ABOUT ME
● I am a Ph.D. trained in physics, with strong communication skills developed from extensive teaching experience for 5+ years; ability to work independently or as part of a team; ability and eagerness to learn new things quickly
● Superior critical thinking and creative problem solving skills developed from 6+ years of experience in quantitative analysis by implementing theoretical many-body physics (condensed-matter physics), machine learning, deep learning, statistics, or data mining
● Desire to build and develop knowledge for the firm and solve client problems
● Programming proficiency at Python, R, Matlab, and SQL; familiar with C++, Tableau, and Spark
EDUCATION
INTERESTS
Data Science
Machine Learning, deep learning, and statistics
2013 - 2019
University at Buffalo, SUNY
Ph.D. in Physics
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Thesis title: "Pairing Phenomena from Low-Density Fermi Gases to Neutron Star Matter"
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Thesis advisor: SUNY distinguished Prof. Eckhard Krotscheck
Condensed Matter Physics
Superconductors and strongly correlated systems, such as ultracold atoms and neutron matters
Many-body Physics
Implement Green's function and linear response theory; Fermi Hyperneted Chain (FHNC) method or Hypernetted Chain (HNC) method; Correlated Basis Function (CBF) theory for bosons or fermions to study emergence in physics
2007 - 2009
National Taiwan Normal University
M.S. in Physics
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Thesis title: "Theoretical Studies of Optical Conductivity and Penetration Depth in Pnictide Superconductors"
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Thesis advisor: Prof. Wen-Chin Wu
2002 - 2006
National Taiwan Normal University
B.S. in Physics
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Excellent negotiation skills and leadership skills developed from high-school teacher training program in 4 years, and 1 year high-school-teacher intern followed by graduation
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Strong critical thinking and mathematical modeling developed from taking 8 core math courses in math department and 4 math courses in physics department
Honors
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Physics Department Fellowship for Outstanding Graduate Students, 2017-2018
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The Physics Graduate Student Memorial Fellowship, 2013-2014
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United Microelectronics Corp. (UMC) Scholarship, 2008
Certificate
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Machine Learning by Stanford University on Coursera [link]
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Neural Networks and Deep Learning by deeplearning.ai on Coursera [link]
2017 - present
Coursera
Data Science
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Experience with exploratory analysis, data cleaning, feature engineering, algorithm selection, and statistical modeling selection on real-world data as evidenced by 7 selected projects on GitHub
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Specialties in Machine Learning, Deep Learning, Statistical Programming, Probability Theory, Statistical Distribution, database modeling, Hypothesis Test
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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization by deeplearning.ai on Coursera [link]
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Structuring Machine Learning Projects by deeplearning.ai on Coursera [link]