Our Research    Funded Projects


Machine Learning for Home-Based Ultrasonic Ovarian Cancer Screening

Prof. Yonina Eldar,

Prof. Anthony E. Samir

This project is targeted at developing new methods and systems to detect ovarian cancer at the early curable stage before it has spread beyond the ovary in order to drastically improve ovarian cancer survival.

Shaping the Future of Disease Diagnostics

Prof. Eric Alm, Prof. David Sontag, Dr. Andrew Allegretti, Prof. Jordan Smoller

This project aims to improve healthcare decisions in challenging diagnostic areas. We envision the development of a non-invasive molecular test, effective across many disease areas, returning results rapidly.

Causal Experimentation and Modeling with Uncertain Disease Labels

Prof. Caroline Uhler, 

Dr. Ernest Fraenkel

This project will address the challenges of how data is collected and analyzed on Amyotrophic Lateral Sclerosis (ALS) using algorithms to design experiments on neurons derived from ALS patients.

iBOCA: A Machine Learning App for Early Detection of Cognitive Impairment

Dr. Kalyan Veeramachaneni, Prof. Saman Amarasinghe, Dr. Chun Lim, Dr. Dan Press, Dr. John Torous

This project will develop a machine learning based iPad app for doctors to administer cognitive tests for assessing mild cognitive impairment. Our goals are (1) to develop machine learning based models for early detection and diagnosis, and (2) to enhance the app to collect more detailed data that will in turn enable rapid detection within 6 minutes.

Disease Monitoring

Cardiovascular Data Science for Personalized Medicine

Prof. Collin M. Stultz,

Dr. Aaron D. Aguirre

This project will build and test models that identify personalized therapeutic interventions that minimize adverse outcomes in patients who are admitted with congestive heart failure.

Improving the Accuracy of Personalized Machine Learning to Monitor Depression

Prof. Rosalind W. Picard, 

Dr. Paola Pedrelli

This project will create a novel method to assess depressive symptoms for individuals by using machine learning analytics applied to objective data from phone sensors and wrist-band sensors. This machine learning work thus has the potential to significantly impact treatment of the foremost cause of disability.

Harnessing Signals of Clinical Effectiveness from Electronic Health Records (EHR) Data to Repurpose Existing Drugs for Unmet Medical Needs

Prof. Roy Welsch, Prof. Stan Finkelstein

This project has a clinical goal to identify signals of clinical effectiveness from EHR data to repurpose currently marketed drugs. And also a methodological goal to create machine learning algorithms that use observational data to compare cohorts of patients to emulate clinical trials.

Nocturnal Seizure Detection and Prediction in Epileptic Patients Using Radio Signals 

Prof. Dina Katabi, Prof. Tim Morgenthaler, Dr. Melissa Lipford, Dr. Mithri Junna

In this project, we will augment the Emerald WiFi-like device with new machine learning models to detect epilepsy seizures simply by analyzing the surrounding wireless signals. We will also use the Emerald device to monitor the relationship between seizures and the patient’s vital signs and sleep stages. We will further study the feasibility of predicting seizures before they occur.

Preventive Medicine

Deep Generative Models for Cryo-EM Reconstruction of Heterogeneous Biomolecular Structures

Prof. Joey Davis, Prof. Bonnie Berger, Prof. Bridget Carragher, Prof. Clint Potter

In this project we propose to develop revolutionary machine learning based techniques to determine structural ensembles of “misfolded” complexes. This work leverages the Berger Lab’s expertise in such computational approaches, MIT’s recent investments in cryo-EM instrumentation (MIT.nano), and Davis Lab’s expertise in producing, isolating and imaging such molecules.

Machine Learning Based Noninvasive Continuous Absolute Blood Pressure Monitoring

Prof. Hae-Seung Lee, Prof. Charles G. Sodini, Prof. Song Han, Dr. Aaron D. Aguirre

In this project, we investigate a new ML based continuous absolute blood pressure (BP) waveform monitoring method. This is based on our on-going research on pulse pressure monitoring using ultrasonography, which yields a continuous BP waveform instead of just systolic and diastolic pressure levels.

A Deep Learning System to Identify Combinatorial MEG and PET-based Biomarkers for Early Detection and Monitoring of Alzheimer’s Disease

Dr. Dimitrios Pantazis, Prof. Quanzheng Li, Prof. Fernando Maetsu

This proposal aims to first construct and monitor biomarkers using combined brain imaging modalities, then use a deep learning model that integrates connectome-structured data to discover multimodal features that predict an individual’s risk for AD.

Application of AI/Machine Learning to Clinical Mental Health: Depression, Bipolar Disorder, and Anxiety

Prof. John Gabrieli,  Prof. Peter Szolovits, Prof. Joseph Biederman, Prof. Mai Uchida, Prof. Dina Hirshfeld-Becker, Prof. Stefan Hofmann

The goal of the present project is to create a new AI / machine learning bridge between one research group in the area of the diagnosis and treatment of mental disorders and another group in the Computer Science and Artificial Intelligence Lab (CSAIL). 

Semi-Supervised Deep Learning for Red Blood Cell Morphological Classification with Applications to Sickle Cell Disease and Hyposplenism

Dr. Ming Dao, Dr. Dimitrios Papageorgiou, Dr. Pierre Buffet, Prof. Olivier Hermine

In this project we propose to develop a robust fully-automated real-time Red blood cells (RBC) classification method with applications to sickle cell disease (SCD) and hyposplenism (impaired spleen function), based on semi-supervised deep learning approach using generative adversarial network (GAN).

Project InCyte: Enhancing Histopathological Diagnosis with Digitization and Machine Learning

Prof. Pawan Sinha, Dr. Kyle Keane, Dr. Prerna Tewari

In this project we will digitize the stored collections of pathology slides in Indian hospitals, and then use the resulting collection of images to train computational classifiers to infer disease labels from histo- and cyto-morphology. Additionally, we believe this will allow us to understand the trade-off between the quality of archiving processes and the long-term predictive value of data sources.

Clinical Operations

Interpretable and Transferable Prediction and Extraction Methods for Medical Reports

Prof. Tommi S. Jaakkola, 

Prof. Kevin Hughes

In this project we focus on realizing and testing automated tools to reason about and extract information from medical reports and records. We design tools that are interpretable, verifiable, and transferable.

AI-­Driven Diagnosis with Documentation in the Emergency Department 

Prof. David Sontag, 

Prof. Steven Horng

This project seeks to re­-envision clinical documentation as cognitive aid by the creation of AI­-driven user interfaces that are tightly integrated into the clinical workflow.

Data Privacy

Private and Scalable Collaboration on Medical Images

Prof. Vinod Vaikuntanathan

In this project we propose to apply powerful methods in cryptography, called secure multiparty computation and homomorphic encryption, to design nimble solutions to the privacy-usefulness conundrum, computing together on the distributed data and models, while guaranteeing privacy to the individual stakeholders.

Drug Discovery

Deep Learning Peptide Representations for Optimal Cellular Delivery of Gene-Editing Cargo

Prof. Rafael Gomez-Bombarelli, Prof. Bradley L. Pentelute

In this project we will train convolutional neural networks on novel 2D representations of peptides that include variational topological representations of natural and unnatural amino acids.

To read more about the PI Projects and see the associated published papers, click here