Exploring how people and autonomous systems can work together safely, effectively, and with appropriately calibrated trust — on our roads and in our technological future.
My work sits at the intersection of cognitive psychology, human factors engineering, and autonomous systems — asking how we make the automated future safer and more equitable for everyone on the road.
How autonomous vehicles can safely communicate intent to pedestrians, cyclists, and other drivers through external interfaces and behavioral cues — making the actions of automation legible to those who must respond to them.
Building empirical models and design interventions that support appropriate trust calibration in automated driving systems — preventing both dangerous over-reliance and the costly disuse of beneficial automation features.
Examining how exposure to AV technology — as passenger, driver, or nearby road user — changes manual driving behavior, risk tolerance, and situational awareness. Extends his Rice honors thesis (Distinction in Research).
Examining how AI design choices in cybersecurity and transportation contexts affect user understanding, decision-making, and reliance — culminating in a forthcoming Springer handbook chapter with the HAC Lab.
Contributed to research on how drivers detect and respond to stopped and slowed lead vehicles during nighttime driving conditions — published at HFES 2023 with Rice faculty and collaborators.
Conducting rigorous behavioral experiments with driving simulation, eye-tracking, and advanced statistical modeling. Trained through the HAC Lab and Rice's STaRT@Rice statistical methods program (2024 Scholar).
Research questions I am currently pursuing in the HAC Lab at Rice University.
Active
A longitudinal study examining how trust in automated driving systems evolves with experience — particularly following automation failures or near-misses. The goal is to identify the specific conditions under which appropriate trust recovery occurs and what design features support re-calibration rather than permanent disuse or dangerous complacency.
Active
Building directly on his Rice undergraduate honors thesis (Distinction in Research and Creative Works), this project examines whether exposure to AV technology as a passenger or nearby observer changes people’s manual driving behavior — including speed choice, following distance, and risk acceptance — in subsequent unaided driving tasks.
Ongoing
Extending AV-human coordination research to complex urban settings with mixed traffic — buses, bikes, pedestrians, and personal AVs sharing space with competing demands on attention.
Examining how personality traits, prior technology experience, cultural background, and age moderate trust formation in automated systems — informing personalized interface design strategies.
Translating empirical findings into evidence-based recommendations for AV deployment standards, eHMI regulatory frameworks, and infrastructure design at the state and federal level.
Ensuring autonomous systems are designed equitably — with attention to older adults, people with disabilities, and communities with limited prior exposure to emerging transportation automation.