Tianshu Huang

Tianshu Huang

I am a PhD student co-advised by Anthony Rowe and Carlee Joe-Wong at the Department of Electrical and Computer Engineering at Carnegie Mellon University. I'm interested in Learning for Systems: applying machine learning to (cyber-physical) systems in ways that leverage both data-driven learning techniques and a theoretical and intuitive understanding of system properties.

Previously, I obtained my B.S. in Electrical Engineering at the University of Texas at Austin in May 2021. While at UT Austin, I worked on Learning to Optimize research with Atlas Wang.

Please contact me at tianshu2@andrew.cmu.edu. You can find my GitHub page here; I also have a photography site.

Research

Machine Learning for Radar
Learning for Distributed Systems

Radars are an ideal complement to cameras for applications such as autonomous driving: both are inexpensive, solid-state sensors, with cameras boasting fine angular resolution and radars providing depth resolution and robustness to adverse conditions. Unfortunately, unlike visual images or Lidar points, radar data are harder to interpret, and lack a large body of existing research. In this project, my goal is to develop machine learning-based methods to interpret radar data both spatially and semantically, potentially replacing Lidar as the primary means of 3D perception in robotics and beyond.

The ability to execute programs in heterogeneous distributed environments while maintaining security and performance isolation is of critical importance as edge and cloud applications become increasingly entangled. The SilverLine framework is our solution, where we propose using WebAssembly modules as an edge-friendly lighter-weight alternative to virtual machines or containers for maintaining isolation while providing portability.

In this research project, my goal is to explore learning-enabled building blocks for managing distributed systems. Key concerns include performance analysis and runtime prediction, as well as debugging and anomaly detection. My approach emphasizes going beyond black-box approaches (in both a statistical and a systems sense) using techniques like instrumentation injection and statistical machine learning approaches.

Publications

Unveiling Heisenbugs with Diversified Execution
Arjun Ramesh, Tianshu Huang, Jaspreet Singh Riar, Ben Titzer, Anthony Rowe
OOPSLA 2025
Heisenbugs - bugs which change their behavior under observation - are among the toughest challenges when debugging programs. We propose to catch these bugs using stochastic instrumentation, and demonstrate the surprising effectiveness of debugging across a diverse set of platforms.
Github Instrumentation Framework
Uncertainty and Interference-aware Runtime Prediction for Edge Computing with Conformal Matrix Completion
Tianshu Huang, Arjun Ramesh, Emily Ruppel, Nuno Pereira, Anthony Rowe, Carlee Joe-Wong
MLSys 2025
Performance prediction in heterogeneous systems such as edge computing is best formulated as matrix completion, which can be extended to handle complex, edge-specific concerns such as interference and uncertainty quantification.
Github Zenodo Slides
Silverline: Lightweight Virtualization and Orchestration of Distributed Systems
Arjun Ramesh, Tianshu Huang, Emily Ruppel, Dakshina Dasari, Behnaz Pourmohseni, Fedor Smirnov, Marco Giani, Paolo Pazzaglia, Charles Shelton, Nuno Pereira, Arne Hamann, Dirk Ziegenbein, Anthony Rowe
RTAS 2025
We develop Silverline: a framework for programming heterogeneous dynamic distributed systems. Using WebAssembly as a lightweight virtualization framework, we design silverline to be fault-tolerant and real-time, and show it in use on industrial-grade systems.
Empowering WebAssembly with Thin Kernel Interfaces
Arjun Ramesh, Tianshu Huang, Ben Titzer, Anthony Rowe
EuroSys 2025
Webassembly (Wasm) adoption for new domains is often hindered by the lack of standard system interfaces. We propose directly exposing OS userspace syscalls as a compromise.
Github Rust Target
DART: Implicit Doppler Tomography for Radar Novel View Synthesis
Tianshu Huang*, John Miller*, Akarsh Prabhakara, Tao Jin, Tarana Laroia, Zico Kolter, Anthony Rowe
CVPR 2024, Oral Presentation — Top 0.78% (90) of all submissions
We propose DART — Doppler Aided Radar Tomography, a Neural Radiance Field-inspired approach to radar novel view synthesis using implicit neural inverse imaging.
Paper Implementation Data Collection Dataset
Optimizer Amalgamation
Tianshu Huang, Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang
ICLR 2022
While meta-training optimizers from scratch is difficult, much attention has been directed towards developing a diverse body of analytical optimizers. Can we use these optimizers to meta-train a stronger optimizer?
Poster Github

Awards

2023-2024 Academic Year | Prabhu and Poonam Goel Graduate Fellowship
Awarded to one student in the ECE department each academic year.

Spring 2023 | ECE Department Recognition Award for Exemplary Qualifying Exam Performance
Recognized by CMU ECE faculty for exemplary Ph.D. qualifying examination performance. This distinction was awarded by faculty vote to select students within the top 10% of Ph.D. student examinees.
This award is typically given to no more than one student each semester.

Fall 2021 - Spring 2024 | ARCS Pittsburgh Chapter Scholar
$15,000 award (over 3 years) for one student nominated by each participating department.

Fall 2017 - Spring 2021 | Virginia & Ernest Cockrell, Jr. Scholarship in Engineering
$48,000 award (over 4 years) for undergraduate students admitted to the ECE Honors program.

Invited Talks

Students

Teaching

Guest Lecture: Machine Learning + Edge
CMU 18-649 Distributed Embedded Systems, April 2024

TA: CMU 18-661 Introduction to Machine Learning, Spring 2024

Guest Lecture: Federated Learning
Duke ECE/COMPSCI 654 Edge Computing, February 2023

Guest Lecture: Machine Learning + Edge
CMU 18-649 Distributed Embedded Systems, November 2022

TA: CMU 18-661 Introduction to Machine Learning, Spring 2022

TA: UT Austin EE 351k Probability, Statistics and Random Processes, Fall 2020 - Spring 2021

Casual Presentations

Service