Research Interests

🔬 Research Statement

My doctoral research investigates the intricate relationship between physiological arousal, cognitive effort, and the integrity of the human brain’s neuromodulatory systems. I am fundamentally interested in understanding why cognitive performance, particularly in aging, becomes vulnerable under demanding real-world conditions.

My work aims to move beyond simple behavioral metrics by integrating psychophysics, pupillometry, and computational modeling to deconstruct the subcomponents of decision-making. By precisely measuring the impact of physical and cognitive load, I seek to build predictive models of performance that can inform the design of safer, more adaptive, and more inclusive technologies for a range of user populations.

Core Research Areas

My research program is built on four interconnected pillars:

  • Aging & Cognitive Neuroscience: Characterizing how age-related changes in the Locus Coeruleus (LC) and its associated neural circuits impact cognitive functions, with a focus on identifying potential biomarkers for neurodegenerative disorders.
  • Dual-Task Interference & Performance: Quantifying how concurrent physical and cognitive stressors interact to impair performance across multiple domains, including perception, memory, and metacognition.
  • Applied Human Factors: Translating cognitive science insights to real-world high-stakes environments (aerospace, healthcare, manufacturing) to enhance safety and performance through data-driven design.
  • Personalized Cognitive Modeling: Moving beyond group averages to model how individual differences in factors like Cognitive Reserve predict a person’s unique resilience to cognitive overload.

🛠️ Methodological Expertise

To address these questions, I specialize in a multi-modal approach to data collection and analysis:

  • Psychophysics: Designing and implementing precise behavioral experiments to measure perceptual and memory thresholds (PsychToolbox).
  • Pupillometry: Using pupil dilation as a real-time, non-invasive biomarker of cognitive load and physiological arousal (MATLAB, R, Python).
  • Machine Learning: Developing predictive models for cognitive state classification using ensemble methods (XGBoost), feature engineering, and cross-validation techniques.
  • Computational Modeling: Applying models like the Drift-Diffusion Model (DDM) to deconstruct decision-making processes.
  • Advanced Statistical Analysis: Employing linear mixed-effects models and Bayesian methods for complex, repeated-measures data (R).
  • Neuroimaging: Analyzing Diffusion MRI data to investigate white matter integrity and its relationship to behavior (FSL, QSIPrep).
  • Real-Time Analytics: Building interactive dashboards and monitoring systems for applied research contexts (R Shiny, web technologies).

🚀 Research in Action

See concrete applications of these research approaches in my Portfolio, featuring case studies that demonstrate the translation of cognitive science principles to real-world challenges in aerospace, healthcare, and education.

🎯 Current Research Focus

PhD Dissertation (2025): “A Psychophysiological Approach to Modeling Dual-Task Interference: Pupillometry, Cognitive Reserve, and Personalized Performance Prediction”

This research aims to develop a comprehensive framework for predicting when and why individuals experience cognitive overload in dual-task scenarios, with applications to aging, technology design, and occupational safety.

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