About

Todd P. Coleman is an Associate Professor of Bioengineering and, by courtesy, of Electrical Engineering at Stanford University. He is a Wu Tsai Neurosciences Institute Faculty Scholar. Dr. Coleman received B.S. degrees in electrical engineering and computer engineering, both summa cum laude, from the University of Michigan. He earned his M.S. and Ph.D. degrees in electrical engineering and computer science from MIT. His postdoctoral studies were conducted at MIT and Mass General Hospital, focusing on quantitative neuroscience. His research is highly multidisciplinary, utilizing tools from applied probability, physiology, and bioelectronics to develop technologies and algorithms for monitoring and modulating the physiology of the nervous system, including the brain and visceral organs. Dr. Coleman has served as a Principal Investigator on grants from NSF, NIH, the Department of Defense, and private foundations. He is an inventor on 10 granted US patents and has been recognized as a Gilbreth Lecturer for the National Academy of Engineering, a TEDMED speaker, and a Fellow of IEEE and the American Institute for Medical and Biological Engineering. Currently, he is a deputy director of the Wu Tsai Neurosciences Institute at Stanford University.

Research topics

• Computer Science
• Medicine
• Optometry
• Engineering
• Ophthalmology
• Psychology
• Neuroscience
• Nanotechnology
• Biomedical engineering
• Chemistry
• Materials science
• Physics
• Computer hardware
• Telecommunications

Selected publications

• parkersruth/bayesian_pulse_deconvolution: v1.0.0-preprint

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-10

  otherOpen access

  Preprint version release

  PublisherDOI

• Vascular waveform analysis using Bayesian pulse deconvolution

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-02-11

  articleOpen access

  Vascular waveforms, which measure bulk flow in blood vessels, are widely used to measure vital signs, diagnose conditions, and predict long-term health outcomes. Analyzing vascular waveforms depends on three fundamentally interdependent tasks: signal filtering, pulse timing detection, and pulse shape extraction. We hypothesized that Bayesian pulse deconvolution can achieve improved performance on all three tasks by solving them jointly. This method uses an analytical, generative model of vascular waveforms with priors informed by physical and biological domain knowledge. In simulations, Bayesian pulse deconvolution achieves better performance on all tasks compared with existing algorithms: 90% reduction of median filtering error, 60% reduction in pulse timing error, and 85% reduction in shape extraction error. The advantages in simulations extend to human recordings of photoplethysmography waveforms. Taking real time-synchronized electrocardiogram R-R intervals as a proxy ground truth, Bayesian pulse deconvolution achieves 40% lower pulse interval estimation error (RMSE =5.1 ms) compared with typical algorithms (RMSE = 8.3 ms, p=1e-10). By extracting more accurate and informative insights from vascular waveforms, Bayesian pulse deconvolution could advance a wide array of health technologies that rely on interpreting signals from blood vessels.

  PublisherDOI

• parkersruth/bayesian_pulse_deconvolution: v1.0.0-preprint

  Open MIND · 2026-02-10

  other

  Preprint version release

  DOI

• A Convex Point Process Model of Heartbeat Dynamics for Inference, Prediction, and Information Quantification

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-03

  preprintOpen accessSenior author

  The dynamics of heartbeat intervals provide important insights into cardiovascular and autonomic nervous system function. Conventional analytical approaches often use fixed-window averaging, which can obscure rapid changes and reduce temporal resolution. Point process models address this limitation by operating in continuous time, enabling more precise characterization of heartbeat variability. A landmark example is the history-dependent inverse Gaussian (IG) point process model of Barbieri et al. (2005), which captures temporal dependencies in heartbeat timing. However, the nonconvex likelihood of the IG model complicates parameter estimation, requiring careful initialization and adding computational burden. In this work, we introduce a convex alternative: a history-dependent gamma generalized linear model (GLM) for heartbeat dynamics. Applied to a tilt-table dataset, our approach yields accurate and robust heart rate estimation. We further extend the model to two more applications: (1) sequential prediction of interbeat intervals, outperforming common machine learning algorithms, and (2) computation of information-theoretic measures demonstrating its utility in quantifying the influence of cardiac medications on heartbeat dynamics.

