CV for Michael C. Hughes

PDF | source | Last updated: April 03 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, published in AISTATS, NeurIPS, ICML
    • Research in applications of ML to health, published in MLHC, CHIL, clinical journals
    • Research in applications of ML to STEM education
    • Mentorship as of 2025: Actively advise 3 full-time Ph.D. students and 1 post-doc
    • Teaching: Taught core data science courses: CS 135 Intro to ML and CS 136 Statistical Pattern Recog.
    • Teaching: Developed new courses for grad students: Bayesian Deep Learning and Learning from Limited Labeled Data

Research Expertise

  • 2012-

    Machine Learning

    • Unsupervised learning, Semi-supervised learning, Supervised learning
    • Probabilistic models: Bayesian nonparametrics, topic models, hidden Markov models, deep generative models
    • Estimation methods for posterior distributions: variational methods, Markov chain Monte Carlo

  • 2017-

    Applications of ML to Healthcare

    • Early warning systems for predicting mortality/deterioration in hospitalized patients
    • - Consume EHR time series of labs, vitals, diagnosis and procedure codes
    • 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
    • Modeling demand for hospital resources over time due to COVID-19 pandemic

  • 2012-

    Human Activity Analysis

    • Sensor time-series, motion capture, video, images

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

Highlighted Publications

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.

Highlighted Preprints

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

  • 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

  • Covert Cerbrovascular Disease detected by AI

    02/15/24 - 01/31/28

    NIH R01

    • Full title: 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

  • 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

  • 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

  • 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

    • 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, SINCERE (arXiv '24)
    • Kyle Heuton, CS, from 2020 - present
    • - Topic: Training spatiotemporal models to learn where to intervene
    • - Papers: Am. J. Epi '24, IMLH workshop '23
    • Ethan Harvey, CS, from 2023 - present
    • - Topic: Time-to-event prediction of dementia from neuroimages

  • 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
    • - 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 '25
    • - 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
    • Hezekiah Branch, BS CS at Tufts, in 2020
    • - Tufts LSAMP fellow
    • - Topic: Supervised Learning for Clinical Multivariate Time Series

  • 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.

    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: 3 open-ended projects, 5 homeworks (conceptual and code questions), 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

    2021-2024

    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.

All Conference Publications (in reverse chronological order)

All Journal Publications (in reverse chronological order)

All Workshop Papers (in reverse chronological order)