level with a size error margin of less than 3 cm. Computer Vision Body Reconstruction Stable Diffusion DApp: Exercise Classification Implemented real-time activity recognition for specified exercises, including but not limited to push-ups and squats, enabling accurate tracking and analysis of workout routines. Skeleton Detection Motion Classification Body AI: 3D Body Data Analysis Engage in a variety of AI side projects focused on leveraging 3D body data, encompassing tasks such as Body ID Recognition, Body Measurements Prediction, and Body Shape Classification. Mesh Data Processing Body Modulization: SMPL Project Engineer • Acer Inc. JanMay 2021 Working

in distributed computing, optimizing code execution across thousands of systems to significantly improve processing speed and efficiency. ◆Developed sophisticated data visualization tools to distill complex datasets into actionable insights, aiding strategic decision-making. The University of Tokyo, Foreign Researcher (OctSep◆Pioneered a neural network-based approach for cell image classification and data visualization, enhancing lab capabilities in biological research. ◆Designed a user-friendly GUI for neural network model training, democratizing access to advanced computational tools for non-programmers. Education National Chiao-Tung University, Ph.D. - Photonics, 2015 ~ 2020 Development of Intelligent Wearable Near Infrared Spectroscopy

based Project using SIMATIC SSignal conditioning of data received from the Aircraft Engine MFI-17 Super Mushshak Part Task Trainer (Simulator) Team lead of the Hardware installation and interfacing with soft instruments Using National Instruments NI-Daq 6343 Hardware designing of Trim Panel using Arduino Uno Foreign Object Debris Detection, Classification, and Localization using Deep Learning Python language based Project using YOLO-V7 (you only look once) Addressed the Class imbalance in FOD-A Dataset FOD detection/localization with improved accuracy Data Acquisition design and implementation of a Level-D Simulator (C130-H) NI-PXI Chassis-based DATA ACQUISITION

and deployed it to the environment. Major Product and Project Experience AI training platform backend development with Label Studio/DVC/MLflow Developed training templates. AI-SaaS Implemented NLU tasks using BERT-based models to provide functions such as named entity recognition, entity relation extraction, and article classification . Implemented NLG tasks using T5 to provide article summarization functionality. NetProbe - DPI Use 1D-CNN and MLP models for encrypted network traffic identification . Use the Autoencoder model for unknown packet detection . Taiwan Ministry of the Interior Investigation Bureau Police Station - AI Crime Investigation Trace Analysis

氣道擴散偵測競賽 I：運用物體偵測作法於找尋STAS, 2022/06/02 - Bronze medal (team): (Kaggle) VinBigData Chest X-ray Abnormalities Detection: Automatically localize and classify thoracic abnormalities from chest radiographs, 2021/03/30 Classification (影像分類): - Bronze medal (solo): (Kaggle) Human Protein Atlas - Single Cell Classification: Find individual human cell differences in microscope images, 2021/05/12 - 5th place (team): (Aidea AI CUP) Mango grade classification, 2020/12/29. https://reurl.cc/

detection, segmentation, and classification AI scenario. Good communication skills with doctors' demands and collaboration with colleagues. Patent Disclosure: Ultrasound detect and notify system. (serial number: I學歷 SepJun 2 National Taiwan University of Science and Technology Masters in Electrical Engineering Thesis "Online Data Stream Analytics for Dynamic Environments Using Self-Regularized Learning Framework", IEEE journal SepJun 2020 Yuan Ze University Bachelor in Electrical Engineering Skills Customer/VC negotiation and customer services DL/ML/AI algorithm, keen problem solving 3D modeling (Blender) Python, Matlab, Tensorflow, Keras Object detection, Classification, [email protected]

modules：Resblock, GhostBottleNeck, SE-layer, DarkBlock. Used Attention mechanism：StripPooling, MixedPoolingModule, SelectiveKernel . According to the input data, use convolutional layers of different dimensions (1D~3D) to learn information. Used weight standardization to assign weights to improve model training effect. Built a composite model of regression and classification. AI development environment management Used docker or Anaconda to establish and maintain the development environment with GPU. Set up the environment to use the LLM (LLAMA2, Taiwan-LLaMa, Codellama, Llama2-chinese-13b, etc.) Model Training and Tuning Tips Adjusted the data batch size according to the