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 five 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.
  • Adaptive Interfaces & Extended Reality (XR): Designing and evaluating adaptive modality systems that dynamically adjust input methods (e.g., gaze vs. hand-pointing) based on cognitive load and performance metrics, with applications to XR environments and human-computer interaction.
  • 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, including computational approaches that link physiological measures (pupillometry) with decision-making processes.

🛠️ 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). Developing computational models that integrate pupillometry with decision-making frameworks (e.g., Drift-Diffusion Model) to understand the interplay between physiological responses and cognitive processes.
  • 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, with ongoing work on linking DDM parameters to pupillometric dynamics to provide mechanistic insights into cognitive effort and arousal.
  • Extended Reality (XR) Development: Building research platforms for adaptive modality systems, implementing Fitts’s Law paradigms with dual input methods (gaze and hand-pointing), and developing real-time adaptive UI interventions based on performance metrics and cognitive load estimates.
  • Advanced Statistical Analysis: Employing linear mixed-effects models and Bayesian methods for complex, repeated-measures data (R). Implementing pre-registered experimental designs with rigorous exclusion criteria and equivalence testing.
  • 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). Developing web-based experimental platforms with precise timing control, comprehensive data logging, and real-time performance monitoring.

🚀 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. Building on my dissertation prospectus, the program integrates:

  • Pre-registered psychophysics and pupillometry experiments to characterize how neuromodulatory state and Cognitive Reserve shape dual-task interference across the adult lifespan.
  • Applied human factors case studies (e.g., surgical performance monitoring and adaptive XR interfaces) that translate these mechanisms into real-world, safety-critical settings.
  • Computational modeling and machine learning, including Drift-Diffusion Models linked to pupil dynamics and calibrated predictive models of cognitive state, to move toward individualized performance prediction and adaptive assistance policies.

Together, these strands are designed to bridge basic cognitive neuroscience, physiological measurement, and deployable human-centered technologies.

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