Data Science and AI Approaches to Predict Human Cognitive Function and Disease Trajectories

The Center for Brain Recovery collaborates across disciplines with data scientists to leverage the power of artificial intelligence, machine learning, and computing to better understand and predict the trajectory of cognitive function influenced by neurological conditions.

Recent Highlights

Publications

1/24/2026

Machine Learning Driven ‘Therapy Calculator’ for Self-Managed Digital Speech-Language Therapy for Individuals with Post-stroke Aphasia

Liu, H., Betke, M., Ishwar, P., & Kiran, S. (2026). Machine Learning Driven ‘Therapy Calculator’ […]

8/15/2025

Aphasia severity prediction using a multi-modal machine learning approach

Hu, X., Varkanitsa, M., Kropp, E., Betke, M., Ishwar, P., & Kiran, S. (2025). Aphasia […]

7/1/2025

Using Unsupervised Dimensionality Reduction to Identify Lesion Patterns Predictive of Post-Stroke Aphasia Severity

Kropp, E., Varkanitsa, M., Carvalho, N., Falconer, I., Billot, A., Al-Dabbagh, M., & Kiran, S. […]

5/11/2025

How the Stroop Effect Arises from Optimal Response Times in Laterally Connected Self-Organizing Maps

Prabhakaran, D., Grasemann, U., Kiran, S., & Miikkulainen, R. (2025). How the Stroop Effect Arises […]

3/31/2025

An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications

Cordella, C., Marte, M. J., Liu, H., & Kiran, S. (2024). An Introduction to Machine […]

1/28/2025

Noisy-channel language comprehension in aphasia: A Bayesian mixture modeling approach

Ryskin, R., Gibson, E., & Kiran, S. (2025). Noisy-channel language comprehension in aphasia: A Bayesian […]

1/2/2025

Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence

Marte, M. J., Carpenter, E., Scimeca, M., Russell-Meill, M., Penaloza, C., Grasemann, U., Miikkulainen, R., […]

12/31/2024

Machine learning predictions of recovery in bilingual post-stroke aphasia: Aligning insights with clinical evidence

Authors: Manuel Marte, Erin Carpenter, Michael Scimeca, Marissa Russell-Meill, Claudia Peñaloza, Uli Grasemann, Risto Miikkulainen, […]

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Past Work

Accomplishments

10 Key Takeaways from Boston Tech Week: How AI Is Transforming the Future of Brain Health

June 23rd, 2026

Transforming Brain Health and Disease through AI #BosTechWeek May 29, 2026 AI is rapidly reshaping how we understand, diagnose, and treat the brain at a moment when aging populations are placing unprecedented strain on healthcare systems. To address this shift, Boston University and the BU Center for Brain Recovery hosted an expert panel discussion that […]

Data Science and AI Approaches

Brain Recovery After Stroke: How AI Could Be Used To Predict the Best Treatment Path

June 9th, 2026 Read more

Announcements

ReMIND: An AI Assistant for Neuroimaging Analysis

May 28th, 2026

ReMIND: An AI Assistant for Neuroimaging Analysis Researchers in the Kolachalama Laboratory at Boston University have recently developed ReMIND, a tool powered by artificial intelligence (AI) to aid clinicians with the growing demand of interpreting and reporting on large quantities of multi-sequence brain scans. With the increasing incidence of neurological disorders, clinicians are currently facing […]

Accomplishments

MedAI Hackathon 2026

April 21st, 2026

April 10–11, 2026 The inaugural MedAI Hackathon successfully brought together over 230 registrants across the Boston University community, bridging the Charles River Campus and the Medical Campus. The event marked an exciting achievement for convergent research at BU, as participants collaborated in cross-disciplinary teams to develop AI powered solutions for real-world challenges in healthcare and […]

Accomplishments

AIAS Best Paper: Gaining Insight into Brain Damage and Rehabilitation through Digital Twins

November 20th, 2025

October 27th – 28th, 2025 Center for Brain Recovery researchers Swathi Kiran (CBR Founding Director) and Risto Miikkulainen (CBR Affiliate) were invited to present their work in the Best Paper Presentation session at the 2025 Chen Institute Symposium for AI Accelerated Science (AIAS). Out of more than 60 submissions and 29 accepted papers, Drs. Kiran […]

Accomplishments

Advances in AI: Diagnosis & Treatment of Neurological and Neurodegenerative Disorders Symposium

October 24th, 2025

October 24th, 2025 On Friday, October 24th, leading researchers from the Boston University Center for Brain Recovery (CBR) traveled to the San Francisco Bay Area to present their ground-breaking work leveraging artificial intelligence and machine learning tools for detection, treatment, and prediction of neurological and neurodegenerative disorders. Hosted in collaboration with the Tianqiao and Chrissy […]

Announcements

Forecasting Recovery: CBR Faculty Awarded $3.2M NIH Grant

September 25th, 2025

Center for Brain Recovery (CBR) faculty members Drs. Archana Venkataraman and Swathi Kiran have been awarded a $3.2M NIH grant from the National Institute on Deafness and Other Communication Disorders to develop computational tools that will predict language recovery in people with post-stroke aphasia. Venkataraman, an expert in machine learning, and Kiran, a clinician-scientist with […]

Data Science and AI Approaches

An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications

March 31st, 2025

Cordella, C., Marte, M. J., Liu, H., & Kiran, S. (2024). An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications. Perspectives of the ASHA Special Interest Groups. https://doi.org/10.1044/2024_PERSP-24-00037 Purpose The purpose of this article is to orient clinicians and researchers to machine learning (ML) approaches, as applied to the field of […]

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