  PublisherOA PDFDOI

• Simultaneous stomach-brain electrophysiology reveals dynamic coupling in human sleep

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-14 · 1 citations

  preprintSenior author

  Sleep involves continuous communication between the brain and body, yet the dynamics of peripheral signals during human sleep remain poorly understood. Here we tested whether gastric electrophysiology exhibits infraslow structure and coordinated fluctuations with cortical rhythms indicative of sleep physiology. Simultaneous high-density electroencephalography (EEG) and electrogastrography (EGG) were recorded across sixty participants and three nights. Gastric power was consistently higher during NREM than REM sleep and declined across successive cycles, consistent with stage-dependent autonomic modulation of visceral activity. For the first time, we show that the gastric rhythm itself exhibits intrinsic infraslow amplitude modulations (∼0.007 Hz), which are selectively amplified during NREM sleep and temporally aligned with infraslow fluctuations in cortical sigma power, strongest during N3 sleep. Event-locked analyses further revealed transient increases in gastric amplitude following cortical slow wave oscillations, particularly when accompanied by sleep spindles. Across nights, variance in gastric infraslow amplitude predicted subjective sleep quality beyond standard polysomnographic and cardiac measures. Together, these findings position the human stomach as a peripheral oscillator whose infraslow dynamics track thalamocortical activity during sleep and predict subjective sleep quality, extending the interoceptive regulatory loop into the sleeping brain.

  PublisherDOI

• Graphical Models and Efficient Inference Methods for Multivariate Phase Probability Distributions.

  PubMed · 2025-05-30

  preprintOpen accessSenior author

  Multivariate phase relationships are important to characterize and understand numerous physical, biological, and chemical systems, from electromagnetic waves to neural oscillations. These systems exhibit complex spatiotemporal dynamics and intricate interdependencies among their constituent elements. While classical models of multivariate phase relationships, such as the wave equation and Kuramoto model, give theoretical models to describe phenomena, the development of statistical tools for hypothesis testing and inference for multivariate phase relationships in complex systems remains limited. This paper introduces a novel probabilistic modeling framework to characterize multivariate phase relationships, with wave-like phenomena serving as a key example. This approach describes spatial patterns and interactions between oscillators through a pairwise exponential family distribution. Building upon the literature of graphical model inference, including methods like Ising models, graphical lasso, and interaction screening, this work bridges the gap between classical wave dynamics and modern statistical approaches. Efficient inference methods are introduced, leveraging the Chow-Liu algorithm for directed tree approximations and interaction screening for general graphical models. Simulated experiments demonstrate the utility of these methods for uncovering wave properties and sparse interaction structures, highlighting their applicability to diverse scientific domains. This framework establishes a new paradigm for statistical modeling of multivariate phase relationships, providing a powerful toolset for exploring the complexity of these systems.

  PublisherOA PDF

• Color-neutral and reversible tissue transparency enables longitudinal deep-tissue imaging in live mice

  Proceedings of the National Academy of Sciences · 2025-08-26 · 4 citations

  articleOpen access

  Light scattering in biological tissue presents a significant challenge for deep in vivo imaging. Our previous work demonstrated the ability to achieve optical transparency in live mice using intensely absorbing dye molecules, which created transparency in the red spectrum while blocking shorter-wavelength photons. In this paper, we extend this capability to achieve optical transparency across the entire visible spectrum by employing molecules with strong absorption in the ultraviolet spectrum and sharp absorption edges that rapidly decline upon entering the visible spectrum. This color-neutral and reversible tissue transparency method enables optical transparency for imaging commonly used fluorophores in the green and yellow spectra. Notably, this approach facilitates tissue transparency for structural and functional imaging of the live mouse brain labeled with yellow fluorescent protein and GCaMP through the scalp and skull. We show that this method enables longitudinal imaging of the same brain regions in awake mice over multiple days during development. Histological analyses of the skin and systemic toxicology studies indicate minimal acute or chronic damage to the skin or body using this approach. This color-neutral and reversible tissue transparency technique opens opportunities for noninvasive deep-tissue optical imaging, enabling long-term visualization of cellular structures and dynamic activity with high spatiotemporal resolution and chronic tracking capabilities.

