title: Decision-Aware Learning of Adaptive Probabilistic Models from Limited Supervision
    • Goal: Develop improved ML methods to help make decisions under uncertainty in health applications
    • My role: Principal Investigator (solo)
    • News Article: Tufts SoE News
    • Total Amount: \$599,998 for 5 years

  • Covert Cerbrovascular Disease detected by AI

    02/15/24 - 01/31/28

    NIH R01

    • Full title: Covert Cerebrovascular Disease Detected by Artificial Intelligence (C2D2AI): Pragmatic Neuroimaging Biomarkers for Future Stroke and Dementia Risk
    • Goal: Develop biomarkers for brain disease from large dataset of CT and MRI images
    • Team: David Kent (PI, Tufts Medical) and Wansu Chen (co-I, Kaiser Permanente)
    • My role: Lead investigator of ML methods to predict risk of stroke and dementia
    • Total Amount: \$790,627 for first year of 5 year project

  • C2D2AI: Pilot Investigation for Neuroimaging Biomarkers

    10/15/23 - 10/15/25

    Alzheimer's Drug Discovery Foundation (ADDF)

    • Full title: Covert Cerebrovascular Disease Detected by Artificial Intelligence (C2D2AI): Pilot Investigation for Pragmatic Neuroimaging Biomarkers for Future Stroke and Dementia Risk
    • Goal: Develop biomarkers for brain disease from pilot dataset of MRI images
    • My role: Lead investigator of ML methods to predict risk of stroke and dementia
    • Team: David Kent (PI, Tufts Medical) and Wansu Chen (co-I, Kaiser Permanente)
    • Total Amount: \$599,788 over 1.5 years

Past Funding Support

  • ML to Identify Opioid-Related Incidents in EMS Patient Care Reports

    04/01/24 - 03/31/25

    Americal Public Health Association (APHA)

    • Title: Using Natural Language Processing and Machine Learning to Improve Identification of Opioid-Related Incidents in EMS Patient Care Reports
    • Context: Injury and Violence Prevention Data Science Demonstration Project
    • Goal: Develop improved alert systems for opioid overdose events in Lowell, MA
    • Team: Shikhar Shrestha (PI, Tufts Public Health) and Tom Stopka (co-I, Tufts Public Health)
    • My role: Lead of ML methods to identify opioid events from written EMS reports
    • Total Amount: \$200,000 for one year

  • MASTR-E: Machine Learning Models for Human Performance Prediction

    9/1/20 - 8/31/24

    U.S. Army NSRDEC, Natick, MA (via Tufts CABCS)

    • Full title: Statistical and Machine Learning Models for Data Reduction and Human Performance Prediction
    • Part of larger cross-institution MASTR-E project funded by US Army
    • Team: Eric Miller (co-PI, Tufts ECE) and Shuchin Aeron (co-PI, Tufts ECE)
    • My role: Lead investigator of variable selection methods and team dynamics time-series modeling
    • Total Amount: Roughly \$496,098 per year for 5 years (yearly renewal of 1 year contract)

  • Autonomous Cognitive Technologies for Novelty in Open Worlds

    11/15/19 - 5/15/23

    DARPA SAIL-ON Program

  • Amortized Inference for Large-Scale Graphical Models

    9/1/19 - 8/31/22

    NSF CISE: Robust Intelligence: Small

    • Co-Investigators: Liping Liu (PI, Tufts CS) and Thomas Stopka (Tufts Public Health)
    • Total Amount: \$399,923

  • A Benchmark De-identified Echocardiogram Database

    5/1/21 - 4/30/22

    Pilot Grant from Tufts CTSI

    • Full title: A Benchmark De-identified Echocardiogram Database for Studying Automated Diagnoses
    • Co-Investigators: Benjamin Wessler (PI)
    • Tufts CTSI = Clinical and Translational Science Institute

  • Estimating the societal value of COVID-19 therapeutics

    1/1/21 - 12/31/21

    Tufts Medical Center (Originating Sponsor: Pfizer)

    • Co-Investigators: Peter Neumann (Tufts CEVR) and Joshua Cohen (Tufts CEVR)

  • The value of predictive analytics during the COVID epidemic

    7/1/20 - 6/30/21

    Tufts Springboard Award (Tufts Univ. Provost's Office)

