News & Events

The integration and analysis of electronic health record (EHR) data across institutions present both significant challenges and transformative opportunities for biomedical research. This talk outlines the development and application of federated data networks such as I2B2 and SHRINE, which enable real-time, privacy-preserving queries across distributed EHR systems. These platforms support large-scale cohort discovery and have been instrumental in initiatives like the EnACT network and the COVID-19-focused 4CE consortium. In contrast, centralized repositories like the NIH’s N3C and Epic’s Cosmos database offer comprehensive datasets conducive to machine learning but may obscure site-specific variability. 

The All of Us Research Program, a flagship initiative of the NIH, aims to build one of the most comprehensive and diverse biomedical data resources in the world by enrolling over one million participants across the United States. Dr. Jordan Smoller, a lead investigator in the program, outlines its structure, scope, and transformative potential for advancing precision medicine. With over 850,000 participants enrolled and more than 630,000 contributing data, the program integrates electronic health records (EHRs), genomic data, physical measurements, surveys, and wearable device data. 

As machine learning (ML) becomes increasingly integrated into healthcare, ensuring ethical, fair, and robust deployment is critical. Dr. Marzyeh Ghassemi and the Healthy ML Lab at MIT investigate the challenges and opportunities of applying ML in clinical settings, with a focus on fairness, privacy, and real-world impact. Through case studies in medical imaging and clinical prediction, her work highlights how models trained on large datasets can exhibit significant disparities in performance across demographic subgroups, including age, race, gender, and insurance status.

Advances in soft wearable robotics and machine learning are enabling new approaches to assist and assess human movement, particularly for aging populations and individuals with neurological impairments. Dr. Conor Walsh and his team at the Harvard Biodesign Lab have developed a suite of soft robotic systems designed to support mobility, rehabilitation, and functional independence in real-world settings. 

Memory performance exhibits substantial moment-to-moment variability, much of which cannot be explained by external factors alone. Dr. Michael Kahana’s research explores the neural underpinnings of this variability and leverages machine learning and intracranial brain recordings to develop targeted interventions for memory enhancement. Through the DARPA-funded Restoring Active Memory (RAM) project, Kahana’s team collected the largest open-access dataset of human intracranial recordings during memory tasks, enabling the development of predictive models that decode memory states in real time.

Early detection of cognitive decline is critical for effective intervention in Alzheimer’s disease and related dementias. Traditional neuropsychological assessments, while useful for diagnosing impairment, are limited in their ability to detect subtle, preclinical changes. Dr. Martin Sliwinski and colleagues propose a novel approach using ultra-brief, mobile cognitive assessments embedded in daily life through ecological momentary assessment (EMA). This method captures high-frequency, real-world data on cognitive performance, enabling the detection of short-term variability and long-term trends. By integrating these assessments with contextual data (e.g., stress, social engagement, physical activity), researchers can model dynamic cognitive processes and identify early signs of decline.

The drug discovery process remains protracted, costly, and inefficient, particularly in the context of neurodegenerative diseases such as Alzheimer's and Huntington's, where therapeutic development has seen limited success. In this keynote, Dr. Jianying Hu presents a comprehensive overview of how artificial intelligence (AI), including classical machine learning and emerging foundation models, is transforming the landscape of drug discovery. She outlines AI-driven strategies across the drug development pipeline—from target identification and molecular generation to disease progression modeling and clinical trial optimization. Highlighted applications include the use of hidden Markov models to construct integrated disease progression models for Huntington’s and Parkinson’s diseases, enabling nuanced patient stratification and improved trial design.