Current Research

• Data-Driven Edge Orchestration for Distributed Systems
  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 build a statistically rigorous learning-based edge orchestration framework from the ground up integrating real-time instrumentation in our system.
  

• Applications for Neural Radiance Fields
  Since its introduction in 2020, Neural Radiance Fields (NeRFs) have garnered increasing attention. Recent advances have moved Neural Radiance Fields closer and closer to true competition with traditional techniques in computer graphics.
  Applications I am interested in include problems outside the traditional NeRF "canon" (few-shot, relighting, quality, etc) such as real time reality capture and non-graphics problems, especially with assistance from non-visual sensors. In particular, I take an alternative view of NeRF research: instead of trying to best reconstruct a scene given sensor inputs, what kind of sensor inputs are necessary to reach a given target reconstruction quality, given available computational resources?
  

Publications

• Bytecode Runtime Prediction by Matrix Factorization (In Review)
  Tianshu Huang, Arjun Ramesh, Jaspreet Singh Riar, Emily Ruppel, Nuno Pereira, Anthony Rowe, Carlee Joe-Wong
  
• Optimizer Amalgamation (ICLR 2022) [Poster]
  Tianshu Huang , Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang
  

Invited Talks

• Giving the Cloud an Edge with WebAssembly, WebAssembly Research Day 2022 (with Arjun Ramesh)

Students

• Quentin Oschatz (Spring 2023-) — Towards Digital Twins by Observation
• Hanke Chen, Undergraduate (Summer 2022-) — Real Time Neural Radiance Fields
• Kriti Kukreja, Masters (Fall 2022) — ML for ARENA Network Flow Understanding
• Shreyashri Biswas, Masters (Fall 2022) — RL for Distributed Resource Management
• Rahul Jha, Masters (Summer 2022 - Fall 2022) — SilverLine Demo and Power Measurement
• Jeremy (Zhanfan) Yu, Masters (Summer 2022) — SilverLine (compilation server and file store; fault tolerance)
• Rituraj Singh, Masters (Spring 2022) — Neural Network Training in WebAssembly
• Anish Singhani, Undergraduate (Spring 2022) — LIDAR-guided depth estimation

Teaching

• Guest Lecture: (Duke) ECE/COMPSCI 654 Edge Computing (Federated Learning)
• Guest Lecture: (CMU) 18-649 Distributed Embedded Systems (Machine Learning + Edge)
• TA: (CMU) 18-661 Introduction to Machine Learning for Engineers, Spring 2022
  • Lecture: Better and Faster Python
  • Lecture: Pytorch (and how we got here)
• TA: (UT Austin) EE 351k Probability, Statistics and Random Processes, Fall 2020 - Spring 2021

Casual Presentations

• The NeRF is Dead ... Long Live the NeRF.
• Topics on the Edge (of Federated Learning)

Awards

• (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.
• (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