Our mission is to create methods to fit the science and not make science fit the methods.

Specifically, we are interested in the following questions that have clinical relevance:

  1. Neurodegeneration – How can we develop software frameworks that can assist dementia screening in various real-world settings?
  2. Digital pathology – How can we build clinical-grade software tools to complement the pathologist workflow?

We are also interested in the following frameworks that have computational relevance:

  1. Domain generalization – Development of neural networks that can generalize well across multiple data cohorts
  2. Representation learning – Construction of efficient neural models on high resolution data to process local and contextual information

Joining our laboratory

We form small teams comprising individuals with complementary expertise and work persistently to build comprehensive solutions. If you are interested in joining us, then we encourage you to contact an active lab member and talk about your interests.


We are grateful for funding from the American Heart Association, the National Institute on Aging, the National Heart, Lung, and Blood Institute, the National Cancer Institute, the Artificial Intelligence and Technology Collaboratories (AITC) for Aging Research program, and the National Institute of Diabetes and Digestive and Kidney Diseases. We also thank the Karen Toffler Charitable Trust, Johnson & Johnson Enterprise Innovation Inc., and Visterra Inc., for funding our work.


– Machine learning (MS650)

– Guest lectures (BF831; HM817; FC713)


October 2023

New paper! Fusion of low-level voice descriptors for dementia, published in the Journal of Alzheimer's Disease.

October 2023

New paper! Large language models in neurology, published in Neurology.

September 2023

New grant! We received an Ignition Award from Boston University to create an AI-based tool for dementia assessment.

September 2023

New paper! Deep learning to predict progression risk on cognitively impaired individuals, published in iScience.

May 2023

New paper! Machine learning in clinical trials: Applications to neurology, published in Neurotherapeutics.

April 2023

Tejus Surendran transitions from a staff scientist in our lab to a PhD student at Carnegie Mellon University.

March 2023

Matthew Miller got matched to MGH Radiology.

March 2023

Akshara Balachandra got matched to Stanford Neurology.

March 2023

Joyce Lee got matched to BMC Neurology.

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