RESEARCH

I study how data-driven systems influence user behavior, motivation, and decision-making, and how they can be designed to adapt responsibly to different users.

Minimalist abstract architectural geometry, sharp concrete angles, soft natural daylight casting subtle diagonal shadows, warm off-white tones, 35mm photography.
Minimalist abstract architectural geometry, sharp concrete angles, soft natural daylight casting subtle diagonal shadows, warm off-white tones, 35mm photography.
How It Works
Paper

Adaptive Learning Interfaces

Adaptive learning interfaces adjust feedback, dashboards, and learning support based on learner behavior, progress, motivation, and individual differences.

My work specifically investigates how social comparison in these interfaces can be adapted to guide students without creating unnecessary pressure, discouragement, or loss of agency.

HIGHLIGHT

Research Focus

Technical performance is only one part of whether an AI system succeeds. Its value also depends on how people interpret its outputs, how it fits into existing workflows, how reliably it performs across users and contexts, and whether its effects can be evaluated. My research brings these questions together across four interconnected areas.

A close-up of a student interacting with a tablet displaying personalized learning feedback.
A close-up of a student interacting with a tablet displaying personalized learning feedback.
GenAI in Education

Using AI in education "the right way" by providing pedagogically appropriate feedback.

Students comparing progress on a classroom leaderboard displayed on a screen.
Students comparing progress on a classroom leaderboard displayed on a screen.
Adaptive Social Comparison

Tailoring feedback and dashboards to individual learners.

Natural Language Processing

Fine grained analysis of social media messages to help stakeholders track customer sentiment.

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AI For Productivity

Building agents that empower humans, not replace them.

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Algorithms must align with measurable human behavior.

We bypass speculative tech-stack hype. By integrating behavioral feedback loops directly into applied machine learning models, we ensure your systems adapt to actual user patterns rather than theoretical assumptions.

Applied Proof

Case Studies

Enterprise ML
EdTech Analytics
GenAI Deployment
Behavioral Alignment
Adaptive Learning
Responsible Auditing

Re-engineered a predictive routing engine for a global logistics provider, reducing operator override rates by forty percent through cognitive alignment.

Designed a behavioral analytics framework for an adaptive learning platform, mapping student engagement patterns to optimize content delivery paths.

Conducted empirical evaluation of a large language model deployment, establishing safety guardrails and human-in-the-loop validation protocols.