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\documentclass{resume} % Use the custom resume.cls style
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\newcommand{\email}{\href{mailto:yl307@rice.edu}{yl307@rice.edu}}
\newcommand{\linkedin}{%
   \href{https://www.linkedin.com/in/yu-cheng-eric-liao/}{\faLinkedinSquare\hspace{0.2em}Yu-Cheng Liao}%
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         #1 $\vert$ #2  $\vert$ \faEnvelope{} \email $\vert$ \linkedin
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\name{Yu-Cheng Liao}

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\begin{document}
\Address{(+1)346 628-2020}{Houston, TX}
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%	EDUCATION SECTION
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\begin{rSection}{Education}
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{\bf Rice University} \hfill {\em Aug. 2023 - Present} 
\\ Master of Computer Science\hfill { Houston, TX, USA }\\
{\bf National Tsing Hua University} \hfill {\em Sept. 2018 - Jan. 2023} 
\\ Bachelor of Science in Computer Science\hfill { Hsinchu, Taiwan }
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%	EXPERIENCE SECTION
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\begin{rSection}{Skills}\begin{itemize}
\item  {Programming}: {\bfseries Javascript}, {\bfseries Java}, {\bfseries C}, {\bfseries C++}, Python(PyTorch, Numpy), Shell Script, Verilog
\item {Frameworks/Technologies}: {\bfseries Node.js}, {\bfseries React.js}, {\bfseries Git, Linux}, {\bfseries PostgreSQL}, Docker, {\bfseries AWS}
\end{itemize}
\end{rSection}
\begin{rSection}{Experience}
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{\bf Institute of Information Science, Academia Sinica}\\
{Research Intern} \hfill {\em Jul, 2022 – Aug, 2022}
\begin{itemize}
\item Fine-tuned {\bfseries YOLOv4}, a well-known one-stage object detection model to predict the locations of remaining unlabeled tomb
\item Transformed real-world coordinates of tombs into object detection trainable datasets
\item Built dataset for Nanshan gravesite, one of the most representative, ancient gravesites in Taiwan
\end{itemize}

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{\bf National Tsing Hua University Vision and Learning lab}\\{Undergraduate research student} \hfill {\em Nov, 2020 – Nov, 2021}
\begin{itemize}
\item Introduced {\bfseries Mask2Hand} (\textcolor{cyan}{\href{https://arxiv.org/abs/2205.15553}{paper link}}), a deep network that predicts 3D hand pose and shape from a 2D binary mask of hand silhouette without relying on RGB or depth information.
\item Achieved comparable performance to {\bfseries state-of-the-art} RGB-based and depth-based methods, despite the more challenging input setting
\item With the parametric hand model and differentiable rendering technique, the self-supervised mechanism is integrated into the end-to-end training process without the need for human annotations
\item Conducted experiments with other states of the art method with the same binary images input
\end{itemize}
\end{rSection}


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%    PROJECTS
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\begin{rSection}{Selected Projects}
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{\bf Time Management Secretary}
\begin{itemize}
\item{Built a scheduling system (\textcolor{cyan}{\href{https://youtu.be/_AxEMolYK6M}{demo link}}) that helps people manage their time automatically by using {\bfseries React}, {\bfseries Redux} and {\bfseries JavaScript} for front-end, {\bfseries Node.js} and {\bfseries Express} for back-end and {\bfseries PostgreSQL} as database}
\item{Deployed the system by using {\bfseries Amazon Web Service} and established version control with {\bfseries Git}}
\item{Used {\bfseries OAuth 2.0} to enable third-party login services such as Google, Facebook}
\item{Implemented auto time scheduling algorithm with considerable parameters in this algorithm}
\end{itemize}
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\end{rSection}
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%    ACTIVITIES
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\begin{rSection}{Courses}
 Software Engineering, Software Studio, Operating Systems, Design and Analysis of
Algorithms, Data Structures, Introduction to Multimedia 

\end{rSection}
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%	SKILLS SECTION
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