MIT Clinical and Applied Machine Learning Group

We develop machine learning techniques with clinical inspiration and real-world relevance. Our current research touches on computer vision, fairness, optimization and causality in networks.


Principal Investigator

Research Scientists

PhD Students

Lab Alumni


Amy Zhao (Oculus)
Guha Balakrishnan (postdoc @ MIT)
Joel Brooks (Righthand Robotics)
Jen J. Gong (postdoc @ Berkeley)
Yun Liu (Google)
Anima Singh (Google)
Zeeshan Syed (University of Michigan and Google)

Gartheeban Ganeshapillai (BlackRock)
Jenna Wiens (University of Michigan)
Alaa Kharbouch (Verizon Big Data Analytics Group)
Asfandyar Qureshi (Google)
Eugene Shih (Cambridge Mobile Telematics, Inc.)
Ali Shoeb (Google)


Udgam Goyal
Courtney Guo
Advaith Anand
Yasyf Mohemedali
Akhil Nistala
Maryann Gong
Akhil Nistala

Visiting Students

Leo Lin (Wistron)

Current Projects

VoxelMorph: Unsupervised Learning for Image Registration

VoxelMorph is a fast, unsupervised, learning-based framework for deformable, pairwise medical image registration. VoxelMorph combines a probabilistic model with unsupervised learning that makes use of recent developments in convolutional neural networks (CNNs). This results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees and uncertainty estimates.

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Anatomical Priors in Convolutional Networks for Biomedical Segmentation

In this project we specifically address the frequent scenario where we have no (or very few) paired training data that contains images and their manual segmentations. We explore generative probabilistic models that employ priors learned from external data through convolutional neural networks, enabling segmentations in unsupervised or semi-supervised settings.

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Unsupervised Data Imputation

In this project we focus on unsupervised estimation of missing image data, where no full observations are available - a common situation in practice. Unsupervised imputation methods for images often employ a simple linear subspace to capture correlations between data dimensions, ignoring more complex relationships. Instead, we work with a general probabilistic model that describes sparse high dimensional imaging data as being generated by a deep non-linear embedding.

and more!

Recent News

Producing better guides for medical-image analysis

With their model, researchers were able to generate on-demand brain scan templates of various ages (pictured) that can be used in medical-image analysis to guide disease diagnosis. Read on MIT News →

Recovering “lost dimensions” of images and video

A novel model developed at MIT recovers valuable data lost from images and video that have been “collapsed” into lower dimensions. It can, for instance, recreate video from motion-blurred images or from cameras that capture people’s movement around corners as vague one-dimensional lines. Read on MIT News →

Using machine learning to estimate risk of cardiovascular death

Using just the first 15 minutes of a patient's electrocardiogram (ECG) signal, an MIT system produces a score that places patients into different risk categories. Read on MIT News →

Recent Publications


M. Rakic, J. Guttag, A.V. Dalca,Anatomical Predictions Using Subject-specific Medical Data, Medical Imaging with Deep Learning (MIDL), July 2020.

A. Zhao, G. Balakrishnan, K. M. Lewis, F. Durand, J. Guttag, A.V. Dalca,Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings, CVPR, June 2020.

K. Lewis, N, Rost, J. Guttag, A.V. Dalca,Fast Learning-based Registration of Sparse 3D Clinical Images, ACM Conference on Health, Inference, and Learning (CHIL), April 2020.

D. Blalock, J.J.G. Ortiz, J. Frankel, and J. Guttag,What Is the State of Neural Network Pruning, Machine Learning and Systems, March 2020.


A.V. Dalca, M. Rakic, J. Guttag, M.R. Sabuncu,Learning Conditional Deformable Templates with Convolutional Networks, NeurIPS, December 2019.

G. Balakrishnan, A.V. Dalca, A. Zhao, J. Guttag, F. Durand, W. T. Freeman,Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions for Images and Videos, ICCV, October 2019.

D. Shanmugam, D. Blalock, and J. Guttag,Multiple Instance Learning for ECG Risk Stratification, Machine Learning for Healthcare, August 2019.

J.J.G Ortiz, D. Mehta, J. Van Stan, R.E., Hillman, J. Guttag, and M. Ghassemi,Learning from Few Subjects with Large Amounts of Voice Monitoring Data, Machine Learning for Healthcare, August 2019.

A.V. Dalca, G. Balakrishnan, J. Guttag, M.R. Sabuncu,Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces, Medical Image Analysis, July 2019.

A. Soleimany, H. Suresh, J.Ortiz, D. Shanmugam, N. Gural, J. Guttag. and S. Bhatia,Image Segmentation of Liver Stage Malaria Infection with Spatial Uncertainty Sampling, ICML Workshop on Computational Biology, June 2019.

A. Zhao, G. Balakrishnan, F. Durand, J. Guttag, A.V. Dalca, Data Augmentation with Spatial and Appearance Transforms for One-shot Medical Image Segmentation, CVPR, June 2019.

A. Nistala and J. Guttag,Using Deep Learning to Understand Patterns of Player Movement in the NBA, Sloan Sports Analytics Conference, March 2019.

