Research Focused:
As a dedicated researcher in Experimental Psychology, I investigate the dynamic relationship between physical agency and visual cognition, with a special emphasis on human perception. My expertise includes visual attention, visual working memory, and applying data analytics to deepen our understanding of these cognitive processes.
Proficient in Experiment Design, Programming, & Data Analytics
I specialize in designing and executing neuroimaging, behavioral, and eye-tracking experiments using platforms like MATLAB, Python, PsychoPy, Qualtrics, M Turk, Unity, and more. Beyond experiment design, I leverage advanced data analytics techniques—including Bayesian methods, regression analysis, and large dataset manipulation—to extract meaningful insights and drive evidence-based research conclusions. Additionally, I excel at bridging the gap between technical analysis and everyday understanding, ensuring that research outcomes and data-driven recommendations are accessible and impactful for both technical and non-technical stakeholders.
Current Role: Product & UX Research Analyst | University of Notre Dame
As a Product & UX Research Analyst at the University of Notre Dame, I bring an Agile mindset to my work, emphasizing iterative improvements, rapid feedback integration, and data-driven decision-making. I have designed and implemented tracking mechanisms to measure user engagement across research platforms, leading to a 15% increase in faculty participation. By developing interactive Tableau dashboards, I provide real-time insights into platform performance, enabling continuous optimization. My experience in Agile environments allows me to conduct user segmentation analysis efficiently, identify key personas, and refine communication strategies to enhance student engagement. I leverage data analytics to drive strategic decision-making, optimize resource allocation, and generate actionable recommendations for product enhancements—all while adapting quickly to evolving needs and insights.
Let's connect and explore the intersections of psychology, UX research, and data science!
Who Am I?
User-Centered Researcher Driving Actionable Insights
I am a User Experience and Human Factors Researcher with a background in experimental psychology, cognitive science, and data analytics. My expertise lies in understanding how users perceive, interact with, and make decisions within digital and physical environments. By leveraging eye-tracking, behavioral experimentation, and statistical modeling, I uncover insights that drive intuitive, data-informed design solutions.
With over five years of research and project management experience, I have led studies analyzing predictive gaze behavior, attentional selection, and cognitive load, producing findings that inform usability improvements, adaptive interfaces, and human-centered product development. I thrive at the intersection of quantitative research, UX strategy, and human factors, transforming complex data into actionable recommendations that enhance user engagement and system efficiency.
Data-Driven, User-Centered, and Iterative
In both UX research and human factors, I approach project management with a data-driven, user-centered, and iterative mindset. My philosophy is grounded in structured hypothesis testing, cross-functional collaboration, and adaptive problem-solving. I believe in aligning research objectives with business and user needs, ensuring that insights translate into actionable design decisions.
I prioritize efficient workflows, scalable methodologies, and rigorous data integrity—whether managing large-scale eye-tracking studies, optimizing usability testing protocols, or streamlining research operations. By leveraging agile methodologies, clear communication, and robust data analysis, I ensure that projects remain on track, flexible to user feedback, and impactful for stakeholders.
Above all, my approach emphasizes measurable outcomes and human-centered insights, ensuring that research findings drive meaningful improvements in user experience, product design, and system usability.