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最輕量、快速的招募方案，數百家企業的選擇
搜尋履歷，主動聯繫求職者，提升招募效率。
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- 搜尋僅開放付費企業檢視的履歷
- 檢視使用者信箱 & 電話
職場能力評價定義
陳威政, Jimmy
Job Title
Taipei, Taiwan
Incoming MET student at CMU ETC | Seeking for 2021 intern for software engineering and game development
工作經歷
研究助理
中央研究院資訊科技創新研究中心
一月 2017 - 七月 2020
Taipei, Taiwan
"Understanding User Behavior in a Cross-domain Scenario"
1. Establish a query suggestion system analyzing users’ behavior of when they changed browsing domains
2. Design and implement a critical ingestion pipeline to process over 5 TB of e-commerce data from Verizon Media using Spark and Pandas
3. Develop novel embedding mechanism which collects time-/cross- domain features and conducted experiments via deep learning framework (RNN-based, Attention) using TensorFlow and PyTorch
"Query Forecasting: Identifying Critical Learning Queries"
1. Analyzed data from Verizon Media’s e-commerce platform, which has millions of active users, and constructed a pipeline to analyze the textual embedding using Python, Shell scripts, and established modeling tools
2. Proposed and constructed a quasi-attention deep learning framework with TensorFlow, aiming to perceive customers’ purchasing intention. Simulation results verified the superiority of the proposed method
"Foresight Research and Its Application in Financial Technology Industry"
1. Utilized Twitter content, technical analysis, and Natural Language Processing (NLP) to analyze US stock market
2. Implemented machine learning algorithms to classify market sentiment using Python and SQL
學歷
國立台灣大學
經濟
2011 - 2015
陳威政, Jimmy
Job Title
Taipei, Taiwan
Incoming MET student at CMU ETC | Seeking for 2021 intern for software engineering and game development
工作經歷
研究助理
中央研究院資訊科技創新研究中心
一月 2017 - 七月 2020
Taipei, Taiwan
"Understanding User Behavior in a Cross-domain Scenario"
1. Establish a query suggestion system analyzing users’ behavior of when they changed browsing domains
2. Design and implement a critical ingestion pipeline to process over 5 TB of e-commerce data from Verizon Media using Spark and Pandas
3. Develop novel embedding mechanism which collects time-/cross- domain features and conducted experiments via deep learning framework (RNN-based, Attention) using TensorFlow and PyTorch
"Query Forecasting: Identifying Critical Learning Queries"
1. Analyzed data from Verizon Media’s e-commerce platform, which has millions of active users, and constructed a pipeline to analyze the textual embedding using Python, Shell scripts, and established modeling tools
2. Proposed and constructed a quasi-attention deep learning framework with TensorFlow, aiming to perceive customers’ purchasing intention. Simulation results verified the superiority of the proposed method
"Foresight Research and Its Application in Financial Technology Industry"
1. Utilized Twitter content, technical analysis, and Natural Language Processing (NLP) to analyze US stock market
2. Implemented machine learning algorithms to classify market sentiment using Python and SQL
學歷
國立台灣大學
經濟
2011 - 2015