Onsite software engineer with 4 years experience of developing and enhancing C++ system. Successfully develop fuzzy-logic-based algorithm for Taiwan radar data quality control. Solid background in mathematics and physical science. Graduate with distinct in physics. .
Taipei, Taiwan http://wangjb.github.io/
2017.01 - present
Onsite software engineer at Central Weather Bureau. Responsible for developing quantitative meteorological information algorithm and enhancing operational radar data processing system realized in C++. Cowork with meteorological colleagues, converting their FORTRAN code into C++ code for operational purpose. Using Python as script lang. Key projects:
2014.04 - 2015.09
Participates in MAIUS project, experienced building “Atom Chip” for realization of Bose-Einstein Condensate of K41 and Rb87. I learned:
2011.10 - 2014.03
Join Ultracold Atomic Physics Laboratory conducted by Dr. Yu-Ju Lin. During this very early setup stage, my main tasks are:
Physics major. Passed elective courses like “Introduction to Superconducting Physics and Quantum Computer”, Quantum Optics, Quantum Information.
Dissertation: "Non-abelian Geometric Phase and Quaternionic Hopf Fibration"
Supervisor: Prof. Chopin Soo
2009 - 2011
Physics major. Have well theoretical training in classic physics and quantum physics.
Passed elective course like Programming(C lang, FORTRAN), Astronomy, Circuits, Electronics(Experiments), Special Theory of Relativity(1), General Theory of Relativity(1), Astronomy Physics, Plasma Physics, Statistical Mechanics. etc.
Passed compulsory courses in department of mathematics like Advanced Calculus(1)(2), Abstract Algebra(1).
Graduated with distinct in physics.
2005 - 2009
Hi there, this is Jung-Bin Wang, a software engineer of International Integrated Systems stationed at Central Weather Bureau. I am responsible for developing meteorological information technique, enhancing and maintaining Taiwan radar data processing system.
During the second year of this job, I develop a fuzzy-logic based radar data quality control algorithm, constructed features and membership functions to identify and remove non-meteorological signals existed in Taiwan radar observation network, like interference pattern, ground clutter, sea clutter, etc. This work has two conference papers, also appear on 2020 ERAD conference.
My daily works include maintaining the backbone of Taiwan radar data processing system, which is realized in C++ language. From radar raw data decoding, radar quality control, radar data application (QPE, PID, etc.), to faster extracting radar archive data, and debugging, I have a full-stack working domain knowledge of radar data application, sophisticated in exploring features in space-domain and time-series data. On the other hand, I also write and rewrite FORTRAN programs to work with colleagues and use Python as scripting language for testing or data visualization.
I am highly confident on my self-learning ability. In college years, I prompt myself to grasp the opportunity to build the front-end website for the department, which is still working well now. When as a research assistant in Academia Sinica, I build the phase-locking circuits and bias coil circuits, which are key components for providing laser frequency and bias magnetic field in high accuracy in order to cool Rubidium 87 atoms down to micro Kelvins to realize Bose-Einstein Condensate.
Thanks to the well academic training in mathematics and physics, I can tackle well software and hardware problems, for example, Fourier transform technique just plays a key role in removing interference signals in Taiwan radar data network. I am a curious person who eager to know more. Keep learning on Coursera, I got certificates on deep learning related courses. Also play in Leetcode to strengthen myself to produce high quality algorithm in work.
I got following Coursera certificates on theoretical and practical knowledge on machine learning deep learning.
Besides learning theoretical course from MOOC, I am also doing a side project to training key word spotting model deploy on ARM Cortex MCU. Through this project I understand practical constraints which AIOT will encounter on deploying applicable models on devices.