Hi, I’m John 👋 — a data scientist and human–AI interaction researcher focused on AI Safety, usable privacy, and decision support for vulnerable users, currently children and families.

I’m currently a Data Science M.S. student at UC San Diego where I design and evaluate systems that help families, students, and communities interact with AI and data safely and effectively.

• Child & family safety in human–AI interaction
  Parental controls, child-safe LLMs, and transparency/logging tools for families.

• Usable privacy, AI safety, and governance
  Turning people’s mental models of risk into concrete controls, interfaces, and policy-facing evaluation methods.

• Learning, metacognition, and visualization
  Tools that help students reflect, track progress, and make better decisions; interfaces as experiments on human judgment.

• Family-Safe GenAI & Parental Controls
  Mixed-methods study + prototype

  • Interviewed parents of K–12 students about how their kids use GenAI chatbots and what they want to moderate.
  • Built LLM-backed prototypes with controls over topics, tone, and when the model defers to an adult, plus visualization of children’s usage.
  • Prototypes built on top of open-source UIs such as
  • Code: private; available on request where appropriate.

• Compass-X – Metacognition Tool for CS2
  Web app + large-scale deployment

  • Full-stack tool that prompts students to reflect on their study plans and progress in data structures courses.
  • Deployed in a large CS2 course; used logs + grades + surveys to study relationships between usage, learning outcomes, and confidence.
  • Code: private; available on request where appropriate.

• Explainability Sanity Checks with LIME
  Reproducing and extending XAI sanity checks

  • Experiments on how stable and trustworthy LIME explanations are under different perturbations and model settings.
  • Repo: XAI-Sanity-Checks-and-Understanding-LIME

• Topic Modeling: NMF vs BERTopic
  Classical vs transformer-based topic models

  • Comparative analysis of NMF and BERTopic on real-world text data, including evaluation metrics and visualizations.
  • Repo: Topic-Modeling_NMF-vs-BERTopic

• Predicting NYPD Response Times
  Public safety + ML pipeline

  • End-to-end pipeline (data cleaning, feature engineering, modeling, evaluation) for predicting NYPD emergency response times from public 911 data.
  • Repo: Predicting_NYPD_Response_Times

• Analyzing San Diego Potholes
  Infrastructure equity & service patterns

  • Spatial analysis and visualization of San Diego 311 pothole requests to explore how service varies across neighborhoods.
  • Repo: Analyzing_San_Diego_Potholes

• Hybrid Graph-Based Content Filtering for Music Recommendation
  Recommender systems & graph-based methods

  • Explores hybrid graph/content-based filtering for music recommendation.
  • Repo: Hybrid_Graph-Based_Content_Filtering_for_Enhanced_Music_Recommendation

• Global Art Exploration and Analysis
  Knowledge graphs & cultural data

  • Uses Neo4j, PostgreSQL, and Redis to explore and analyze global art collections.
  • Repo: Global_Art_Exploration_and_Analysis

• Languages: Python, R, SQL, Java, HTML/CSS/JavaScript
• Data & ML: pandas, scikit-learn, basic PyTorch/TF (prototyping), time series, recommender systems, topic modeling
• Data systems: Jupyter, Dask, Spark (intro), AWS and containerized environments
• Human-centered methods: interviews, surveys, thematic analysis, experimental design, A/B testing
• Other: reproducible pipelines, careful documentation, evaluation-first thinking

• Understanding Parents’ Desires in Moderating Children’s Interactions with GenAI Chatbots through LLM-Generated Probes — CHI submission (first author)
• Student Usage of Metacognition-Promoting Tool in a CS2 Course and its Relationship with Performance — SIGCSE TS 2025
• Engagement with Metacognition-Promoting Web-based Interventions and its Relationship with Learning Outcomes — ASEE 2025
• Uncovering Meaningful Computing Contexts for Incarcerated College Students — ITiCSE 2024
• An Empirical Evaluation of Live Coding in CS1 — ICER 2023
• Understanding and Measuring Incremental Development in CS1 — SIGCSE TS 2023

Full list and PDFs on Google Scholar.

• Email: jjdrisco [at] ucsd.edu
• LinkedIn: linkedin.com/in/jjdrisco