Python, C/C++, Java, SQL, Matlab, Apache Spark, Hadoop, Impala

JIUN-TING CHEN

https://jiun-ting.github.io/ https://www.linkedin.com/in/jiun-ting-chen 608-***-**** ********@****.*** EDUCATION

M.S. in Computer Sciences Madison, WI

University of Wisconsin-Madison Sept. 2019 – present

Courses: Operating Systems, Big Data Systems, High Performance Computing, Database Management Systems, Algorithms, Optimization, Mathematical Foundation of Machine Learning, User Interfaces SOFTWARE PROJECTS

Multi-threaded MapReduce Library (C Parallel Programming)

Implemented a MapReduce library that parallelly processed workloads by user-defined mapper/reducer function.

Optimized partitioning algorithm and arranged locks to allow mappers to each put values into different partitions correctly and efficiently under the concurrent data structure. Big Data Systems Benchmark (Python System Evaluation)

Benchmarked and optimized the performance among several industrial platforms/systems such as PyTorch, Spark, Cassandra, and PostgreSQL.

Implemented distributed data model training in PyTorch. Benchmarked the performance of different gradient synchronization methods such as gather /scatter, and all-reduce.

Deployed HDFS and Apache Spark. Implemented PageRank algorithm and optimized its latency by caching RDD. Ensured system robustness by designing an experiment that verified its fault-tolerant protocol.

Conducted experiments to evaluate I/O performance for both Relational and NoSQL databases. Gained the sweet spot between performance and power consumption by tuning different knob settings. Course Enrollment System (React.js User Interface Design)

Designed an enrollment webpage that supported searching, shopping cart, course rating and recommendation while JSON files serve as the backend.

Implemented Boolean search based on inputted values from the sidebars and the keyword filter.

Developed callback mechanisms which enabled the courses to be selected and dropped either as a whole or by sub-session. And Implemented data structures to notice the user if a selected course fails to meet the prerequisite.

Created a rating function for cart items that enabled users to get a recommended list based on their preference. Relational Database Enhancement (C++, SQL Database Management)

Implemented B+ tree functionality in Badger DB, supporting index creation, range query; Managed the pages properly to ensure that they are pinned in the buffer pool only if necessary.

Designed query optimization strategies in SQLite to beat the default execution plans with 30%-90% improvement in average user time for queries with aggregation and subqueries. PROFESSIONAL EXPERIENCE

Data Scientist (Python, SQL Machine Learning for Anomaly Detection) Taoyuan, Taiwan Chunghwa Telecom Laboratories Nov. 2017 – Aug. 2019

Developed an ML-based IoT device monitoring function to filter anomalies from 500,0000 devices among hundreds of groups in hour-scale latency and automatically outputted daily statistical analysis reports.

Reduced the cost to analyze network issues by constructing a data-driven evaluation function with Terabyte- scale Impala database, which helps us evaluate in an automatic and objective manner.

Found optimal locations to set base stations by ML and statistical analyses, including identifying thousands of target railway passengers from approximately 8 million users and crowd estimating. TECHNICAL SKILLS

Languages: C/C++, Python, Java, SQL, MATLAB, Front-end (HTML/CSS/JavaScript/React/React Native)

Tools: Apache Spark, Hadoop, Impala, Docker, AWS, ML packages (NumPy, Pandas, Scikit-Learn, PyTorch)

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