• Major: 
• Computer Science and Information Engineering, National Taiwan University
• Lab and Other Experiences: 
• Undergraduate research assistant in Machine Discovery and Social Network Mining Laboratory, National Taiwan University
• OpenHCI human-computer interaction Workshop
  
• CS Related Courses: 
• Math: Calculus, Linear Algebra, Game Theory, Probability, Formal Languages and Automata Theory
• Data Structures and Algorithms: Data Structures and Algorithms, Algorithm Design and Analysis
• System: Systems Programming, Operating Systems, Network Administration and System Administration, 
• Machine Learning: Machine Learning Foundations, Machine Learning, Special topics on Machine Learning, Topics in Machine Learning, Applied Deep Learning, Security and Privacy of Machine Learning
• Others: Computer Networks, Computer Vision, Computer Security, Introduction to Secure Coding

Theoretical

• Data Structures and Algorithms. 
  
• Basic knowledge of Linux kernel and Operating System including CPU scheduling, memory management, file system, etc.
• Basic knowledge of network architecture and protocols. 
• Linear Algebra
• Mathematical theorem behind common machine learning model, e.g. PLA, SVM, tree-based models, Matrix Factorization, gradient-based optimization
  
• Theorem and algorithm about Computer Vision. 
  
• Cryptography. 

Practical

• Language:  C/C++, Python, Java, Rust. 
• Design Pattern. 
• Develop Android Apps in java with Android Studio. 
• Basic HTML/CSS, JS, php, SQL. 
• Git and git-flow. 
• Docker
• Security
• Reverse Engineering in Windows and Linux. 
• Binary Exploitation on Linux (pwn). e.g. overflow, stack pivoting, GOT hijack, ROP chain, use-after-free. 
• Basic technique of Web Attack. 
• Machine Learning researches in pytorch.
• Basic knowledge of tensorflow. 
• Data Analysis with Python(nympy, pandas,etc).

• AI cup - Machine Learning contest for Paper Annotating
• Team project. Given abstracts and citation graph of papers. Do a multi-label classification and predict the Categories of the paper. 
• Related Skills: Pytorch, Bert, node2vec, word2vec
• Lab - Chinese e-commercial unsupervised attribute extraction
• Use unsupervised approaches to train a model that can extract attribute from product title, such as color, size, model name, etc. 
• Related Skills: Image Embeddings, disentanglement, NLP, triplet loss, gaussian mixture embedding. 
• Lab - New Signboard Detection
  
• Detect new signboard in a video by comparing it with a series of old videos. 
• Related Skills: Metric Learning (Siamese, Triplet)
• Gray-box Attack and Defense of Adversarial Attack on Cifar10 Dataset
• Related Skills: PGD, FGSM, Adversarial training, SHIELD