林柏宇

Software Engineer

Graduated from the Department of Computer Science at National Chengchi University, I am currently a fresh graduate entering the workforce. My main research interests are Recommendation Systems and Information Retrieval. I continue to learn and improve both my domain knowledge and techniques in these fields. I am eager to apply my skills and contribute to innovative projects in a dynamic work environment.

0975856550
[email protected]

WORK EXPERIENCE

Mar 2024 - Aug 2024
Taipei, Taiwan

Software Engineer

Headquarter.ai

  • Contributed to developing the product - Recommend-HQ, which enables users to build personalized recommendation platforms more easily. My primary responsibilities included automating schema generation and building AWS CDK stacks to support ETL processes and training pipeline, efficiently creating serverless recommendation applications tailored to various use cases.

Jul 2022 - Oct 2023
Taipei, Taiwan

Intern

Headquarter.ai

  • Textual Feature Research: Improved article recommendation by researching textual features.
  • KNN Filter Support: Assisted in implementing KNN filters for recommendation systems.
  • LLM Embedding Experimentation: Used LLM embeddings for course and job matching.

Jul 2020 - Jan 2021
Taipei, Taiwan

Intern

ACADEMIA SINICA CITI

  • Participate in internship projects, learn and apply NLP knowledge through news article classification, fake news detection.

EDUCATION

Sep 2017 - Jun 2021

NATIONAL CHENGCHI UNIVERSITY

Bachelor of Computer Science

Jul 2021 - Jul 2023

NATIONAL CHENGCHI UNIVERSITY

Master of Computer Science ( Member of CFDA & CLIP Labs )

KEY COMPETENCIES

  • Programming Languages : C / C++ / Python / Web (HTML, css, Javascript, React)
  • Languages : Mandarin Chinese, Taiwanese, English
  • Cloud Service Tools: Amazon Web Services (AWS)

PROJECTS / COMPETITIONS

OPEN GRAPH BENCHMARK : OGBL-COLLAB

  • Link prediction task on the collaboration network between authors indexed by MAG.
  • 5th place on leaderboard.

RECSYS CHALLENGE 2022

  • Session-based recommendation on Dressipi fashion recommendation dataset.
  • 13th place on leaderboard.

FASHION RECOMMENDATION SYSTEM

  • Undergraduate senior side project, implemented by Django, crawler, Elasticsearch, and some recommendation algorithms.
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