Biomedical Imaging and Data Science Lab

About Us

The Biomedical Imaging and Data Science Lab (BIDSLab) directed by Prof. Joyita Dutta, is part of the Department of Biomedical Engineering at the University of Massachusetts Amherst. BIDSLab is an engineering and data science lab and maintain deep ties with hospital-based researchers and physicians in the Boston area and beyond. Our broad research goal is to develop signal processing and artificial intelligence (AI) based solutions to a range of inverse problems, including biomedical image processing and reconstruction, brain network analysis, and electrophysiological signal processing for healthcare applications. Several of our ongoing research projects are focused on the development of AI-based approaches to aid the diagnosis and prognosis of Alzheimer's disease.

Recent Updates

NIH R03 Grant

Aug 2023. We received a multi-PI NIH/NIA R03 grant on early Alzheimer's disease forecasting in collaboration with Dr. Madalina Fiterau (UMass CICS).


Jun 2023. BIDSLab received a pilot grant from the UMass Center for Clinical and Translational Science (UMCCTS). The grant will support our work on thalamic imaging in collaboration with UMass Chan Medical School.

PLOS One Paper

May 2023. Our paper titled "AI-driven sleep staging from actigraphy and heart rate" has been accepted for publication in PLOS One.

MassAITC Pilot Award

Apr 2023. BIDSLab received a Pilot Award from MassAITC titled "An academic-industrial partnership for AI-based sleep staging in the elderly using the Dreem headband and a smartwatch."

SNMMI 2023

Apr 2023. We are excited to announce that Vibha and Tzu-An's abstracts got accepted for the PIDSC Young Investigator Award competition and Fan's abstract got accepted as an oral in the Data Science track at the SNMMI 2023 Annual Meeting. Congrats to everyone!!!

Medical and Science Student Research Grant

Mar 2023. Congrats Vibha for receiving the 2023 Medical and Science Student Research Grant from the Society of Nuclear Medicine and Molecular Imaging (SNMMI)!

JNM Paper

Mar 2023. Our perspective article on interpretable AI titled "Artificial intelligence algorithms need to be explainable – or do they?" has been accepted for publication in the Journal of Nuclear Medicine.