Master of Science (M.S.) in Electrical Engineering and Computer Science (EECS) - CGPA: 4.04 / 4.30

• Research Areas: Vision-Language Model, Image Processing, Computer Vision, Generative AI
• Coursework: Data Mining, Machine Learning for Signal Processing, Pattern Recognition, Deep Learning, Digital Image Processing, Mobile Robots, Applications of Language Processing in Practice, Computer Vision, Practice in Entrepreneurship

Bachelor of Engineering (B.Eng.) in Information Engineering (IT) - CGPA: 3.65 / 4.00

• Thesis: Online Lectures Monitoring Software Component Development and Engineering
• Coursework: Algorithms and Data Structures, Artificial Intelligence, Image Processing and Computer Vision, Natural Language Processing, Big Data and Analytics, Database Engineering, Software Engineering

• Generative AI team member at NYCU Hucenrotia Lab; designed a LINE Bot–ChatGPT system on Azure and developed VLM-based vision modules for object detection/localization. Focusing thesis on open-vocabulary detection for robotic assembly.
• Skills: Python, Image Processing, Computer Vision, Deep Learning, Generative Artificial Intelligence, Natural Language Processing, System Architecture and Integration, Project Management

• Created Automate Address Domicile Prediction and Correction, an in-house program and algorithm using pattern matching technique and dictionary, solving and correcting valid but incorrect domicile name aliases. The accuracy results for correcting city domicile name aliases 79,1% and for district 97,6%.
• Created Personal Identifiable Information (PII) Scanner, in-house algorithm, model, and program to scan, profile, tag PII from database tables. Outperforms Google Sensitive Data Protection for Indonesian PII by 40%, improving from 35-50% to 75-91%.
• Skills: Python, SQL, Machine Learning, Natural Language Processing, Data Science and Analytics, JupyterLab, BigQuery, Docker, Looker Studio, Airflow, Streamlit

• Empirically evaluated three sexism-detection systems (fine-tuned Transformer fusion; zero-shot AR & diffusion LLMs) using a unified two-stage bilingual-fusion pipeline with annotator demographics.
• Paper published. Joy Chrissetyo Prajogo, Lung-Hao Lee, and Hsien-I Lin (2025). NYCU-NLP at EXIST 2025: An Empirical Study of Annotator-Aware Two-Stage Pipeline for Sexism Detection in Tweets. In Proceedings of the Working Notes of CLEF 2025 - Conference and Labs of the Evaluation Forum, Madrid, Spain, 9-12 September 2025, vol. 4038, pp. 2119-2132.

• Adapted CycleGAN to generate synthetic metallic surface defect images to diversify detection training data for AOI model.
• Paper published. S. Sanjaya, H. -I. Lin and J. Prajogo, "CycleGAN-Based Synthetic Data Generation and Automatic Image Annotation for Metallic Surface Defect Detection," 2025 13th International Conference on Control, Mechatronics and Automation (ICCMA), Paris, France, 2025, pp. 528-533, doi: 10.1109/ICCMA67641.2025.11369560.

• Built the vision system for a Generative-AI 3-Cup-and-Ball robot demo using YOLOv7 + SORT and custom in-cup ball tracking.
• Paper published. N. Khoirurizka, J. C. Prajogo, S. Perkins, C. -H. Kuo and H. -I. Lin, A Case of Cups and a Ball: Utilizing Generative Artificial Intelligence for Human-robotic Collaboration in Task Execution, 2024 International Automatic Control Conference (CACS), Taoyuan, Taiwan, 2024, pp. 1-6, doi: 10.1109/CACS63404.2024.10773301.