    • Full title: Demonstrating the value of a proposed Tufts-led predictive analytics and comparative effectiveness research network during the COVID epidemic
    • Co-Investigators: David Kent (Tufts Medical) and Jessica Paulus (Tufts Medical)
    • Total Amount: \$50,000

  • Estimating Individual Treatment Effects

    7/1/19 - 6/30/20

    Tufts Collaborates Award (Internal)

    • Title: 'Estimating Individual Treatment Effects from Randomized Clinical Trials using Machine Learning'
    • Co-Investigators: David Kent (Tufts Medical Center)
    • Total Amount: \$50,251

Invited Talks

Past Research Experience

  • Assistant Professor of Computer Science

    2018 - present

    Tufts University, Medford, MA

    • Tenure-track role focused on research, teaching, mentorship, and service
    • Research in statistical methods for machine learning, published in AISTATS, NeurIPS, ICML
    • Research in applications of ML to health, published in MLHC, CHIL, clinical journals

  • 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

    2016

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

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

    2016

    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

    2012

    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.

Professional Service

  • Senior Area Chair (co-organizer of entire peer review process)

    • 2024 - CHIL
    • 2023 - CHIL

  • Area Chair

    • 2025 - MLHC
    • 2024 - MLHC
    • 2023 - MLHC and ML4H
    • 2022 - CHIL and MLHC
    • 2021 - CHIL and MLHC

  • Senior Program Committee / Meta-Reviewer

    • 2021 - AAAI
    • 2020 - AAAI

  • Program Committee / Reviewer

    • 2025 - NeurIPS, ICML
    • 2024 - NeurIPS, NeurIPS Datasets&Benchmarks, AISTATS, ICML
    • 2023 - NeurIPS, NeurIPS Datasets&Benchmarks, AISTATS, ICLR
    • 2022 - NeurIPS, NeurIPS Datasets&Benchmarks, AISTATS, ICLR
    • 2020 - NeurIPS, AISTATS, ICLR, MLHC
    • 2019 - NeurIPS (reviewer award), AISTATS, ICLR
    • 2018 - NeurIPS (reviewer award), AAAI, AISTATS, ICLR, AMIA CRI
    • 2017 - NeurIPS, ICML, AAAI
    • 2016 - NeurIPS
    • 2015 - NeurIPS, ICML
    • 2014 - NeurIPS, ICML
    • 2013 - NeurIPS (reviewer award)

  • 2023-2024

    Organizer: Conference on Health, Inference, and Learning (CHIL)

    • Two-day up-and-coming conference with proceedings published in PMLR
    • My role: Organize paper review process as Senior Area Chair
    • Provide continuity across both CHIL '23 and CHIL '24

  • 2021

    Workshop Organizer: Your Model is Wrong at NeurIPS 2021

    • Robust Bayesian methods workshop at NeurIPS '21
    • Full Title: Your Model is Wrong: Robustness and misspecification in probabilistic modeling
    • Full-day virtual workshop with invited keynotes, accepted papers/posters, and lively panel discussions.
    • Helped with peer-review process for accepted papers, hosted/introduced some speakers.

  • 2018

    Workshop Organizer: ML4H at NeurIPS 2018

    • Machine Learning for Health workshop at NeurIPS '18 (NeurIPS ML4H 2018).
    • Full-day workshop with invited keynotes, accepted papers/posters, and lively panel discussions.
    • Provided a forum for interdisciplinary interaction between clinicians, statisticians, and computer scientists.
    • Helped with website, PR, and continuity in peer-review process from previous years.

  • 2018

    Workshop Organizer: BNP at NeurIPS 2018

  • 2017

    Workshop Organizer: ML4H at NeurIPS 2017

    • 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: BNP at NeurIPS 2016

    • 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.

