I am Ke Haur Taur, currently a graduate student studying Electrical and Computer Engineering at the University of Michigan, Ann Arbor
I am Ke Haur Taur, currently a Masters Student studying Electrical and Computer Engineering at the University of Michigan, Ann Arbor.
I engaged in an independent study starting from my junior year in undergrad at National Central University, which was about regression applications of machine learning. Also in my junior year, I also took part in a summer research program at University of California San Diego which focused on designing ML hardware accelerators based on my skill set of Verilog programming and digital logic design on a FPGA. With this foundation, I was able to conduct research on efficient hardware implementation of computational kernels used in ML.
Current Locale:
Ann Arbor, MI, United States
Email Correspondence:
khtaur [at] umich [dot] edu
Master's degree, Electrical and Computer Engineering
Rackham Graduate School of Engineering
Dates attended or expected graduation: 2020 – 2022 (Expected)
Admitted Area of Focus: Embedded Systems
EECS 470: Computer Architecture
EECS 504: Foundations of Computer Vision
Master's degree, Electronics Engineering
Graduate Institute of Electronics Engineering
Advisor: Professor Sy-Yen Kuo
Dates attended or expected graduation: 2020.09 -- 2021.01 (Dropped Out)
Admitted Area of Focus: Memory Synchronization
EEE5022: Computer-Aided VLSI System Design
Bachelor's degree, Electrical Engineering
Independent Study Advisor: Professor Wen-June Wang
Dates attended: 2016 – 2020
Interested Area of Focus: Machine Learning, Digital Logic Systems and Computer Architecture
Computer Organization
Data Structures
Statistical Learning
Probability and Statistics
Intro. to VLSI Design
Neural System and Applications
KH Taur, XY Deng, MH Chou, JW Chen, YH Lee, WJ Wang, "A study on Machine Learning Approaches for Predicting and Analyzing the Drying Process in the Textile Industry", International Automatic Control Conference (CACS), Nov. 2019
DOI link: 10.1109/CACS47674.2019.9024364