teaching

I enjoy teaching core systems topics and connecting them to modern AI and cloud workloads.
My teaching so far has focused on cloud computing, distributed systems, and AI systems infrastructure.

Teaching interests

• Distributed systems
• Cloud computing, cloud-native microservices, and serverless platforms
• AI systems and infrastructure for large-scale LLM / MoE workloads
• Computer architecture and GPU-centric systems from a software perspective

Course and lecture experience

University of Virginia

Guest lectures on Cloud-Native Computing and Microservices
Department of Computer Science, University of Virginia, 2025

• Designed and delivered lectures on:
  • Cloud-native architecture and microservices in production data centers
  • Workload characteristics and QoS-aware resource management
  • Case studies based on Alibaba microservice traces and real systems
• Prepared lecture slides and an in-class quiz to help students reason about microservice dependency graphs, tail latency, and resource management policies.
• Emphasized how ideas from microservices extend to AI/LLM serving and serverless infrastructures.

Mentoring

I have also been actively involved in mentoring students and collaborators:

• Mentored research interns and junior students on:
  • Resource management for large-scale microservices
    
  • Serverless for diffusion model
• Guided mentees through the full research cycle: problem formulation, system design and implementation, experimentation, and paper writing.

If you are a student interested in AI infrastructure, systems for ML, feel free to contact me about potential research projects or independent study opportunities.