G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, A.V. Dalca,VoxelMorph: A Learning Framework for Deformable Medical Image Registration, IEEE Transactions on Medical Imaging, January 2019.

J.P. Cortes, V. Espinoza, M. Ghassemi, D. Mehta, J. Van Stan, R. Hillman, J. Guttag, and M. Zanartu,Ambulatory Assessment of Phonotraumatic Vocal Hyperfunction Using Glottal Airflow Measures Estimated from Neck-surface Acceleration, PLOS One, January 2019.


D. Blalock, S. Madden, and J. Guttag,Sprintz: Time Series Compression for the Internet of Things, ACM Journal on Interactive, Mobile, Wearable, and Ubiquitous Technologies, September 2018.

A.V. Dalca, G. Balakrishnan, J. Guttag, and M. R. Sabuncu,Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration, MICCAI, September 2018.

J. Gong and J. Guttag,Learning to Summarized Electronic Health Records Using Cross-Modality Correspondences, Machine Learning in Healthcare, August 2018.

H. Suresh, J. Gong, and J. Guttag, “Learning Tasks for Multitask Learning: Heterogeneous Patient Populations in the ICU," KDD: Knowledge Discovery in Databases, August 2018.

G. Balakrishnan, A. Zhao, A.V. Dalca, F. Durand, J. Guttag, “Synthesizing Images of Humans in Unseen Poses,” CVPR: Conference on Computer Vision and Pattern Recognition, June 2018.

G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, and A.V. Dalca, “An Unsupervised Learning Model for Deformable Medical Image Registration,” CVPR: Conference on Computer Vision and Pattern Recognition, June 2018 (to appear).

A.V. Dalca, J. Guttag, and M. R. Sabuncu, “Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation, CVPR: Conference on Computer Vision and Pattern Recognition,” June 2018.

J.P. Cortes, V. Espinoza, M. Ghassemi, D. Mehta, J. Van Stan, R. Hillman, J. Guttag, and M. Zañartu,

“Using Aerodynamic Features and Their Uncertainty for the Ambulatory Assessment of Phonotraumatic Vocal Hyperfunction,” IEEE International Conference on Biomedical and Health Informatics, March 2018.

J. Oh, M. Makar, C. Fusco, R. McCaffrey, K. Rao, E. Ryan, L. Washer, L. West, V. Young, J. Guttag, D. Hooper, E. Shenoy, and J. Wiens, “A generalizable, data-driven approach to predict daily risk of Clostridium difficile infection at two large academic health centers,” Infection Control & Hospital Epidemiology, 2018 (to appear).

M. Makar, J. Wiens, and J. Guttag, “Learning the Probability of Activation in the Presence of Latent Spreaders,” AAAI, February 2018.


R. Jaroensri, A. Zhao, G. Balakrishnan, D. Lo, J. Schmahmann, F. Durand, and J. Guttag, “A Video-based Method for Automatically Rating Ataxia,” Machine Learning in Healthcare, August 2017.

D. Blalock and J. Guttag, “Bolt: Accelerated Data Mining with Fast Vector Compression,” KDD 2017, August 2017.

J. Gong, T. Naumann, P. Szolovits, and J. Guttag, “Predicting Clinical Outcomes Across Changing Electronic Health Record Systems,” KDD 2017, August 2017.


D. Blalock and J. Guttag, “EXTRACT: Strong Examples from Weakly-labeled Sensor Data,” ICDM 2016, December 2016.

J. Brooks, M. Kerr, and J. Guttag, “Developing a Data-driven Player Ranking in Soccer Using Predictive Model Weights, KDD 2016, August 2016.

J. Gong, M. Gong, D. Levy-Lambert, J. Green, T. Hogan, and J. Guttag, “Towards an Automated Screening Tool for Developmental Speech and Language Impairments,” Interspeech 2016, September 2016.

M. Ghassemi, J. Hillman, D. Mehta, J. Van Stan, Z. Syed, and J. Guttag, “Uncovering Voice Misuse Using Symbolic Mismatch,” Machine Learning in Healthcare, August 2016.

Y. Liu, C. Stultz, and J. Guttag, “Transferring Knowledge from Text to Predict Disease Onset,” Machine Learning in Healthcare, August 2016.

J. Brooks, M. Kerr, and J. Guttag, “Using Machine Learning to Draw Inferences from Pass Location Data in Soccer,” Statistical Analysis and Data Mining, August 2016.

J. Wiens, J. Guttag, and E. Horvitz, “Patient Risk Stratification with Time-varying Parameters: a Multitask Learning Approach, Journal of Machine Learning Research, 17(77), June 2016.

A. McIntyre, J. Brooks, J. Guttag, and J. Wiens, “Recognizing and Analyzing Ball-screen Defense in the NBA,” Sloan Sports Analytics Conference, March 2016.

J.P.  Cortes, V. Espinoza, M. Zanartu, M. Ghassemi, J. Guttag, D. Mehta, J. Van Stan, and R. Hillman, “Discriminating patients with vocal fold nodules from matched controls using acoustic and aerodynamic features from ambulatory voice monitoring data,” 10th International Conference on Voice Physiology and Biomechanics, March 2016.