I’m a final-year electrical engineering Ph.D. student at Stanford University interested in systems and networking. Earlier in my PhD, I worked with Phil Levis and Chris Ré on building hardware component knowledge bases using training data generation and multi-task learning. Now, I work with Phil Levis and Keith Winstein exploring ultra-low-latency, gaze-contingent video compression.

I will be joining Google full-time as a software engineer after graduation.


2015 – 2021Ph.D. in Electrical Engineering
Stanford University
2015 – 2017M.S. in Electrical Engineering
Stanford University
2010 – 2015B.S. in Computer Engineering
Brigham Young University, Summa Cum Laude


Industry Experience

2020-06 – 2020-09Software Engineering Intern, Google
  • Added support for TCP tx zerocopy (tx0cp) using io_uring in the Linux kernel.
  • Profiled and optimized benchmarks to demonstrate an 18% improvement in CPU efficiency for tx0cp via io_uring.
2019-06 – 2019-09Research Intern, Google
  • Explored BBRv2 for many-to-one data center traffic, reducing latency and retransmit rates by 30% and 80%, respectively.
  • Open sourced Transperf, a transport protocol performance tool for testing TCP over emulated network scenarios.
2017-06 – 2017-09Software Engineering Intern, NVIDIA
  • Helped develop a new system-level Windows driver for gaming laptops.
  • Designed and implemented secure APIs in kernel-space C code.
2015-04 – 2015-06Software Engineering Intern, Novi Security
  • Prototyped embedded software architectures to analyze and improve testability.
  • Built infrastructure for continuous integration and test-driven development.

Research Experience

2015-09 – PresentPh.D. Research Assistant, Stanford University (Advisors: Phil Levis and Keith Winstein)
Area: Systems and Networking
  • Current: Ultra low latency foveated video compression.
  • Past: Generating hardware component knowledge bases with training data generation and multitask learning (w/ Chris Ré).
2014-04 – 2015-06Undergraduate Research Assistant, Brigham Young University (Advisor: Mike Wirthlin)
Area: Embedded Systems, FPGA Reliability, and Fault Injection
  • Implemented VHDL components used in FPGA reliability experiments.
  • Created standalone JTAG fault injection tool for radiation testing in C/C++.

Teaching Experience

W2019Introduction to Computer Networks (CS 144), Graduate CA
Stanford University
W2016Program Analysis and Optimizations (CS 243), Graduate Grader
Stanford University
W2014Data Structures and Algorithms (CS 235), Undergraduate TA
Brigham Young University


Select publications, in reverse chronological order.

Peer-Reviewed Papers

  1. Creating Hardware Component Knowledge Bases with Training Data Generation and Multi-task Learning, ACM TECS 2020
    L. Hsiao, S. Wu, N. Chiang, C. Ré, and P. Levis
    [paper] [code] [data]
  2. Automating the Generation of Hardware Component Knowledge Bases, LCTES 2019
    L. Hsiao, S. Wu, N. Chiang, C. Ré, and P. Levis
    [paper] [slides] [poster] [code] [data]
  3. Fonduer: Knowledge Base Construction from Richly Formatted Data, SIGMOD 2018
    S. Wu, L. Hsiao, X. Cheng, B. Hancock, T. Rekatsinas, P. Levis, and C. Ré
    [paper] [code]
  4. Smart Contracts for Machine-to-Machine Communication: Possibilities and Limitations, IOTAIS 2018
    Y. Hanada, L. Hsiao, and P. Levis
  5. Estimating Soft Processor Soft Error Sensitivity through Fault Injection, FCCM 2015
    N. Harward, M. Gardiner, L. Hsiao, M. Wirthlin
  6. A Fault Injection System for Measuring Soft Processor Design Sensitivity on Virtex-5 FPGAs, FASA 2014
    N. Harward, M. Gardiner, L. Hsiao, M. Wirthlin


  1. The Price of Free Illegal Live Streaming Services, arXiv 2019
    H. Ayers and L. Hsiao
  2. TCPTuner: Congestion Control Your Way, arXiv 2016
    K. Miller and L. Hsiao
    [paper] [code]

Past Projects