Selected Projects

A selection of projects that show how I design behavioral measures, analyze human responses, evaluate model performance against human judgment, and translate messy participant-facing data into clear conclusions.

Human-referenced model evaluation

Human Speech Emotion Classification

Erdős Institute data science project comparing machine-learning approaches to human performance on speech emotion classification.

  • Worked with a speech-emotion dataset to evaluate how well models classified affective categories from audio.
  • Compared support vector machine and convolutional neural network approaches against a human benchmark.
  • Examined category-level error patterns to understand where model behavior aligned with, or diverged from, human judgments.
  • Framed model performance as more than an aggregate accuracy score: the goal was to identify what kinds of emotional judgments were difficult, ambiguous, or systematically confused.

Cognitive measurement

Theory of Mind + Inhibitory Control

A research program operationalizing abstract social-cognitive judgments into measurable behavioral outcomes across development.

  • Designed and analyzed tasks measuring how children reason about others’ false beliefs while managing competing reality-based responses.
  • Used dense longitudinal data and computational modeling to study how inhibitory control contributes to change over time.
  • Translated theoretical constructs—belief representation, response conflict, developmental variability—into testable predictions and interpretable measures.
  • Relevant proceedings: CogSci 2020 and CogSci 2026.

Participant-facing research operations

Lookit + Liberty Science Museum Studies

Remote and public-facing developmental studies with children and families, where data quality depends on clear tasks, good instructions, and careful moderation.

  • Supported child-friendly study design, participant comprehension, and smooth data collection in both online and museum-based contexts.
  • Attended to task failure modes such as misunderstanding instructions, attention shifts, parent interference, and incomplete responses.
  • Helped translate live research constraints into practical decisions about materials, moderation, and data interpretation.
  • Built experience communicating research goals clearly to families and adapting procedures in public-facing settings.

How I work

Across these projects, I’m interested in the same core problem: how to make judgments observable. Whether the judgment belongs to a child, an adult participant, or a model, I care about designing measures that reveal not only whether an answer is correct, but what kind of reasoning or failure may have produced it.

My work combines behavioral data, careful task design, and statistical and computational analysis to make those patterns interpretable.