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

Current Research Radar Distributed Systems
Past Research Learning to Optimize
(CVPR 2024) DART: Implicit Doppler Tomography for Radar Novel View Synthesis
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
(ICLR 2022) Optimizer Amalgamation
Tianshu Huang, Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang
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
(In Review) WebAssembly Debugging Paper
Arjun Ramesh, Tianshu Huang, Jaspreet Singh Riar, Ben Titzer, Anthony Rowe
(In Review) Stop Hiding The Sharp Knives: The WebAssembly Linux Interface
(In Review) Program Runtime Prediction Paper
Tianshu Huang, Arjun Ramesh, Jaspreet Singh Riar, Emily Ruppel, Nuno Pereira, Anthony Rowe, Carlee Joe-Wong

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