Hi! I'm Arsyad Kamili
Undergraduate Computer Science student at the National University of Singapore
- Specializing in Database Systems, Parallel Computing, Algorithms
- Minoring in Quantitative Finance and in Urban Studies
December 2023 - Present (August 2024)
Software Engineer Intern @ ShopBack
Develop and maintain the company's Business Process system and platform.
August 2022 - November 2023
Teaching Assistant @ National University of Singapore
Named to NUS SoC's Honour List of Student Tutors (AY22/23). Facilitated tutorial and lab sessions, graded assignments, and provided consultations for 8 to 15 students in CS1101S (Programming Methodology) and CS2030S (Programming Methodology 2), imparting fundamental computer science concepts and functional programming principles in CS1101S, while delving into object-oriented design and advanced functional programming techniques in Java for CS2030S. I also taught 50 students on CS2100 (Computer Organization) as a tutor; guided students in understanding the low-level details of computer architecture, C, assembly language, and cache management.
May 2023 - August 2023
Research Intern @ A*Star Institute of Infocomm Research
Optimized, pipelined, and parallelized network flow and data transfer algorithms across distributed computer vision and mapping systems in C++ by utilizing the latest papers and publications pertaining to the topic to achieve more spread-out and accurate point cloud data within a third of the processing time.
May 2021 - August 2021
Trainer @ Undisclosed IT Firm
Trained the employees fundamentals of asynchronous programming and FastAPI, functional programming paradigm, and React.JS to assist the company in transitioning its tech stack to a set of new one heavily involving FastAPI in its backend and ReactJS for its frontend.
March 2021 - May 2021
Finance Analyst Intern @ Undisclosed IT Firm
Generated efficient finance analytics report pipeline for a saving and loan cooperative client handling over Rp. 400 billion of asset by utilizing Oracle SQL and Python's Pandas library. The main outcome was to efficiently collect millions of rows of data scattered across multiple databases to create a Markov Chain-based transition matrix to assess the probability of default of the loans.