Researcher · Division of Health AI
Todd Levy, MS
Todd Levy is a Senior Biomedical Engineer at the Division of Health AI, Feinstein Institutes for Medical Research, with an MS in Electrical Engineering from Case Western Reserve University and a post-masters certificate in Applied Biomedical Engineering from Johns Hopkins University. He specializes in neural decoding and data analytics, applying signal processing and machine learning to advance bioelectronic medicine research. As a key contributor to the Division of Health AI and the Neural and Data Science laboratory, Levy has co-authored 17 publications including papers in Nature Communications and JAMA, with research focus on COVID-19 prognostic models, vagus nerve signal recording and analysis, and development of self-monitoring auto-updating clinical models.
8 projects17 papers
- Role
- Senior Biomedical Engineer
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Research projects
Research projects
Nursing workforce optimization
Machine learning models using DeepAR probabilistic forecasting predict nursing workforce demand across Northwell's hospital units up to 12 months ahead, supporting preemptive hiring and staffing decisions across diverse specialties.
Operational AI
Vagus nerve digital twin (REVA)
A comprehensive anatomical dataset of 60 human vagus nerves (30 left, 30 right), spanning millions of micro-CT images across hundreds of terabytes of data. Using 3D nnU-Net segmentation, this project builds a detailed vagus digital twin to guide the design of selective vagus nerve stimulation therapies, part of a $6.7 million NIH SPARC award in collaboration with the TNP Lab.
Anatomical Data AI
Vagus decoding: inflammation (preclinical)
This research decodes how vagus nerve neurons sense inflammatory cytokines in real time, enabling development of diagnostic bioelectronic devices and closed-loop inflammation treatments. Building on prior work showing vagal decoding of immune signals, recent studies reveal that individual vagal sensory neurons selectively respond to specific cytokines, with altered responses during active inflammation.
Preclinical AI
Vagus decoding: metabolic states (preclinical)
Preclinical research using decoding algorithms to identify neural signals from the vagus nerve that respond specifically to hypoglycemia. A decoder achieved high accuracy in reconstructing blood glucose levels from vagus nerve recordings, with median error of 18.6 mg/dL, and revealed that TRPV1 nociceptor neurons are critical for sensing low glucose states.
Preclinical AI
Chronic vagus nerve recordings (mouse)
Chronic wireless recording of compound action potentials from the mouse vagus nerve up to 6 months, enabling longitudinal tracking of neural activity in disease models (CIA, CAIA) to predict inflammation severity and evaluate neuromodulation efficacy.
Preclinical AI
Publications
Selected papers
International Journal of Neural Systems
Longitudinal characterization of compound action potentials in chronic vagus nerve recordings in mice
(opens in new tab)Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
An Artificial Intelligence-Based Probabilistic Forecasting Model for Predicting Nursing Workforce Demand in Hospital Units
(opens in new tab)Nature Communications
Control of spatiotemporal activation of organ-specific fibers in the swine vagus nerve by intermittent interferential current stimulation
(opens in new tab)Journal of Surgical Oncology
Machine learning to predict completion of treatment for pancreatic cancer
(opens in new tab)International Journal of Medical Informatics
Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York
(opens in new tab)Brain Stimulation
Organ- and function-specific anatomical organization of vagal fibers supports fascicular vagus nerve stimulation
(opens in new tab)Nature Communications
Development and Validation of Self-Monitoring Auto-Updating Prognostic Models of Survival for Hospitalized COVID-19 Patients
(opens in new tab)Bioelectronic Medicine
A method to quantify autonomic nervous system function in healthy, able-bodied individuals
(opens in new tab)International IEEE/EMBS Conference on Neural Engineering (NER)
Noninvasive, multimodal assessment of physiological responses to transcutaneous auricular vagus nerve stimulation
(opens in new tab)Bioelectronic Medicine
Machine learning to assist clinical decision-making during the COVID-19 pandemic
(opens in new tab)Journal of Neuroscience Methods
An impedance matching algorithm for common-mode interference removal in vagus nerve recordings
(opens in new tab)Bioelectronic Medicine
Identification of hypoglycemia-specific neural signals by decoding murine vagus nerve activity
(opens in new tab)Proceedings of the National Academy of Sciences
Decoding cytokine specific neural activity from the cervical vagus nerve
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