Laboratory for Deep Neurophenotyping
100 Cambridge Street
Boston, MA 02114
Explore This Lab
We are a group of neurologists and computer scientists who share the passion for using technology to precisely understand how certain neurologic diseases impact human motor and cognitive function. We believe that capturing high-definition behavior at scale will lead to the discovery of fundamental characteristics of disease that will inform new approaches to therapy development, diagnosis, and management. Our initial focus is on cerebellar ataxia, Parkinson’s disease, and neurodegenerative disease in general.
We are deeply thankful to the patients and patient advocates who donate their time to our research program during their visit to Massachusetts General Hospital. Likewise, this work would not be possible without the generous support from the Ataxia-Telangiectasia Children’s Project (A-TCP), Biogen Inc., the Broad Institute, NINDS, the Massachusetts Life Sciences Center, the Orphan Disease Center of UPenn and the Mass General Department of Neurology.
We are currently seeking a Postdoctoral Fellow in Machine Learning. Learn more and apply at the main Mass General Brigham job site.
We are always looking for individuals with a strong quantitative and algorithmic background as well as a passion for understanding the brain and neurologic disease to lead areas of interest within our group. Contact email@example.com for more information.
Anoopum Gupta, MD, PhD
Dr. Gupta received his undergraduate degree in Electrical Engineering from Georgia Tech, his medical degree from the University of Pittsburgh School of Medicine, and his PhD in Robotics and the Center for the Neural Basis of Cognition from Carnegie Mellon University’s School of Computer Science. He completed his residency in neurology at the Mass General Brigham/Harvard Medical School Residency Program. Dr. Gupta’s previous research utilized methods in machine learning and signal processing to understand how large populations of neurons in the hippocampus support learning, memory, and decision making.
Mainak Jas, PhD
Mainak completed his BTech in Instrumentation Engineering from Indian Institute of Technology Kharagpur in India, his Masters degree in machine learning and data mining from Aalto University in Finland and PhD in signal and image processing from Telecom ParisTech in France. Mainak's past research has focused on developing novel automated methods using signal processing and machine learning for interpreting and analyzing neuroimaging data. He also maintains and contributes to several open source software packages such as MNE-Python, MNE-BIDS, and HNN-core.
Adonay Nunes, PhD
Adonay Nunes received his doctoral degree in Biomedical Physiology from Simon Fraser University, Vancouver. His research focused on understanding the underlying oscillatory activity and synchrony of neural ensembles that characterize individuals with Autism and in children born very preterm. During his degree, he used data analysis methods involving parametric statistics and machine learning to model the population and predict their symptom severity. Currently, he is interested in extracting neurodegenerative phenotypes for machine learning classification and prediction by integrating video, wearable, and physiological data recorded during cognitive and motor assessments.
Siddharth Patel, PhD
Siddharth received his PhD in Biophysics from Indian Institute of Science, Bangalore where his research focused on understanding the sequence-structure-function relationships in proteins. He translated this experience, analyzing Antibody structures at Pfizer, where he applied machine learning techniques to predict antibody properties. As a Data Scientist at MindMics, a health tech startup, he designed and implemented algorithms on wearable sensor time series data to monitor cardiovascular health. He is interested in characterizing movement impairment and quantifying progression of neurodegeneration by applying and interpreting deep learning models on wearable sensor data.
Kyriakos Vattis, PhD
Kyriakos received his doctoral degree in Physics from Brown University. He worked in the field of Theoretical Astrophysics and Cosmology, focusing on a variety of problems including dark matter and primordial black holes. He used data analysis methods such as Markov Chain Monte Carlo (MCMC) and machine learning to model dark matter properties and how they affect the cosmological evolution. Currently, he is interested in analyzing audio, video, wearable, and physiological data recorded during cognitive and motor assessments, using machine learning classification and prediction, to study neurodegenerative phenotypes and their evolution.
Clinical Research Assistant
Nicole graduated from Syracuse University with a Bachelor of Arts degree in Psychology and Neuroscience in 2019. She hopes to attend graduate school and pursue a career researching neurodegenerative diseases, focusing on preventatives and treatments.
Clinical Research Assistant
Anna graduated with a Bachelor of Science degree in Neuroscience from Trinity College in 2020. She hopes to pursue a career in neuropsychology, and is especially interested in health equity, health literacy and working with pediatric populations.
