Experience & Background

My academic journey, research, and work history

Education

My academic foundation

Ph.D. in Computer Science
Portland State University
Current
Currently Pursuing

Research: Large Language Models in Education & Efficient NLP

  • Large Language Models
  • Artificial Intelligence
  • Efficient NLP Systems
M.S. in Computer Science
University of Wisconsin-Milwaukee
Graduate
Jan 2022 – Dec 2023  ·  GPA 3.7

Full-time graduate studies in Computer Science

  • Machine Learning (Python)
  • Advanced OOP
  • Cloud Computing (AWS)
  • Advanced Networks & Program Analysis

Research & Work Experience

Projects and roles I've contributed to

Teaching undergraduate Information Technology courses, developing innovative curricula aligned with ABET standards, and mentoring diverse student populations through hands-on, project-based learning.

  • Teach Systems Analysis I–III, Database Systems (Intro & Advanced), Big Data, Data Mining, Python Programming, and C# Programming
  • Developed project-based learning modules aligned with ABET standards
  • Spearheading the design of a new Artificial Intelligence course curriculum
  • Actively mentor faculty, participate in curriculum committees, and contribute to accreditation efforts
  • Integrate Azure cloud computing platform into classroom instruction
Curriculum Design Azure Python ABET

Designed and delivered a Web Programming course covering modern JavaScript frameworks, CSS design, HTML templating, Git, and full-stack integration, while facilitating hands-on Agile projects.

  • Designed and delivered Web Programming course: JavaScript frameworks, CSS, HTML templating, Git, full-stack integration
  • Facilitated hands-on projects and Agile workflows to enhance student learning
  • Advised students on career development, boosting internship and co-op placements
JavaScript HTML/CSS Git Agile

Engineered enterprise-level Java/Spring Boot applications for financial services at Citi Bank, ensuring high availability, security, and performance aligned with business requirements.

  • Engineered enterprise-level Java/Spring Boot applications for financial services
  • Led backend modernization: achieved 17% increase in application speed and 24% faster analytics reporting via optimized RESTful API designs
  • Translated technical concepts for non-technical stakeholders to drive business acceptance
Java Spring Boot REST API FinTech

Designed scalable Java-based reporting solutions and implemented full-stack enhancements for eligibility compliance operations at Citi Bank.

  • Designed scalable Java-based reporting solutions: achieved 20% boost in system efficiency for eligibility compliance
  • Implemented JavaScript-based client-side validation and strategic database tuning
  • Enhanced Oracle SQL performance by 50% through query optimization
  • Bridged backend and frontend development teams as a versatile full-stack contributor
Java Oracle SQL JavaScript Full-Stack

Publications & Presentations

Research I've contributed to

📄
GRIPS: Grounded Pre-SQL Signature for Structured Data Reasoning
ACL
2026

Language models fail at structured-data reasoning not because they cannot produce SQL, but because they misalign questions with the schema before SQL generation begins. GRIPS is a neurosymbolic framework that separates question-to-schema grounding from SQL generation, improving accuracy by an average of 11 REMS points while reducing token usage by 47.8% across two benchmarks.

View Paper
🎓
ASEE Conference Paper: Diagnosing CS1 Misconceptions with LLMs: A Pilot Study on Alignment, Variability, and Prompt Sensitivity
American Society for Engineering Education (ASEE)
2026

As LLMs become routine tools in CS1 courses for debugging, code explanation, and feedback, their instructional reliability remains under-scrutinized; models that generate correct code may still misdiagnose the conceptual errors novice programmers actually make. This study examines whether widely used LLMs converge or diverge when diagnosing the same CS1 misconceptions, focusing on diagnostic alignment, cross-model variability, and prompt sensitivity.

View Paper
🏆
Free Reliable AI for Every Student: Measuring AI Consistency So Every Student Gets Answers They Can Actually Rely On
Oregon Institute of Technology
2026

Presented research at OIT's annual IdeaFest celebration of student scholarship and creativity. AI tools are increasingly used in CS education for code explanation, debugging, and feedback, yet most evaluations hide a critical reality: the same model can answer the same prompt differently across repeated runs, and educational AI must be trustworthy and reproducible, not just performant.

View Poster / Slides

Certifications & Awards

Recognition and credentials

Azure Developer Associate
Microsoft · 2022
M.S. Computer Science
University of Wisconsin-Milwaukee · GPA 3.7