resume

General Information

Full Name Albert (Geyang) Xu
Email albertxu1010@gmail.com
Phone 858-568-6771
Website albertgy9910.github.io
Location Los Angeles, CA

Research Interests

  • Data-centric robustness of ML pipelines
  • ML/MLOps reliability & monitoring
  • Responsible & transparent data management
  • Streaming & distributed data systems
  • Data provenance & explanations
  • Governance of ML systems

Education

  • 2026.09 - Expected
    Ph.D. in Computer Science (Incoming)
    University of California, Riverside
    • Riverside, CA
  • 2022.09 - 2024.03
    Master of Science in Computer Science
    University of California, San Diego
    • GPA: 3.88/4.0
    • Courses: Operating Systems, Networks, Programming Languages, ML, Probabilistic Modeling, Data Ethics
  • 2018.09 - 2022.06
    Bachelor of Science in Computer Science
    University of Liverpool, UK
    • GPA: 3.93/4.0 (top 3%)

Experience

  • 2025.10 - 2026.04
    Software Engineer
    Fuyao Glass America Inc.
    • Built a YOLOv8 computer-vision pipeline for inline automotive-glass inspection, detecting scratch, chip, edge, and coating defects on line-camera frames.
    • Integrated detections into Python/SQL ETL with coating/tempering/inspection data; shipped role-based dashboards for OEE, FPY, scrap, and changeover.
    • Productionized CV inference + ETL with cron, alerts, and access control — 99.3% job success and 2.4 hrs/week less downtime; supported A/B and DOE setpoint studies in Jupyter.
  • 2024.10 - 2025.10
    Software Engineer
    4Pexonic Inc.
    • Developed an academic impact analysis platform tracking citation patterns across tens of thousands of records using Node.js, MongoDB, and Next.js with Docker, serving university researchers and policy institutions.
    • Built two-tier caching (in-process LRU + Redis), cutting median and p95 latency by 40%.
    • Integrated gRPC microservices; implemented bcrypt/session auth and Nginx TLS.
  • 2023.10 - 2024.10
    Research Engineer
    UCSD Halıcıoğlu Data Science Institute
    • Built modular Python Injector pipeline (generate → sample → inject → eval) with beam-search + Optuna/TPE, cutting downstream AUC by >0.25 vs. random attacks.
    • Co-authored SAVAGE (PVLDB'25), designing corruption dependency graphs and bi-level black-box search to model mechanism-aware missingness, selection-bias, and outlier patterns for pipeline-level stress-testing of ML systems.
    • Ran Inject → Clean → Retrain benchmarks across missing-value, selection-bias, and outlier scenarios to evaluate state-of-the-art cleaning, debiasing, and UQ pipelines and expose their data-centric robustness gaps.

Publications

Skills

  • Programming Languages
    • Python, Java, Go, SQL, C/C++, JavaScript, R
  • Frameworks and Tools
    • Docker, Git, gRPC, Node.js, Next.js, Django, React, MongoDB, Optuna, Jupyter, YOLOv8, AWS