Clinical Research Assistant
Faye graduated from the University of Illinois at Urbana Champaign with a Bachelor of Science degree in Interdisciplinary Health Sciences in Spring 2022. She hopes to attend medical school with a focus on conducting research and promoting cultural competancy in healthcare.
- Steven Arnold, MD, Massachusetts General Hospital and Harvard Medical School
- James Berry, MD, Massachusetts General Hospital and Harvard Medical School
- Katey Burke, PT, DPT, NCS, Massachusetts General Hospital Institute of Health Professions
- Zhuoqing (George) Chang, PhD, Electrical and Computer Engineering, Duke University
- Jean-Francois Daneault, PhD, Department of Kinesiology and Health, Rutgers University
- Krzysztof Gajos, PhD, Harvard Paulson School of Engineering and Applied Sciences, Harvard University
- Amanda Guidon, MD, Massachusetts General Hospital and Harvard Medical School
- Albert Hung, MD, PhD, Neurology, Massachusetts General Hospital and Harvard Medical School
- Maia Jacobs, PhD, Harvard Paulson School of Engineering and Applied Sciences, Harvard University
- Sheraz Khan, PhD, Massachusetts General Hospital and Harvard Medical School
- Sunghoon Ivan Lee, PhD, College of Information and Computer Sciences, University of Massachusetts Amherst
- Vineet Pandey, PhD, Harvard Paulson School of Engineering and Applied Sciences, Harvard University
- Guillermo Sapiro, PhD, Electrical and Computer Engineering, Duke University
- Jeremy Schmahmann, MD, Neurology, Massachusetts General Hospital and Harvard Medical School
- Christopher Stephen, MD, Massachusetts General Hospital and Harvard Medical School
- Winnie Ching, Clinical Research Assistant, subsequently a medical student at University of Pikeville-Kentucky College of Osteopathic Medicine
- Mary Donovan, Clinical Research Coordinator, subsequently a medical student at Georgetown University
- Nergis Khan, Clinical Research Coordinator, subsequently a medical student at Stanford University
- Karin Knudson, Post-Doctoral Fellow, subsequently a Senior Data Scientist at Tufts University
- Jessey Ouillin, Clinical Trial Associate, OM Pharma, Meyrin, Switzerland
- Akansha Pandey, Medical Student, Brown University
Recent publications include:
Khan NC, Pandey V, Gajos KZ, Gupta AS (2021) Free-Living Motor Activity Monitoring in Ataxia-Telangiectasia. Cerebellum. https://doi.org/10.1007/s12311-021-01306-y
Oubre B., Daneault JF., Whritenour K., Khan NC, Stephen CD, Schmahmann JD, Lee SI, Gupta AS (2021). Decomposition of Reaching Movements Enables Detection and Measurement of Ataxia. Cerebellum. https://doi.org/10.1007/s12311-021-01247-6
Chang Z, Chen Z, Stephen CD, Schmahmann JD, Wu H-T, Sapiro G, Gupta AS (2020): Accurate Detection of Cerebellar Smooth Pursuit Eye Movement Abnormalities via Mobile Phone Video and Machine Learning. Scientific Reports 10, 18641 (2020). doi.org/10.1038/s41598-020-75661-x
Azami H, Arnold SE, Sanei S, Chang Z, Sapiro G, Escudero J, Gupta AS (2019): Multiscale Fluctuation-based Dispersion Entropy and its Applications to Neurological Diseases. IEEE Access. 2019 7:68718-68733. doi:10.1109/ACCESS.2019.2918560.
Jacobs M, Gheihman G, Gajos KZ, Gupta AS (2019): “I think we know more than our doctors:” How online health communities help caregivers manage care teams with limited disease-related expertise. Proceedings of the ACM on Human-Computer Interaction 3, CSCW, Article 159 (November 2019), 22 pages. Summary published at Medium.
Gajos KZ, Reinecke K, Donovan M, Stephen CD, Hung AY, Schmahmann JD, Gupta AS (2019): Computer mouse use captures ataxia and parkinsonism, enabling accurate measurement and detection. Movement Disorders. doi:10.1002/mds.27915