  PublisherDOI

• Functionalized Adhesive Thin Flexible Electrode Arrays for Large-Scale Unobtrusive Ambulatory Monitoring of Neuromuscular Activity

  2025-07-14

  articleSenior author

  Wearable electrical sensors offer noninvasive, high-fidelity monitoring of organ-level neuromuscular activity. In gastrointestinal applications, electrogastrography (EGG) enables detection of slow-wave (0.05 Hz) gastric myoelectric activity from the skin surface. However, commonly used current electrode systems with individually placed 3M Red Dot electrodes are bulky, prone to electrode placement variability, and unsuitable for long-term or unsupervised clinical use. Here, we present a comparative evaluation of scalable fabrication strategies for a conformable, adhesive-integrated electrode array designed specifically for continuous, high-resolution EGG (HR-EGG). The array is constructed on thin, flexible polyimide substrates with rounded perforations to improve breathability and is paired with a gentle, silicone-based medical adhesive suitable for sensitive skin. This design enables consistent inter-electrode spacing, reduces user burden, and offers significant conformability improvements over rigid commercial multi-electrode systems. It also offers scalability advantages over soft stretchable arrays requiring cleanroom fabrication. Multiple electrode interface strategies-including Ag/AgCl, dry PEDOT:PSS, and conductive hydrogel coatings-are implemented and characterized using electrical impedance spectroscopy. The final patch design is validated through a representative pre- and post-meal recording, showing reliable capture of gastric slow-wave activity. This work supports scalable deployment of HR-EGG in clinical and research settings, expanding access to noninvasive gastrointestinal diagnostics.

  PublisherDOI

• Machine Learning Methods to Track Dynamic Facial Function in Facial Palsy

  IEEE Transactions on Biomedical Engineering · 2025-05-07 · 4 citations

  articleSenior author

  OBJECTIVE: For patients with facial palsy, the wait for return of facial function and resulting vision risk from poor eye closure, difficulty speaking and eating from flaccid oral sphincter muscles, and psychological morbidity from the inability to smile or express emotions can be devastating. There are limited methods to assess ongoing facial nerve regeneration: clinicians rely on subjective descriptions, imprecise scales, and static photographs to evaluate facial functional recovery. We propose a more precise evaluation of dynamic facial function through video-based machine learning analysis to facilitate a better understanding of the sometimes subtle onset of facial nerve recovery and improve guidance for facial reanimation surgery. METHODS: We present machine learning methods employing likelihood ratio tests, optimal transport theory, and Mahalanobis distances to: 1) assess the use of defined facial landmarks for binary classification of different facial palsy types; 2) identify regions of asymmetry and potential palsy during specific facial cues; and 3) quantify palsy severity and map it directly to widely used clinical scores, offering clinicians an objective way to assess facial nerve function. RESULTS: Our results demonstrate that video analysis provides a significantly more accurate and detailed assessment of facial movements than previously reported. CONCLUSIONS: Our work allows for precise classification of facial palsy types, identification of asymmetric regions, and assessment of palsy severity. SIGNIFICANCE: This project enables clinicians to have more accurate and timely information to make decisions for facial reanimation surgery, which will have drastic consequences on the quality of life for affected patients.

  PublisherDOI

• Recipe for Success: Pilot Evaluation of Hands-on Youth Cooking Class Program

  Journal of the Academy of Nutrition and Dietetics · 2025-09-23

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

Recent grants

• Behavioral, Physiological, and Quantitative Models of Pro-Social Behavior

  NIH · $1.4M · 2016–2020

• Inverse Methods for Spatiotemporal Characterization of Gastric Electrical Activity and its Association with Upper GI Symptoms from Cutaneous Multi-electrode Recordings

  NIH · $158k · 2021–2023

• Collaborative Effort: Message-Passing Algorithms: From Practice to Theory and Back to Practice

  NSF · $207k · 2005–2008

Frequent coauthors

• Negar Kiyavash

  32 shared

• Sanggyun Kim

  Hanyang University

  29 shared

• Muriel Médard

  Massachusetts Institute of Technology

  26 shared

• David C. Kunkel

  25 shared

• Michelle Effros

  California Institute of Technology

  22 shared

• Diego Mesa

  Università Cattolica del Sacro Cuore

  19 shared

• Christopher J. Quinn

  Iowa State University

  17 shared

• Vincent M. Wu

  University of California, San Diego

  13 shared

Labs

• Todd Coleman's LabPI

  1-2 sentence research focus

Education

• Ph.D., Bioengineering

  Stanford University

  2000

• M.S., Bioengineering

  Stanford University

  1996

• B.S., Bioengineering

  University of California, San Diego

  1994

Awards & honors

• Gilbreth Lecturer for the National Academy of Engineering
• Fellow of IEEE
• Fellow of the American Institute for Medical and Biological…
• TEDMED speaker