• I'm a highly motivated M.S. in Computer Science candidate at The University of Chicago, specializing in Cloud Computing, Machine Learning, Data Analytics, and Distributed Systems.
• My passion lies in leveraging cutting-edge technologies to solve complex problems and drive innovative solutions.

Education:

• The University of Chicago (Chicago, IL)

  • M.S. in Computer Science, March 2025 (Expected)
  • Specialized in Cloud Computing, Machine Learning, Data Analytics, and Distributed Systems

• National Taiwan University (NTU) (Taipei, Taiwan)

  • B.A. in Economics and Double Major in Political Science, January 2022
  • Concentrated in Statistics, Econometrics, and Machine Learning

• Lund University (LU) (Lund, Sweden)

  • Exchange Program in Social Science, January 2022

Professional Experience:

• P.LEAGUE+ (Taipei, Taiwan)

  • Data Analyst, September 2022 - July 2023
  • Orchestrated the development of comprehensive player and referee datasets employing Synergy Stats and Python, yielding pivotal insights for tactical and strategic decision-making by teams, league authorities, and media representatives.
  • Executed sophisticated K-means clustering to analyze player performance metrics and team dynamics; utilized Streamlit to create interactive visualizations, enhancing stakeholder understanding and engagement, and applied Excel for systematic data management, supporting nuanced analytical narratives.
  • Conducted behavioral data analysis to distill customer trends and preferences, utilizing Python for data manipulation and Excel for data visualization, thereby informing customer engagement strategies and operational enhancements.
  • Designed and managed a robust data pipeline using Python to streamline the aggregation and preprocessing of complex datasets, leading to a more efficient workflow and timely insights for strategic initiatives.

• Fermilab (Data Science Clinic) (Chicago, IL)

  • Data Science Intern, March 2024 – June 2024
  • Enhanced Graph Neural Networks to improve neutrino detection in Liquid Argon Time Projection Chamber systems, increasing detection efficiency and accuracy.
  • Implemented a sawtooth mechanism and residual connections in neural networks, resulting in a 3% increase in model accuracy and a 5% improvement in overall performance.
  • Leveraged Python and PyTorch for model development and optimization, ensuring robust and reliable data analysis processes.
  • Collaborated with a team of scientists and data experts, contributing to groundbreaking research in particle physics and data science.
  • Analyzed complex data sets to derive meaningful insights, supporting the development of advanced detection methods and contributing to the broader scientific goals of Fermilab.

• The Climate Extremes Theory and Data (CeTD) Group – The University of Chicago (Chicago, IL)

  • Research Assistant, June 2024 - Present
  • Processed data pipeline for efficient handling of large scientific datasets.
  • Developed a parallel IO DataLoader utilizing dask and xarray for improved data processing performance.
  • Adapted and modified AI weather models to enhance forecast accuracy for predicting the Asian monsoon.
  • Contributed to projects aimed at assisting farmers in India by providing more reliable weather forecasts.

Projects:

• Buy Earth a Coffee Application (March 2024 - June 2024)

  • Implemented back-end functionalities in Node.js with MongoDB for data management, using Mongoose for database schema creation and data interaction.
  • Integrated AWS S3 for file storage, handling file uploads and secure storage with public access configurations.
  • Configured secure user authentication and session management using Next-Auth, enhancing application security and user experience.
  • Utilized React for front-end development, enhancing UI with custom components and managing state with hooks.

• NuGraph: a Graph Neural Network (GNN) for Neutrino Physics Event Reconstruction (Partnering with Fermi Lab, March 2024 - June 2024)

  • Advanced NuGraph3 GNN architecture, optimizing data aggregation and message-passing for enhanced event-level predictions.
  • Implemented a sawtooth mechanism for sequential node embedding updates, refining model accuracy and performance.
  • Applied residual connections in NuGraph3, boosting robustness and feature refinement across iterative message-passing.
  • Streamlined data pipelines with Python, enabling efficient data handling and supporting advanced analytical capabilities.

• Genomic Annotations Service (January 2024 - March 2024)

  • Led the design and development of a Genomic Annotations Service, a web service for gene data analysis utilizing Flask, Globus, and AWS cloud technologies including S3, EC2, SQS/SNS, DynamoDB, Lambda, and Step Machines.
  • Built RESTful APIs with JavaScript and Python, managing data workflows and service integration to provide a robust user experience.

• Generative AI Idea Validator for Circular Economy (January 2024)

  • Pioneered the design and deployment of a cutting-edge AI-driven validation tool using OpenAI GPT-4 to analyze and categorize concepts within the circular economy sector. The tool is engineered to autonomously produce detailed reports that evaluate the sustainability, commercial potential, and innovative aspects of new ideas.
  • Integrated Retrieval-Augmented Generation (RAG) with GPT-4 to enhance the AI's ability to fuse retrieved information with generated content, ensuring the production of highly accurate and contextually relevant sustainability assessments.

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