cv

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Basics

Name Leo Han
Label PhD Student
Email lxh4@cornell.edu
Url https://leoxhan.com
Summary Third-year Ph.D. student at Cornell Tech focused on advancing the efficiency and sustainability of data centers across the full computing stack: from physical hardware to resource scheduling and software optimization. Research addresses critical challenges in sustainable cloud computing, including the fair attribution of carbon footprints to individual cloud applications and uncertainty quantification in embodied carbon estimates for computing hardware.

Work

  • 2023.09 - Present
    Graduate Student Researcher
    Cornell Tech
    Conducting research in sustainable cloud computing and carbon footprint attribution.
    • Developed Fair-CO2, a game-theoretic framework for equitable and efficient attribution of data center carbon footprints to individual users, incorporating colocation interference effects and the impact of dynamic demand on hardware provisioning (ISCA'25).
    • Enhanced hardware carbon modeling tools through probabilistic methods that quantify data and model uncertainties in embodied carbon estimates (CarbonClarity, ICCAD'25).
    • Designing optimized agent-to-service interfaces for cloud software services that serve AI agent users, focusing on system-level resource efficiency and performance.
  • 2023.05 - 2023.08
    Undergraduate Student Researcher
    University of Toronto
    FPGA accelerator design for Bayesian inference algorithms.
    • Designed FPGA-based accelerator for residual belief propagation, a Bayesian inference algorithm, leveraging task-level speculative parallelism to achieve scalable performance across many cores.
  • 2021.05 - 2022.08
    GPU Power Delivery Intern (Professional Experience Year)
    Intel
    GPU power management and performance optimization.
    • Led end-to-end design of a tool for precision testing of GPU compliance to PCIe specifications for input voltage.
    • Led enablement of key platform-wide power management and hardware power telemetry features on desktop and datacenter GPUs and accelerators which reduced power excursions by over 95%.
    • Improved gaming performance (frames-per-second) by 40% while enabling stricter compliance to power limits on GPUs by tuning power management control loops.
  • 2019.05 - 2019.08
    Undergraduate Student Researcher
    National University of Singapore
    Energy harvesting research with dielectric elastomer generators.
    • Optimized design of dielectric elastomer generator (DEG) to increase energy harvesting conversion efficiency by 14 times while decreasing prototype size by 4 times.
    • Modularized DEG prototype to quickly and easily change between different biasing voltages and different dielectric elastomer capacitances, significantly reducing time required for experimental trials.

Volunteer

  • 2019.11 - 2022.08

    Toronto, ON

    Team Leader
    University of Toronto Solar Racing Design Team
    Led a team of over 40 dedicated undergraduate and graduate students to design and build a solar-powered race vehicle for the 2023 World Solar Challenge, a 3000 km endurance race in Australia.
    • Coordinated technical and business teams to meet project milestones and resource needs.
    • Procured over $190,000 in funding and sponsorships through grants and industry sponsorships.
    • Co-led design and manufacturing efforts with chief engineer and sub-system leads.
  • 2018.09 - 2019.10

    Toronto, ON

    Electrical and Fabrication Team Member
    University of Toronto Solar Racing Design Team
    Team member contributing to electrical systems and structural design for solar race vehicle.
    • Assembled and validated solar race vehicle's electrical systems for the 2019 World Solar Challenge.
    • Designed, simulated, and built major composite structural components for solar race car.

Education

  • 2023.09 - Present

    New York, NY

    Ph.D.
    Cornell Tech
    Electrical and Computer Engineering
    • Machine Learning Systems
    • Algorithmic Game Theory
    • FPGA Architecture
    • ASIC Design
  • 2018.09 - 2023.04

    Toronto, ON

    B.A.Sc.
    University of Toronto
    Engineering Science
    • Computer Architecture
    • Operating Systems
    • Computer Security
    • Electronic Devices

Awards

Publications

Skills

Programming Languages
C
C++
Python
Go
Cloud Computing
Docker
gRPC
Kubernetes
Machine Learning Systems
PyTorch
Nvidia Nsight Systems/Compute
Digital Design
Verilog
SystemVerilog
Xilinx Vitis/Vivado
Laboratory
Function generators
Power supplies
Oscilloscopes
Micro-soldering

Languages

English
Native speaker
Chinese (Mandarin)
Native speaker

Interests

Research Interests
Sustainable Cloud Computing
Data Center Efficiency
Carbon Footprint Attribution
Hardware-Software Co-optimization
Machine Learning Systems
AI Agent Workloads
Resource Scheduling

Projects

  • 2023.09 - 2025.01
    Fair-CO2: Carbon Attribution Framework
    Developed a game-theoretic framework for equitable and efficient attribution of data center carbon footprints to individual users, incorporating colocation interference effects and dynamic demand impacts.
    • Published at ISCA'25 (23% acceptance rate)
    • Game-theoretic approach to carbon accounting
    • Addresses fairness in cloud resource attribution
  • 2023.09 - 2025.01
    CarbonClarity: Embodied Carbon Uncertainty Quantification
    Enhanced hardware carbon modeling tools through probabilistic methods that quantify data and model uncertainties in embodied carbon estimates.
    • Published at ICCAD'25 (25% acceptance rate)
    • Probabilistic carbon modeling
    • Uncertainty quantification framework
  • 2024.01 - Present
    AI Agent-Service Interface Optimization
    Designing optimized agent-to-service interfaces for cloud software services that serve AI agent users, focusing on system-level resource efficiency and performance.
    • Cross-stack co-optimization
    • Resource efficiency for AI workloads
    • Cloud service optimization
  • 2023.05 - 2023.08
    FPGA-based Bayesian Inference Accelerator
    Designed FPGA-based accelerator for residual belief propagation, leveraging task-level speculative parallelism to achieve scalable performance across many cores.
    • FPGA hardware acceleration
    • Speculative parallelism
    • Bayesian inference optimization