Hung-Yueh Chiang 江泓樂

[Curriculum Vitae] [Résumé]
Ph.D. Candidate
Affiliations: EnyAC
Department of Electrical and Computer Engineering
The University of Texas at Austin

About me

Hung-Yueh Chiang is a third-year Ph.D. student in Energy-Aware Computing Group (EnyAC) at The University of Texas at Austin (UT) under Prof. Diana Marculescu’s supervision. Before joining EnyAC, Hung-Yueh was a deep learning engineer at XYZ Robotics, a start-up company in Shanghai. From 2016 to 2018, Hung-Yueh worked with Prof. Winston Hsu on 3D point cloud learning and received his M.S. degree from National Taiwan University (NTU), Taipei, Taiwan in 2018. He earned his B.S. degree from National Yang Ming Chiao Tung University (NYCU, formerly National Chiao Tung University), Hsinchu, Taiwan in 2015. In the last semester at NYCU, he had an exchange program to ETH Zürich.


Oct 11, 2023 :tada: Paper Accepted
Our paper: Efficient Low-rank Backpropagation for Vision Transformer Adaptation has been accepted in NeurIPS 2023.
Apr 12, 2023 :briefcase: Internship
I will join Rivian as a software engineering intern in 2023 summer.
Apr 8, 2023 :page_with_curl: Paper Update
We are unable to release the implementation because of patent filing for MobileTL. Welcome to email me if you have any questions about reproducing the experiments.
Jan 10, 2023 :page_with_curl: Paper Update
Our work, MobileTL, is accepted as an oral paper in AAAI 2023.
Dec 8, 2022 :page_with_curl: Paper Released
Our AAAI 2023 paper: MobileTL: On-device Transfer Learning with Inverted Residual Blocks is released on Arxiv.


The University of Texas at Austin
Sep. 2021 - Current
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

Work experience

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

Selected publications

  1. 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
  2. Conference Oral paper AAAI
    MobileTL: On-device Transfer Learning with Inverted Residual Blocks
    Hung-Yueh Chiang, Natalia Frumkin, Feng Liang, and Diana Marculescu
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 2023
  3. FOX-NAS: Fast, On-device and Explainable Neural Architecture Search
    Chia-Hsiang Liu, Yu-Shin Han, Yuan-Yao Sung, Yi Lee, Hung-Yueh Chiang, and 1 more author
    In ICCV Low-Power Computer Vision Workshop (LPCV) 2021
  4. 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, and 3 more authors
    arXiv preprint arXiv:2104.04687 2021
  5. A unified point-based framework for 3d segmentation
    Hung-Yueh ChiangYen-Liang LinYueh-Cheng Liu, and Winston H Hsu
    In International Conference on 3D Vision (3DV) 2019
  6. Cross-domain image-based 3D shape retrieval by view sequence learning
    Tang Lee, Yen-Liang LinHungYueh Chiang, Ming-Wei Chiu, Winston Hsu, and 1 more author
    In International Conference on 3D Vision (3DV) 2018


  • I like playing table tennis, piano, 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...

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.