Mountain View, California, United States
Hi, I’m Justin! I’m originally from Taipei, Taiwan and currently working as a software development engineer at AWS Glue/Lake Formation in the San Francisco Bay Area. I graduated in 2023 from UC Berkeley with High Distinction in General Scholarship and double majors in Computer Science and Operations Research & Management Science (ORMS). My technical interests include AI/ML (CV, NLP, RL, robotics) and big data cloud analytics services. I’m also interested in learning more about the tech venture capital space. Feel free to connect and reach out to me at [email protected]! Resume can be provided upon request. Btw, I’m passionate about tennis, baseball, and motor racing!
AWS Glue & Lake Formation (Apache Iceberg Open Table Format Maintenance team)
Taught probability theory to ~300 students 🎲 An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
AWS AI Computer Vision - Amazon Rekognition (Stored Video Engineering team)
Machine Learning & Data Analytics (MLDA) team Remote due to Covid
Data Structures course with ~2000 students Fundamental dynamic data structures, including linear lists, queues, trees, and other linked structures; arrays strings, and hash tables. Storage management. Elementary principles of software engineering. Abstract data types. Algorithms for sorting and searching. Introduction to the Java programming language.
Introductory linear algebra, differential equations, and circuit design course with ~1000 students This course and its follow-on course EECS16B focus on the fundamentals of designing modern information devices and systems that interface with the real world. Together, this course sequence provides a comprehensive foundation for core EECS topics in signal processing, learning, control, and circuit design while introducing key linear-algebraic concepts motivated by application contexts. Modeling is emphasized in a way that deepens mathematical maturity, and in both labs and homework, students will engage computationally, physically, and visually with the concepts being introduced in addition to traditional paper/pencil exercises. The courses are aimed at entering students as well as non-majors seeking a broad foundation for the field.