Roy Ho

Available for opportunities

UC Davis Graduate | B.S. Statistical Data Science, Minor in Computer Science

Currently: Seeking full-time Data Analyst / Data Engineer roles while building Solstice, a sneaker resale analyzer.

Email: royho.career@gmail.com

Location: San Francisco, CA

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About

Hi, my name is Roy Ho, and I am a recent UC Davis graduate with a Bachelor's degree in Statistical Data Science and a minor in Computer Science.

I'm interested in building data-driven solutions using Python, R, SQL, and Excel, with a focus on machine learning. I'm currently pursuing roles in data analytics and data engineering.

11+

Projects

Core Tools

Page AI Research Paper

Core Skills

Python
SQL
Tableau
Power BI
Excel
R
Pandas
Scikit-learn
Data Visualization
Machine Learning

Featured Projects

SF Restaurant Safety Map

Built an interactive map of 26,000+ health inspections across 6,200+ San Francisco restaurants using public DataSF data, an ETL pipeline into a SQLite schema, a Flask REST API, and a React + Mapbox frontend for search, filters, and inspection details.

Python, SQL, Flask, React, Mapbox, ETL

View on GitHub

Job Market Analytics Dashboard

Team-built dashboard analyzing 9,000+ tech job postings for salary, skill, and geographic trends. Contributed the scraping layer and a TF-IDF resume matching engine.

Python, Flask, scikit-learn, TF-IDF, Selenium

View on GitHub

Award Winner: Best Execution

Drowsy Driver Detection

Built a real-time drowsiness detection system using dlib facial landmarks and Eye Aspect Ratio thresholding, with a scikit-learn classifier on the Kaggle MRL dataset and a live OpenCV alert.

Python, OpenCV, dlib, scikit-learn

View on GitHub

Additional Projects

In Progress

Solstice

Sneaker resale analyzer that runs a 4B-parameter vision model fully on-device to identify shoes from photos and grade their condition, with a FastAPI and PostgreSQL backend tracking market prices over time.

Python, FastAPI, PostgreSQL, React, TypeScript

Heart Stroke Risk Prediction

Machine learning model to predict stroke risk from healthcare data, with preprocessing, training, and an interactive Streamlit app.

Python, scikit-learn, Streamlit

View on GitHub

Portuguese Wine Type and Quality Prediction

Classified red vs. white wines at 98.3% accuracy and predicted quality ratings using logistic regression, LDA, MANOVA, and PCA on chemical properties.

Python, scikit-learn, PCA

View on GitHub

Stock Trading Algorithm

Built a multi-factor stock screening model with NLP sentiment analysis (FinBERT), supervised classification, and an automated daily ETL pipeline delivering real-time investment signals.

Python, scikit-learn, NLP, ETL

NBA Player Performance Prediction

Predicted 5th-season NBA player performance using regression and classification models on historical stats and draft data.

Python, Random Forest, Gradient Boosting

View on GitHub

Analyzing Movie Reviews Across Genres

Compared IMDb audience reviews against professional critic reviews across five genres. Built the critic rating extraction pipeline and fit a RoBERTa sentiment classifier that outperformed the VADER baseline.

Python, Selenium, VADER, RoBERTa

View on GitHub

Drake Time Series Analysis

Forecasted Drake's popularity trends using 14 years of Google Trends data with ARMA and ARIMA models.

R, forecast, ggplot2

View on GitHub

NBA Player Salary Analysis

Analyzed the relationship between player performance metrics and salary structures using regression and clustering.

R, tidyverse, ggplot2

View on GitHub

Socioeconomic Predictors of Crime Rates

Modeled the relationship between poverty, unemployment, and crime rates using multiple linear regression and model selection.

R, ANOVA, AIC/BIC

View on GitHub

Graph-Based Shortest Path Analysis (Six Degrees of Kevin Bacon)

Built a graph traversal algorithm to compute degrees of separation between actors through shared movie appearances.

Python, BFS, Graph Algorithms

View on GitHub

Experience

JAIKE

Journal of Artificial Intelligence and Knowledge Engineering

January 2025 – Present

Artificial Intelligence Researcher

Conducted research on LLM-based automation, focusing on API-driven system design, agent orchestration frameworks, and productivity applications across research, coding, and enterprise workflows.
Authored a 25-page research paper on LLM-based automation and agent architectures, synthesizing peer-reviewed and industry research on API integration, architectural design patterns, system limitations, and responsible deployment; submitted for journal publication.
Served as a peer reviewer for JAIKE, evaluating research on retrieval methods in large language models, reasoning performance in extended tasks, and large-scale model architectures for methodological rigor and evaluation quality.

TechSprint Innovators

March 2024 – September 2025

Head of Data Engineering

Built a multi-factor stock screening model using fundamental, technical, and NLP-based sentiment features (FinBERT).
Developed and evaluated a supervised classification model in scikit-learn to predict price appreciation, performing feature selection, model tuning, and performance validation on historical market data.
Engineered and automated a daily ETL data pipeline (Python, yfinance, Alpaca API) running on a Raspberry Pi to filter equities, generate structured CSV outputs, and deliver real-time investment signals via Discord webhook.

AISC

AI Student Collective

September 2024 – April 2025

General Member

Built machine learning models for stroke risk prediction and real-time drowsy driver detection within structured project cycles.
Performed data preprocessing, feature engineering, model development, evaluation, and project presentations.
Worked within quarter-long sprint cycles with defined milestones, code reviews, and final project demos, following structured machine learning development workflows from ideation to deployment.