Welcome! My research spans statistical machine learning and its applications in healthcare and the sciences.
I am an Assistant Professor in the Dept. of Computer Science at Tufts University and a primary faculty member for the Tufts Machine Learning research group. Previously, I was a postdoctoral fellow in computer science at Harvard SEAS, advised by Prof. Finale Doshi-Velez. I did my Ph.D. in CS at Brown University in May 2016, advised by Prof. Erik Sudderth (now at UC-Irvine).
Research Highlights
My lab pursues advances to core machine learning methods as well as high-impact applications.
First, I'm interested in fundamental problems in statistical machine learning:
- probabilistic models for multivariate time-series with irregular or missing data
- scalable inference for latent variable models (see our AABI 2019 paper)
- effective semi-supervised learning with latent variable models (see our AISTATS 2018 paper, Time Series Workshop 2021 paper), and arXiv preprint from 2020)
- Bayesian nonparametric approaches to growing model complexity as data demands (see our BNPy Python package and NeurIPS 2015 paper)
- model-based reinforcement learning with open-world, partially-observable state (see our AISTATS 2020 paper)
- optimizing deep learning models to be more interpretable (see our AAAI 2020 and AAAI 2018 papers)
Second, I'm interested in several exciting clinical applications of these techniques:
- predicting deterioration and need for interventions in the Intensive Care Unit (ICU) (see our CHIL 2020 paper and IMLH 2021 workshop paper)
- forecasting demand for hospital resources during the COVID-19 pandemic (see our MLHC 2021 paper)
- automating preliminary diagnosis of heart disease using echocardiograms (see our MLHC 2021 paper)
- training models that generalize well across dataset shift or across subjects (see our MLHC 2019 paper or a non-medical take in our UIST 2021 paper)
- recommending stable antidepressants personalized to patients with major depression (see our JAMA Network Open 2020 journal paper)
For more, see my Research page
News
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[Aug 2021] Two papers from HughesLab appearing at MLHC 2021
- Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories [PDF]
- This is a new model for forecasting daily demand for hospital beds from COVID-19 patients.
- Led by first author Gian Marco Visani (Tufts CS undergrad '21)
- With key help two other student co-authors: Ally Lee and Cuong Nguyen
- With two collaborators from Tufts Medical Center: David Kent, MD and John Wong, MD (both clinician-researchers)
- With one collaborator from the Center for the Evaluation of Value and Risk in Health at TMC: Josh Cohen , a researcher in applied math/decision sciences/health economics
- A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms [PDF]
- This is a new dataset release in pursuit of two goals: (1) improved early detection of heart disease (aortic stenosis) and (2) improved methods that can learn from both limited labeled datasets and abundant uncurated unlabeled data via semi-supervised learning
- Data and code available at https://TMED.cs.tufts.edu
- Led by first author Zhe Huang (Tufts CS Ph.D. student)
- With co-PI and key collaborator from Tufts Medical Center: Dr. Ben Wessler, MD
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[July 2021] New preprint on easy conjugate Bayesian methods for categorical data
- Will be at the Tractable Probabilistic Modeling (TPM) workshop (co-located with UAI) to present our paper on Easy Variational Inference for Categorical Observations via a New View of Diagonal Orthant Probit Models, led by Mike Wojnowicz (Tufts DISC)
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[July 2021] Three papers accepted to workshops at ICML
- Will be at the Interpretable Machine Learning for Healthcare (IMLH) workshop to present our paper on Optimizing Clinical Early Warning Systems to Meet False Alarm Constraints, led by Ph.D. student Preetish Rath
- Will be at the Uncertainty and Robustness in Deep Learning (UDL) workshop to present our paper on Evaluating the Use of Reconstruction Error for Novelty Localization, led by Ph.D. student Patrick Feeney
- Will be at the Time Series Workshop (TSW) to present our paper on Prediction-Constrained Hidden Markov Models for Semi-Supervised Classification, led by UC-Irvine Ph.D. student Gabe Hope. UPDATE: Gabe's poster received the Best Poster award at the TWS workshop!
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[July 2021] Work on Graph Matching accepted at ICML 2021
- Check out our paper on Stochastic Iterative Graph Matching (SIGMA) a probabilistic approach to finding a correspondence between the nodes of two similar graphs, with several collaborators from Tufts CS: Ph.D. student Linfeng Liu and faculty colleagues Soha Hassoun and Liping Liu
- Cool evaluations on applications from systems biology and computer vision
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[Apr 2021] New work on probabilistic models for COVID-19 forecasting, with collaborators from Tufts Medical Center
- Preprint on a new ACED-HMM model for COVID-19 hospitalized patient trajectories , led by first author Gian Marco Visani (Tufts CS undergrad '21)
- Workshop paper on COVID-19 patient count forecasting at a single hospital, led by first author Alexandra Lee (Tufts CS undergrad '20)
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[Dec 2020] Invited Talk at I Can't Believe It's Not Better Workshop at NeurIPS 2020
- I'll be an invited speaker at the I Can't Believe It's Not Better! Workshop at NeurIPS 2020
- I'll talk about our recent work on Prediction Constrained training, covering in progress work (Hope et al. arXiv 2020) as well as recent publications at AISTATS 2018 and AISTATS 2020.
- Video recording
- Slides: The Case For Prediction Constrained Training [PDF]
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[May 2020] Paper applying our latest ML methods to psychiatry accepted to JAMA Network Open
- Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models
- Read this to understand how to use our latest prediction-constrained topic models to recommending personalized antidepressants.
- Full paper and supplement available at jamanetwork.com
- Special thanks to awesome clinical collaborators Roy Perlis MD and Tom McCoy MD.
