4+ years of extensive experience in statistics, data analysis, hydrology, climate change and programming, laying a good professional quality. Previous a project manager at evaluating the effect of climate change in Taiwan. Participated in two civil and water related international seminars and two domestic seminars in the form of posters. Published a journal in Taiwan Mining. Looking forward to applying the knowledge and experience I learned during my master's degree to solving practical problems related to human health and promoting public health.
Title of Thesis: The hydrological characteristics of Hehuan Mountain watershed and impact assessment of climate variation
Advisor: Kuo-Chin Hsu Ph.D
Resourceful, flexible, fast-learner, self-starter
Writing, reading, stock, minimalism, fitness
I have joined 2017 AECOM summer intern project, hired in ENV-RE department for 6 weeks. In the last week, my team used the district analysis to coordinate with soil and water improvement regulations, redesign the old camp into a smart elder health care center. Finally, we won the second place.
I brought my research to 2018 SWAT conference. In this conference, my topic was highly recognized. However, IPCC AR4 was not the newest climate change scenario, so I was suggested by the masters to adopt IPCC AR5 as new direction. Also, I got some new ideas by reading other works from this conference.
In this conference, we were asked to give a short introduction about our poster. This is a new policy and we were only informed the day before the presentation. My key contribution is to propose a easily used surrogate model that can be used to replace physical-based model that require detailed information when fractal behavior is observed in the input data.
Big data - ANN, DT, Cluster
In the class - Data Mining ( J. B. Tsai), I used use five different US stock market data to train the neural network parameters and analyze the price of TSMC. These five US stocks are Brent, NASDAQ, Dow Jones, S&P 500 and SOX. The data length is five years which is from 2013/10/14~2018/10/8. Decision tree and cluster also used to choose the vital variables. Applying ANN technology to prediction, the accuracy rate is up to 56%.