Research

My research develops human-centered methods for designing and managing complex sociotechnical systems in which people and intelligent technologies must work together effectively. I focus on how human states, preferences, and behavior can be represented within digital twins and AI-enabled decision systems, and how these representations can support adaptive human-AI teaming, workflow design, and operational decision-making. My work combines systems engineering, artificial intelligence, human factors, and modeling and simulation, with current applications in healthcare and broader extensions to manufacturing, transportation, and defense.

RESEARCH THEMES

Human-in-the-loop Digital Twins

I study how digital twins can represent not only technical processes and system states, but also human workload, behavior, preferences, and well-being. My goal is to develop digital twins that can support real-time monitoring, prediction, and decision-making in complex sociotechnical systems.

Human-AI Teaming

I study how people and intelligent systems can work together effectively in complex sociotechnical systems. My research examines how workload, trust, preferences, and situational awareness can inform the design of adaptive AI systems that support appropriate sharing of information, decisions, and control.

AI & Human Factors in Healthcare

I use electronic health records data, machine learning, simulation, and human-factors methods to understand clinician workload, burnout, and interactions with digital technologies. A central goal of this work is to develop decision-support systems that improve healthcare operations without creating additional burden for clinicians.

Sociotechnical Systems & Digital Engineering

I examine how engineering technologies and organizational systems interact during digital transformation. This work considers technical, human, organizational, and policy barriers to the adoption of model-based systems engineering, digital engineering, and other emerging technologies.

CURRENT RESEARCH

Digital Twins for Clinician Workload and Burnout

I am developing a human-in-the-loop digital twin framework for understanding and managing clinician workload and burnout. The work integrates healthcare operations data, clinician-reported measures, machine learning, simulation, and systems modeling to characterize how workload emerges over time and how system-level interventions might improve clinician well-being.

EHR-Based Prediction of Clinician Workload

This work investigates whether routinely collected electronic health records activity can be used to predict clinician workload. I am particularly interested in the trade-offs between population-level and personalized models and in using explainable machine learning to identify the activities most strongly associated with workload.

Modeling Healthcare Work as a Sociotechnical System

I use model-based systems engineering and simulation to represent interactions among clinicians, patients, workflows, information systems, and organizational processes. These models are intended to support experimentation with system changes before they are implemented in real clinical environments.

Digital Engineering Transformation

I study digital engineering transformation as a sociotechnical challenge rather than a purely technological one, with particular attention to organizational barriers, policy goals, workforce considerations, and the implementation of model-based systems engineering.

RESEARCH APPROACH

Systems Engineering

MBSE · SysML · System Architecture · System Dynamics

Artificial Intelligence

Machine Learning · Explainable AI · Predictive Modeling · Personalized AI

Human Factors

Workload · Burnout · Human-AI Interaction · Trust & Preferences

Decision Sciences

Simulation · Optimization · Decision Support · Statistical Modeling

APPLICATION DOMAINS