Mike Tran

M.S Student·IDiR Lab, University of Texas at Arlington

I'm passionate about neural network model architecture, web development and automation practices. I enjoy building clean, robust, and maintainable systems. A well-written codebase is a work of art to me. My experience spans research, machine learning, web development, and cloud technologies. I'm also a novice photographer and a gamer in my free time.

Experience & Education

2022

2023

2024

2025

2026

Work

Research Assistant

IDiR Lab, University of Texas at Arlington · May 2024 – Present

  • Develop a full-stack dashboard for RDF database using Streamlit, Plotly, Cytoscape.js, and Mapbox to provide interactive graph visualizations
  • Containerized the complete application infrastructure with Docker for streamlined deployment and scalability. – Integrated Large Language Models (LLMs) using LangChain to enable natural language querying of the graph database
  • Implemented a Retrieval-Augmented Generation (RAG) pipeline enhanced with vector search capabilities using MongoDB Atlas Vector Search

Projects

Dashboard for RDF Graph Database with LLM Integration

Writing SPARQL queries is notoriously difficult for non-experts due to its complex syntax and steep learning curve, not to mention the need to understand the underlying graph structure and relationships between entities. To address this challenge, we developed an interactive dashboard that allows users to interact with the graph using either a predefined set of filters or natural language queries powered by Large Language Models (LLMs).

PythonStreamlitKnowledge Graph
GitHub ↗Live Demo ↗

Machine Learning for Corn Ear Phenotyping

Corn is one of most widely grown crops in the world, and accurate phenotyping of corn ears is crucial for breeding programs and yield estimation, as well as disease detection and management. I developed an end to end computer vision pipeline that can automatically detect corn ears from images, count individual kernels, and cluster kernels into spatial rows for structural analysis.

GitHub ↗Live Demo ↗

Sparql Profiler: A Tool for Automated Proling Knowledge Graphs

Knowledge graphs (KGs) are widely used to represent complex relationships between entities in various domains, such as healthcare, finance, and e-commerce. Heterogeneous KGs often contains hundreds of classes and thousands of properties, making it challenging for users to understand the structure and content of the graph. To addres this, we developed a tool to automatically profile KGs and generate comprehensive reports that summarize key statistics and insights about the graph.

GitHub ↗Live Demo ↗

Blog

Why your personal website is outdated (and how to fix it)

Stop fighting with code just to update your bio. Here is why I use Notion as my CMS to keep my portfolio alive and updating effortlessly

Read article →

Contact

  • cv résumé
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