Applied AI & Robotics Research Scientist
I take AI systems from the lab to the real world — building autonomous robots that triage and treat critically injured patients in mass casualty events, validated not just in simulation but on real hardware with real clinical constraints.
My research centers on a single question: what if one responder could save many? In mass casualty events, the gap between patients who need immediate care and the responders available to provide it costs lives. I build the systems that close that gap.
At Carnegie Mellon's Robotics Institute, advised by Artur Dubrawski, I developed autonomous robots that can assess patient vitals, locate hemorrhage sites, and perform procedural interventions — without waiting for a human to be free. Critically, each system was taken beyond simulation into real-world deployment: validated on physical robots, tested in animal surgical models, and evaluated under the constraints of actual clinical environments.
My philosophy: AI should extend human expertise, not replace it. I focus on bridging the gap between laboratory breakthroughs and field-ready systems — designing around uncertainty quantification, equity across patient populations, and robust performance on real hardware under real conditions.
My dissertation organizes around two autonomous systems, each addressing a critical phase of mass casualty response.
A robotic system that navigates to patients, estimates vital signs through non-contact sensing, and makes triage decisions using a Bayesian network with embedded clinical knowledge — all in real time, across multiple simultaneous casualties.
An ultrasound-guided robotic system for femoral vessel cannulation — locating bleeding vessels, tracking tissue deformation, and guiding needle placement to control life-threatening hemorrhage without a trained clinician present.
A parameter-free algorithm for estimating heart rate from video in challenging outdoor environments. Introduces spectral concentration and total variation metrics for adaptive signal optimization. Outperforms 12 state-of-the-art methods across 4 datasets, including the novel NATURE outdoor benchmark I collected and released.
Augmentation pipeline for ultrasound imagery that improves downstream vessel detection and needle guidance in low-quality, real-world scans. Designed for data-scarce surgical robotics settings with limited labeled training examples.
A segmentation network with first-class uncertainty quantification, enabling the triage system to communicate confidence alongside predictions. Critical for deployable AI in high-stakes medical decisions with limited training data.
Automated detection of femoral vessel bifurcation landmarks in ultrasound imagery — a critical navigation prerequisite for autonomous cannulation. Validated on in vivo animal data under real surgical conditions.
A Bayesian network integrating real-time sensor streams from ROS2 nodes with embedded clinical triage protocols. Enables data-efficient decision-making by grounding probabilistic inference in structured medical domain knowledge.
Real-time needle tracking under tissue deformation, plus a 3D ultrasound visualization tool for intraoperative spatial awareness. Built for deployment on actual robotic hardware with hard latency constraints.
Selected publications from my doctoral research on autonomous medical robotics.
I completed my PhD at Carnegie Mellon University and am actively exploring research positions in industry — particularly at organizations working on healthcare AI, medical robotics, embodied AI, or safety-critical autonomous systems.
If you're working on hard problems that need to move from the lab to the real world — where the stakes are real and the systems must actually work — I'd love to talk.