cv

Curriculum Vitae of Linji (Joey) Wang

Basics

Name Linji Wang
Label Ph.D. Student in Computer Science
Email joewwang@outlook.com
Phone (412) 888-6071
Url https://linjiw.github.io/
Summary Ph.D. student in Computer Science (AI and Robotics) focused on curriculum learning and reinforcement learning for robotics; publications at IROS 2025 and IEEE RA-L 2025

Work

  • 2025.05 - 2025.08

    Bellevue, WA

    Software Development Engineer Intern - RDS Proxy Team
    Amazon Web Services (AWS)
    Developed performance testing and visualization tools for RDS Proxy deployments
    • Developed IPEBench Data Visualization Platform with Streamlit, creating 8 interactive visualization types that reduced engineers' performance regression analysis time from 8 hours to 15 minutes
    • Built production-grade Regression Testing Framework (10,000+ lines) with statistical analysis (Welch's t-test, power analysis, Bonferroni correction) achieving 99% confidence in detecting performance regressions
    • Implemented adaptive sampling system using Thompson Sampling and Bayesian optimization for convergence detection, improving test reliability from 47% to 90% and eliminating false positives that previously blocked deployments
    • Integrated AWS CloudWatch metrics with automated dashboard generation, enabling real-time performance monitoring and data-driven decision making for RDS proxy deployments across multiple regions
  • 2023.08 - Present
    Graduate Research Assistant
    RobotiXX Lab, George Mason University
    Curriculum learning research for efficient reinforcement learning in robotic manipulation
    • Developed Grounded Adaptive Curriculum Learning (GACL), a novel framework integrating real-world data with adaptive simulated task generation for robotic manipulation
    • Achieved 24.58% higher success rate and 50% improved sample efficiency compared to baseline methods
    • Three papers accepted at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2025
  • 2022.01 - 2023.05
    Research Assistant
    Computational Engineering and Robotics Lab, CMU
    3D AR Scene Inpainting via Deep Learning
    • Developed end-to-end deep learning pipeline achieving 92% accuracy in scene completion
    • Implemented GAN model improving texture realism by 35% over baseline
    • Applied RANSAC and DBSCAN algorithms reducing processing time by 40%

Education

  • 2023.09 - Present

    Fairfax, VA

    Ph.D.
    George Mason University
    Computer Science - AI and Robotics
    • Advanced Machine Learning
    • Deep Learning
    • Reinforcement Learning
    • Computer Vision
  • 2021.09 - 2023.05

    Pittsburgh, PA

    M.Sc.
    Carnegie Mellon University
    Mechanical Engineering
    • Machine Learning
    • Deep Learning
    • Computer Vision
    • Deep Reinforcement Learning & Control
  • 2016.09 - 2021.05

    Cincinnati, OH

    B.S.
    University of Cincinnati
    Mechanical Engineering

Skills

Programming Languages
Python
C++
SQL
Bash
CUDA
ML/DL Frameworks
PyTorch
TensorFlow
JAX
Streamlit
Plotly
OpenAI Gym
AWS & Cloud
RDS
CloudWatch
EC2
Lambda
S3
Docker
Kubernetes
Robotics & Simulation
ROS
IsaacGym
MuJoCo
OpenAI Gym
Reinforcement Learning
PPO
SAC
Curriculum Learning
Reward Shaping
Statistical Analysis
Hypothesis Testing
Power Analysis
A/B Testing
Regression Analysis

Languages

English
Fluent
Chinese
Native

Interests

Artificial Intelligence
Reinforcement Learning
Generative AI
Computer Vision
Robotics
Curriculum Learning
Sim-to-Real Transfer
Navigation

Projects

  • 2023.05 - 2023.07
    Flexible Long-Term Mortality Prediction
    Developed survival analysis model integrating CNN with Cox Proportional Hazards model
    • Integrated MobileNet v2 with Cox Proportional Hazards model
    • Implemented attention mechanisms for critical area focus
    • Achieved 15% improvement over traditional methods

Publications