Mentorship

  • Doctoral Mentees as primary adviser, in progress

    Tufts University

    • Patrick Feeney, CS, from 2020 - present
    • - Topic: Self-supervised and Supervised Contrastive Learning in Open Worlds
    • - Papers: TMLR '23, arXiv '24
    • Kyle Heuton, CS, from 2020 - present
    • - Topic: Training spatiotemporal models to learn where to intervene
    • - Papers: ICML '25, Am. J. Epi '24, IMLH workshop '23
    • Ethan Harvey, CS, from 2023 - present
    • - Topic: Time-to-event prediction of dementia from neuroimages
    • - Papers: MLHC '23, TMLR '24, arXiv '25

  • Doctoral Mentees as primary adviser, completed

    Tufts University

    • Zhe Huang, CS, from 2019 - 2024
    • - Topic: Deep Learning with Limited Labeled Data: New Methods and Applications to Echocardiography
    • - Awards: Outstanding Academic Scholarship Award from Tufts School of Engineering (one per year)
    • - Papers: CVPR '24, AISTATS '23 (oral), MLHC '23, MLHC '21, NeurIPS D&B '21
    • - Committee: Prof. B. Mortazavi (Texas A&M); Liping Liu, Rob Jacob, and Ben Wessler, MD
    • Preetish Rath, CS, from 2019 - 2024
    • - Topic: Addressing False Alarms and Missingness in Clinical Prediction Models
    • - Papers: AISTATS '22, IMLH workshop '23, preprint '24
    • - Committee: Prof. Joyce Ho (Emory); Liping Liu, Eric Miller, and Donna Slonim

  • Doctoral Mentees as co-adviser, completed

    Tufts University

    • Kevin Cheng, ECE, from 2019 - 2022
    • - Primary adviser: Eric Miller (Tufts ECE)
    • - Topic: Optimal Transport methods for Time Series modeling
    • - Papers: NeurIPS '21, IEEE Trans Signal Proc. '23, ICASSP '20
    • - Next position: Takeda, Principal AI/ML Research Scientist

  • Postdoctoral Mentees as primary adviser, in-progress

    Tufts University

    • Kaitlin Gili, from 2024 - present
    • - Tufts CS Postdoctoral researcher
    • - Topic: Modeling group dynamics in STEM education
    • - Papers: arXiv '25

  • Postdoctoral Mentees as primary adviser, completed

    Tufts University

    • Michael T. Wojnowicz, from 2020 - 2023
    • - Tufts DISC Data Scientist, primary adviser M. C. Hughes
    • - Papers: ICML '22, AABI '23, arXiv '24
    • - Next position: Research associate at Harvard Univ., Dept. of Biostatistics, advised by Jeff Miller
    • - Current: Tenure-track faculty at Montana State University School of Computing

  • Masters Program Research Mentees

    Tufts University

    • Yu Liu, MS DS with thesis, from 2020 - 2021
    • - Topic: An Evaluation Pipeline for Heterogeneous Treatment Effect Prediction
    • - Next Position: Merck
    • Xi Chen, MS CS with project, from 2020 - 2021
    • - Topic: Bayesian Nonparametric Mixture Models for Missing Data
    • - Next Position: CS PhD student at Rutgers

  • Post-bacc Research Mentees

    Tufts University

    • Ally Lee, BS CS from Tufts, in 2020
    • - Topic: Bayesian Analysis of Autoregressive Models for Multi-Site Hospital Admission Forecasting
    • - Next Position: Software Engineer at Hubspot, Boston, MA
    • Lily H. Zhang, BS from Harvard, post-bacc researcher 2019 - 2020
    • - Topic: 'Any Parameter Encoders for Topic Models: Variational Encoders that amortize across models as well as data'
    • - Next Position: PhD candidate in CS at NYU, advised by Prof. Rajesh Ranganath

  • Undergraduate Research Mentees

    Tufts University

    • Mary-Joy Sidhom, BS CS with honors thesis, in 2022
    • - Topic: Deep Learning for Doppler Echocardiography from Limited Labeled Data
    • - Papers: AISTATS '23 (oral)
    • - Next Position: Software Engineer II at ASML
    • Manh Duc Nguyen, BS CS with honors thesis, in 2019
    • - Topic: Particle-based algorithms for Bayesian neural networks
    • - Next Position: Ph.D. student in CS at U. Penn

  • Harvard University SEAS

    2016-2017

    Research Mentor

    • Mentored undergraduate senior thesis projects on Bayesian nonparametric inference.
    • Frederick Widjaja. 2017 honors thesis: {Streaming Variational Inference for the Indian Buffet Process}.
    • Madhu Vijay. 2017 honors thesis: {Characterizing Posterior Uncertainty for the Indian Buffet Process}.

