Hung-Yueh Chiang
江泓樂

About me

Hung-Yueh is a Senior Deep Learning Software Engineer on the Inference and Model Optimization team at NVIDIA. Prior to joining NVIDIA, he obtained his Ph.D. degree at The University of Texas at Austin (UT), advised by Prof. Diana Marculescu. He completed his M.S. degree from National Taiwan University (NTU) and a B.S. degree at National Yang Ming Chiao Tung University.

I’m interested in building efficient agentic AI systems for everyone on every device. My research interests include:

  • Quantization and compression: Weight and KV-cache compression with new floating types for faster inference
  • Efficient agentic system design: Lightweight architectures and scheduling strategies for multi-step reasoning agents
  • On-device agentic systems: Deploying capable AI agents on resource-constrained edge devices

News

Apr 6, 2026 :tada: New Position
I will join NVIDIA as a Senior Deep Learning Software Engineer in Inference and Model Optimization.
Feb 20, 2026 :mortar_board: Ph.D. Thesis Defense
I successfully defended my Ph.D. thesis: Model Adaptation and Compression for Edge Intelligence
Jan 26, 2026 :tada: Paper Accepted
Our paper: UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs has been accepted to ICLR 2026.
Dec 11, 2025 :computer: Tool Released
We release an open-sourced profiling tool: ELANA: A Simple Energy and Latency Analyzer for LLMs, please checkout our GitHub and the technical report
Dec 3, 2025 :page_with_curl: Paper Released
Our newest paper: UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs is released on Arxiv.

Work experience


NVIDIA - Senior Deep Learning Software Engineer
Apr. 2026 - Present
Inference and model optimization
Eigen AI - Research Intern
Dec. 2025 - Feb. 2026
NVFP4 quantization and serving for large language models
Rivian - Software Engineering Intern (Deep Learning)
May 2023 - Aug. 2023
Neural architecture search for object detection
Amazon - Research Scientist Intern
May 2022 - Nov. 2022
Image synthesis and generation for shoe virtual try-on
XYZ Robotics - Deep Learning Engineer
Jun. 2019 - May 2021
Image segmentation and object detection for logistics robots

Education


The University of Texas at Austin
Sep. 2021 - Feb. 2026
Ph.D. in electrical and computer engineering
National Taiwan University
Sep. 2016 - Sep. 2018
M.S. in computer science
ETH Zürich
Jan. 2015 - Sep. 2015
Exchange student
National Yang Ming Chiao Tung University
(National Chiao Tung University)
Sep. 2011 - Sep. 2015
B.S. in computer science

Selected publications

* Equal contribution

  1. UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs
    In International Conference on Learning Representations (ICLR) 2026
  2. Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models
    In International Conference on Machine Learning (ICML) 2025
  3. Quamba: A Post-Training Quantization Recipe for Selective State Space Models
    In International Conference on Learning Representations (ICLR) 2025
  4. Efficient Low-rank Backpropagation for Vision Transformer Adaptation
    Yuedong YangHung-Yueh Chiang, Guihong Li, Diana Marculescu, and Radu Marculescu
    In Advances in Neural Information Processing Systems (NeurIPS) 2023
  5. Conference Oral paper AAAI
    MobileTL: On-device Transfer Learning with Inverted Residual Blocks
    Hung-Yueh ChiangNatalia FrumkinFeng Liang, and Diana Marculescu
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 2023
  6. Learning from 2D: Pixel-to-Point Knowledge Transfer for 3D Pretraining
    Yueh-Cheng Liu, Yu-Kai Huang, Hung-Yueh Chiang, Hung-Ting Su, Zhe-Yu Liu, Chin-Tang Chen, Ching-Yu Tseng, and Winston H Hsu
    arXiv preprint arXiv:2104.04687 2021
  7. A unified point-based framework for 3d segmentation
    Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, and Winston H Hsu
    In International Conference on 3D Vision (3DV) 2019

Academic service

Conference Reviewer: NeurIPS (2024, 2025), ICLR (2025, 2026), ICML (2025, 2026), AAAI 2026

Open-source contributions

Pro bono office hours

I've committed to set aside 1 hour every week to provide mentorship and/or guidance for whoever is in need. If you find my research, work, oversea and/or life experience useful, welcoming to fill in this form. For feedback, questions, and/or random messages, please use this link.

Miscellaneous

  • I like playing table tennis and taking pictures of new visiting places (Instagram).
  • I served in marine corp from 2015-2016 as the mandatory military service in Taiwan.
  • I was diagnosed with thyroid cancer in 2016 and had a total thyroidectomy. Fortunately, I am fine now.

CV of failures

Life can be tough. We fail all the time, but failures pave the way to success. I am working on my CV of failures. Stay tuned...