I’m a Master’s student in Computer Science at Columbia University, focused on building reliable, scalable software systems across backend infrastructure, systems programming, and applied machine learning.

My background spans production engineering in safety-critical environments, large-scale data pipelines, and academic systems foundations. I enjoy working on problems where correctness, performance, and real-world constraints actually matter.

M.S. in Computer Science (Machine Learning Track)
Expected Dec 2026

Relevant coursework:

• Interface Design
• Advanced Software Engineering
• Machine Learning
• Natural Language Processing
• Algorithms & Data Structures

B.Eng. in Mechatronics Engineering
Graduated 2022

Software Engineer (Rotational Program)

Completed GM’s TRACK program, rotating across multiple production teams in vehicle software and cloud infrastructure:

• DevQA / Software Test Specialist
  Worked on validation pipelines and test frameworks for vehicle software releases.
  Built a diagnostics automation framework that reduced manual testing effort and improved regression coverage across deployments.

• C++ Engineer (Controls & Diagnostics)
  Developed and debugged embedded diagnostics and middleware components in modern C++.
  Focused on reliability, performance, and observability in Linux/QNX environments supporting safety-critical vehicle systems.

• Data Analytics Engineer
  Designed and implemented a Go-based ETL pipeline on Microsoft Azure to process large volumes of vehicle telemetry data.
  Built Grafana dashboards backed by structured metrics to improve system observability, anomaly detection, and root-cause analysis at scale.

• Virtualization & Automation (Final Placement)
  Worked on cloud-based tooling and CI/CD automation to support large-scale vehicle software testing and deployment workflows.

This experience shaped how I approach production systems, ownership, and engineering tradeoffs under real constraints.

Embedded / Control Systems Co-op

Completed a year-long co-op during my undergraduate studies, working on embedded control and diagnostics systems.
Gained early exposure to low-level debugging, hardware–software integration, and production engineering workflows.

McMaster University

Supported course instruction and labs for an Operating Systems course.
Helped students understand core systems concepts including processes, scheduling, memory management, and concurrency, reinforcing my own foundations in low-level systems design.

Advised by Prof. Henning Schulzrinne

Starting Spring 2026, I will be working on a research project focused on:

• Detecting fraudulent calls, texts, and emails
• Modeling interaction history and sender reputation
• Exploring interactive and LLM-assisted trust assessment

The work bridges NLP, machine learning, and deployable system design, with an emphasis on practical protection mechanisms rather than purely theoretical models.

• Backend & Distributed Systems
• Systems Programming (C/C++, Go)
• Cloud Infrastructure & Observability
• Machine Learning & NLP in production contexts
• Performance analysis, debugging, and reliability

Languages & tools I frequently use:

• C/C++, Go, Python, Java
• Linux, Git, Docker
• SQL / PostgreSQL
• Grafana, CI/CD pipelines

Pinned repositories highlight:

• Backend service design and APIs
• Full-stack coursework projects
• Systems-oriented implementations

I prefer fewer projects with real depth, clear design decisions, and solid documentation.

• GitHub: @rogerwangdev
• LinkedIn: https://www.linkedin.com/in/luojiewang/
• Email: lw3240@columbia.edu

Always happy to talk about systems engineering, ML in real-world settings, or software design tradeoffs.