  • Brown University

    2014-2016

    Research Mentor

Teaching

  • Tufts CS Dept.

    Spring 2025

    Course: CS 135 Intro to Machine Learning

    • Taught core principles of machine learning to 104 students
    • Format: 2 open-ended projects, 5 homeworks (concept questions and code exercises), 2 exams

  • Tufts CS Dept.

    Fall 2024

    Course: CS 152 Learning from Limited Labeled Data

    • Taught research seminar course to 35 students
    • Format: two homeworks plus open-ended team research project

  • Tufts CS Dept.

    Spring 2024

    Course: CS 136 Statistical Pattern Recognition

    • Taught advanced statistical learning course to 31 students
    • Format: 4 units on fundamentals (math-intensive homeworks, coding assignments, and quizzes) plus project

  • Tufts CS Dept.

    Fall 2023

    Course: CS 135 Intro to Machine Learning

    • Taught core principles of machine learning to 101 students
    • Format: 2 open-ended projects, 5 homeworks (concept questions and code exercises), 2 exams

  • Tufts CS Dept.

    Spring 2023

    Course: CS 136 Statistical Pattern Recognition

    • Taught advanced statistical learning course to 36 students
    • Format: 5 math-intensive homeworks, 5 coding-intensive homeworks, 5 quizzes

  • Tufts CS Dept.

    Fall 2022

    Course: CS 152 Bayesian Deep Learning

    • Taught special topics course to 26 students
    • Format: weekly homeworks for first month, then 2-month open-ended team project

  • Tufts CS Dept.

    Spring 2021

    Course: COMP 136 Statistical Pattern Recognition

    • Taught advanced statistical learning course to 29 students
    • Format: 5 math-intensive homeworks, 5 coding-intensive homeworks, 5 short quizzes, 2 exams

  • Tufts CS Dept.

    Fall 2020

    Course: COMP 135 Intro to Machine Learning

    • Taught core principles of machine learning to 95 students
    • Format: 3 open-ended projects, 5 homeworks (conceptual and code questions), and 5 quizzes

  • Tufts CS Dept.

    Spring 2020

    Course: COMP 136 Statistical Pattern Recognition

    • Taught advanced statistical learning course to 35 students
    • Format: 5 math-intensive homeworks, 5 coding-intensive homeworks, 5 short quizzes, 2 exams

  • Tufts CS Dept.

    Fall 2019

    Course: COMP 150 Bayesian Deep Learning

    • Taught advanced topics seminar to 23 students
    • Format: weekly homeworks for first month, then 2-month open-ended team project

  • Tufts CS Dept.

    Spring 2019

    Course: COMP 135 Introduction to Machine Learning

    • Taught core principles of machine learning to about 50 students
    • Format: 3 open-ended projects, weekly homeworks, and 2 exams

  • Tufts CS Dept.

    Fall 2018

    Course: COMP 150 Bayesian Deep Learning

    • Taught advanced topics seminar to about 18 students
    • Format: weekly homeworks for first month, then 2-month open-ended team project
    • One project resulted in publication at IEEE conference (ICDL-EpiRob 2019)

  • Fall 2013

    Lead Graduate TA for CS 142: Intro to Machine Learning

    • Led weekly 1 hour recitation session to review key concepts for 50+ students.
    • Designed homework assignments and exam questions.

Outreach Experience

  • Tufts DIAMONDS Program , Medford, MA

    2021-2025

    Research Mentor

    • Mentored two undergraduate students per summer in data science research projects.
    • Outcomes from '23: mentee Jyontika Kapoor admitted to Northwestern PhD program in Statistics
    • Outcomes from '21: mentee Christopher Slaughter wins Goldwater Scholarship, Gates Fellowship

  • TEALS and Boston Latin Academy , Roxbury, MA

    2014-2016

    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

    2014

    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 available on YouTube

  • Olin College Engineering Discovery , Needham, MA

    2007-2010

    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.

Highlighted Preprints

Peer-Reviewed Conference Publications (in reverse chronological order)

Superscripts indicate mentored student's status: u = undergraduate, m = masters, d = Ph.D. student, b = post-bacc, c = medical student. Complete publication list at end of this document.

Peer-Reviewed Journal Publications (in reverse chronological order)

All Workshop Papers (in reverse chronological order)