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reducing parameter training volume to 2% , decreasing memory usage by 70%, and increasing training speed by 25%. Architecture Optimization: Used Direct Preference Optimization (DPO) to optimize Reinforcement Learning, improving model efficiency and decision quality. [ G itHub ] Graduate Research Assistant OctOct 2023 Institute of Information Science, Academia Sinica, Taiwan Natural Language and Knowledge Processing Lab (NLP Lab) Publication: Published in ACM CIKMSequential Text-based Knowledge Update with Self-Supervised Learning for Generative Language Models . [ Paper | GitHub ] Performance has improved by 18.8% compared to traditional methods in the past and surpassed SOTA LLMs by 39.6

Patrick Hsu AI Research & Development As a seasoned AI engineer with six years of experience, I specialize in computer vision, 3D body model reconstruction, generative AI, and possessing some knowledge in natural language processing (NLP). | New Taipei City, [email protected] Work Experience (6 years) Algorithm Research & Design• TG3D Studio MayPresent A skilled engineer specialized in computer vision and generative AI with experience in developing and training AI models for digital fashion applications. Body AI: Virtual Try On Integrated cutting-edge technologies such as Stable Diffusion, ControlNet, and Prompt Engineering to create a sophisticated system for

趙啟雲 chiyun chao A software engineer with 4 years of experience in AI model training and backend development. In the domain of machine/deep learning, I am familiar with: - focus on NLP(Natural language processing) and DPI(Deep packet inspection) - NLP networks such as Transformer, BERT, T5 - have experience with AI training platform development with mlflow, Label Studio, DVC In terms of backend development, I have the following experience： - backend framework: Spring boot, Flask - familiar with DB syntax for Elasticsearch, PosgresSQL and MSSQL Taiwan E-mail: [email protected] Skill languages Python JAVA backend

HUEY-LONG CHEN 陳惠龍 Kaggle Competitions Expert: https://www.kaggle.com/alanchen1115 Competitions: NLP (自然語言處理): - Silver medal (solo): (Kaggle) The Learning Agency Lab - PII Data Detection: Develop automated techniques to detect and remove PII from educational data. 2024/04/24 - Silver medal (solo): (Kaggle) U.S. Patent Phrase to Phrase Matching: Help Identify Similar Phrases in U.S. Patents, 2022/06/21 Recommendation system (推薦系統): - Silver medal (solo): (Kaggle) OTTO – Multi-Objective Recommender System: Build a

incorporating: - A clean architectural approach - Integration with Firebase services - Support for the latest Android features ensuring scalability for future organizational growth - Successfully retained a 4.6-star user rating and boosted job application rates by 10%. • AI Implementation: Created an AI tool to convert UI test cases from natural language into UiAutomator2 scripts, seamlessly integrating them into the CICD process. This resulted in a 90% acceleration UiAutomator2 test case generation. • Technology Introduction: - Flutter: Developed an in-house App Dashboard to consolidate Android/iOS/backend CICD build statuses and test report outcomes. Single-handedly

including Recurrent Neural Network (RNN), word2vec, paragraph vector. Working Experience Intern, JulDec 2014 Phalanx Biotech Develop machine learning solutions for cancer gene detection and TSMC semiconductor quality prediction. Intern, JulAug 2013 Solid State System Assisted in CMOS MEMs microphone product development including proto-type simulation, fabrication/packaging process, defect analysis and market survey. Achievements 1. Qualified of preliminary contest of TSMC Big Data Analytics for Semiconductor Manufacturing. Taiwan,Completion of Broad-DREAM Gene Essentiality Prediction Challenge, 2014 Professional Skills Research Field Machine Learning Deep Learning Computer Vision Natural Language Processing Lang...

expressions, enhancing user interaction experiences. Emotion Recognition Systems: Developing AI systems capable of understanding and responding to user emotions, used to enhance customer service and user interactions. Voice Technology (TTS): Natural Voice Generation: Employing Text-to-Speech (TTS) technology to convert text into natural, fluent voice across multiple languages, suitable for virtual assistants and customer service bots. Voice Interaction Interfaces: Developing voice interaction interfaces that enable digital personas to conduct natural dialogues, offering more humanized interaction methods. Large Language Models and Natural Language Processing: Personalized Response Generation: Leveraging personality-driven large language models to provide customized