Chia-Cheng (Jerry) Yen

Chia-Cheng (Jerry) Yen

Teaching Assistant

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location of Chia-Cheng (Jerry) YenDublin, California, United States

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  • Timeline

  • About me

    Principal Member Of Technical Staff at AT&T

  • Education

    • Fu Jen Catholic University

      2008 - 2012
      Bachelor of Science - BS Computer Science and Information Engineering GPA: 3.96/4.0 (Major GPA: 4.0/4.0)

      1. Best Ranked 1st, Average Ranked 2nd in class2. Academic Excellence Award (7 times)• Awarded to students with top 5% GPA for that semester• Provided scholarships to students with top 5% GPA for that semester3. Second Best Project Award• Ranked 2nd out of 27 teams

    • University of California, Davis

      2017 - 2022
      Doctor of Philosophy - PhD Computer Science
    • National Tsing Hua University

      2012 - 2014
      Master of Science - MS Computer Science GPA: 4.27/4.3 (Major GPA: 4.3/4.3)

      Activities and Societies: Attended IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, International Workshop •Research topics including Networks, Clustering, Stereo Matching, and Data Compression

  • Experience

    • Fu Jen Catholic University

      Sept 2011 - Jan 2012
      Teaching Assistant

      • Provided class tutoring for Assembly Language• Assisted students with programming and examining

    • National Tsing Hua University

      Jul 2012 - Aug 2014
      Research Assistant

      1. Distributed Delivery of Videos over Ultra-dense Networks • Funded by Ministry of Science and Technology, Taiwan (R.O.C.) • Deploy distributed storage using LT codes on cloud platform for popular videos • Evaluate distributed delivery techniques for hot videos over ultra-dense wireless environments2. Clustering Algorithm for Gene Expression Data • Propose an unsupervised framework for classifying time-series data • Achieve higher classification accuracy than other methods3. Stereo Matching •Funded by Novatek Corporation •Estimate disparity by left and right images; obtain disparity information to construct depth map4. Data Compression in WSNs • Propose tree-structured linear approximation for compression data • Compress data based on available transmission rate without high distortion • Achieve better performance in compression for heterogeneous sensor data Show less

    • Institute of Information Science, Academia Sinica

      Apr 2017 - Aug 2017
      Research Assistant

      • Research topics including wireless networks and machine learning

    • University of California, Davis

      Apr 2018 - Dec 2021

      1. Delay-based Scheduling for Maximizing Traffic Throughput and Minimizing Latency at Multiple Intersections• Apply the network model to schedule traffic signal controls for multiple intersections• Consider different priorities of pedestrians, vehicles, or transportation2. Deep Reinforcement Learning (DRL) for Multi-intersection Control• Increase learning ability of DRL-agents by learnable image features • Enhance performance of DRL-agents by the proposed reward function• Achieve 3x speed-up during training by the proposed 2DSARSA3. Security Vulnerability on Backpressure-based TSC Schemes• Discover potential threats to modern TSC systems• Avoid 100% of security attacks by the proposed algorithms Show less

      • Graduate Student Researcher

        Sept 2017 - Dec 2021
      • Graduate Teaching Assistant

        Apr 2018 - Jun 2021
    • Ericsson

      Jun 2021 - Sept 2021
      Data Scientist Intern

      Global Artificial Intelligence Accelerator (GAIA), Team NEUTRON 2 (Manager: Dr. Wenting Sun)Develop a novel graph neural network (GNN-based) root cause analysis (RCA) algorithm that is applicable to 5G wireless environments. The goal is to predict potential root cause nodes and a chain of failures led by the root cause nodes. Higher prediction accuracy is achieved in our simulation if more nodes (features) are considered.

    • AT&T Labs, Inc.

      Sept 2021 - now

      1. Home Network Security• Develop a deep learning model for detecting potential threats and discovering vulnerabilities in home network.2. Remote Worker Security• Study the impact of MAC address spoofing on the identification of real users.• Propose device fingerprint via DPI data to secure network security when workplace is extended to home.3. RG Latency Prediction• Exploit DNN and multi-dimension features to boost prediction performance on RG latency.• Achieve 84% accuracy on predicted latency.4. 5G NR• Analyze the Key Performance Indicators (KPIs).• Provide insights into business development, marketing communications, innovative product and service. Show less

      • Principal Member of Technical Staff

        Jun 2024 - now
      • Senior Member of Technical Staff

        Sept 2021 - Jun 2024
  • Licenses & Certifications

    • TOEFL

      ETS Global B.V.