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[Jan 2020] Paper accepted at AISTATS 2020
- POPCORN: Partially Observed Prediction-Constrained Reinforcement Learning by Joseph Futoma, Michael C. Hughes, and Finale Doshi-Velez
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[Dec 2019] Two papers accepted at AABI 2019
- Amortized Variational Inference for Rapid Model Comparison , with first author Lily Zhang
- Challenges in Computing and Optimizing Upper Bounds of Marginal Likelihood based on Chi-Square Divergences , with Melanie F. Pradier and Finale Doshi-Velez
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[Nov 2019] Paper Accepted at AAAI 2020
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[Aug 2019] Paper Accepted at MLHC 2019
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[June 2019] Invited Talk at BNP 12
- "Scalable and Reliable Variational Inference for Dirichlet Process Clustering with Sparse Assignments"
- "Venue: \\href{https://www.stats.ox.ac.uk/bnp12/programme.html}{12th International Conference on Bayesian Nonparametrics}"
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[Dec 2018] Two short papers accepted to workshops at NeurIPS 2018
- Prediction-Constrained POMDPs at RLPO @ NeurIPS2018
- Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation at ML4Health @ NeurIPS 2018
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[Aug 2018] I'm organizing 2 Workshops at NeurIPS 2018:
- All of Bayesian Nonparametrics (BNP @ NeurIPS 2018)
- Machine Learning for Health (ML4H @ NeurIPS 2018)
Please consider submitting a short paper!
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[Aug 2018] I'll present a tutorial -- "Machine Learning for Clinicians: Advances for Multi-Modal Health Data" -- at MLHC 2018.
Goal: make healthcare professionals aware of the latest tools from ML and help them be research effective collaborators. Please visit the Tutorial Website (with outline, slides, and full bibliography).
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[Aug 2018] I have joined the faculty at Tufts' Computer Science Department!
I'm actively looking for students (ugrad and Ph.D.) for various research projects. Please contact me if interested.
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[Apr 2018] Best Paper Award at SoCalNLP 2018
Our winning 2-page short paper was a compact summary of our AISTATS 2018 paper: Semi-Supervised Prediction-Constrained Topic Models. Thanks to co-author Gabe for presenting the work, to the SoCal NLP organizers for hosting, and to Amazon for sponsoring the award.
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[Jan 2018] Paper accepted to AISTATS 2018.
Our paper -- Semi-Supervised Prediction-Constrained Topic Models -- describes a new framework for training topic models and other latent variable models to improve supervised predictions while still providing good generative models with interpretable topics. The new approach fixes core issues with past methods like sLDA, and shines especially in semi-supervised tasks, when only a small fraction of training examples are labeled.
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[Dec 2017] Presenting at NIPS 2017 Workshops
• Poster: Prediction-constrained Topic Models for Antidepressant Recommendation at ML for Health Workshop (NIPS ML4H 2017)
• Poster and Talk (by Mike Wu): Optimizing deep models with tree-regularization at Transparent and Interpretable ML workshop (NIPS TIML 2017) (will also appear in AAAI '18)
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[Nov 2017] Paper accepted to AAAI 2018.
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability [PDF]
Our paper describes a new regularization method to
optimize recurrent neural networks to have more interpretable decision boundaries (closer to the decision trees that clinicians like). -
[Nov 2017] Invited talk at MIT Lincoln Laboratory
"Optimizing Machine Learning Models for Clinical Interpretability"
Slides: [slides.pdf, 5 MB]
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[Sep 2017] Organizing Machine Learning for Health (ML4H) workshop at NIPS 2017
Please submit some awesome papers!
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[Mar 2017] Presented paper on ICU intervention prediction at AMIA CRI '17
Nominated for Clinical Informatics Research Award (1 of 7 nominees)
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[Feb 2017] Invited talk on BNPy at Boston Bayesians meetup
Event website and talk abstract
Slides from my talk: [slides.pptx, 73 MB] [slides.pdf, 23 MB]
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[Dec 2016] BNPy software tutorial at NIPS 2016 workshop
Slides from my talk: [slides.pptx] [slides.pdf]
New: BNPy project website with example gallery
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[Dec 2016] Posters at NIPS 2016 Workshops
Fast per-document inference for supervised topic models at ML for Health workshop.
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[Sep 2016] Organizing Workshop at NIPS '16: Practical Bayesian Nonparametrics
Please consider submitting to our workshop: https://sites.google.com/site/nipsbnp2016/
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[Aug 2016] Started post-doc at Harvard
You can now find me at my new office in Maxwell-Dworkin (MD 209).
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[May 2016] Successful Ph.D. defense!
Many thanks to family and friends who supported me along the way.
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[Jan 2016] Invited talks on my thesis.
I visited several research groups at Northeastern, U. Washington, and MIT to discuss results from my thesis work trying to make effective variational inference for clustering that scales to millions of examples. [slides PDF] [slides PPTX]
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[Dec 2015] Invited talk at NIPS 2015 workshop.
I gave an invited talk at the Bayesian Nonparametrics: The Next Generation workshop about my thesis work building effective variational inference for models based on the Dirichlet process and its hierarchical variants. [slides PDF]
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[Sept 2015] Paper accepted at NIPS 2015.
Our paper [PDF] describes a new algorithm for Bayesian nonparametric hidden Markov models that can handle hundreds of sequences and add or remove hidden states during a single training run.
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[May 2015] Paper accepted at AISTATS 2015.
Our paper [PDF] describes a new algorithm for topic models that can effectively remove redundant or junk topics during a single training run.
