About

I am a Final-year PhD student at University of Wisconsin–Madison, advised by Pedro Morgado and Yu Hen Hu.
My research interests lie at the intersection of computer vision and deep learning. I focus on Code Large Language Models (Code LLMs), Multimodal Large Language Models (MLLMs), and improving the efficiency of self-supervised learning models in both training and inference.

I've been fortunate to work with really great people along the way. From Spring to Winter 2024, I was a research intern at Microsoft, working with Yunsheng Li, Weijian Xu, and Mengchen Liu. In Summer 2022, I was a research intern at TikTiok, working with Yu Tian, Linjie Yang, Haichao Yu and Heng Wang. In Summer 2021, I was a research intern at NEC Labs America , working with Farlay Lai and Asim Kadav. At Academia Sinica, I was a research assistant working with Chu-Song Chen. I graduated from National Tsing Hua University with an M.S. in Computer Science, where I was advised by Jia-Shung Wang.

More details, please see my CV (updated: February 2025).

I am actively looking for Research Scientist positions starting in 2025.

Publications

From Prototypes to General Distributions: An Efficient Curriculum for Masked Image Modeling
Jinhong Lin*, Cheng-En Wu*, Huanran Li, Jifan Zhang, Yu Hen Hu, Pedro Morgado (*equal contribution)
Conference on Computer Vision and Pattern Recognition (CVPR), 2025
Patch Ranking: Efficient CLIP by Learning to Rank Local Patches
Cheng-En Wu, Jinhong Lin, Yu Hen Hu, Pedro Morgado
Winter Conference on Applications of Computer Vision (WACV), 2025
Accelerating Augmentation Invariance Pretraining
Jinhong Lin*, Cheng-En Wu*, Yibing Wei, Pedro Morgado (*equal contribution)
Conference on Neural Information Processing Systems (NeurIPS), 2024
Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?
Cheng-En Wu, Yu Tian, Haichao Yu, Heng Wang, Pedro Morgado, Yu Hen Hu, Linjie Yang
International Conference on Computer Vision (ICCV), 2023
Self-supervised Video Representation Learning with Cascade Positive Retrieval
Cheng-En Wu, Farley Lai, Yu Hen Hu, Asim Kadav
L3D-IVU Workshop at Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Merging Well-Trained Deep CNN Models for Efficient Inference
Cheng-En Wu, Jia-Hong Lee, Timmy ST Wan, Yi-Ming Chan, Chu-Song Chen
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2020
Extending Conditional Convolution Structures For Enhancing Multitasking Continual Learning
*Cheng-Hao Tu, *Cheng-En Wu, Chu-Song Chen (*equal contribution)
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2020
Compacting, Picking and Growing for Unforgetting Continual Learning
Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, Chu-Song Chen
Conference on Neural Information Processing Systems (NeurIPS), 2019
IMMVP: An Efficient Daytime and NighttimeOn-Road Object Detector
Cheng-En Wu, Yi-Ming Chan, Chien-Hung Chen, Wen-Cheng Chen, Chu-Song Chen
IEEE International Workshop on Multimedia Signal Processing (MMSP), 2019
On Merging MobileNets for Efficient Multitask Inference
Cheng-En Wu, Yi-Ming Chan, Chu-Song Chen
EMC2 Workshop at IEEE International Symposium on high Performance Computer Architecture (HPCA), 2019
Traffic pattern modeling, trajectory classification and vehicle tracking within urban intersections
Cheng-En Wu, Wen-Yen Yang, Hai-Che Ting, Jia-Shung Wang
IEEE International Smart Cities Conference (ISC2), 2017

Work Experience


Microsoft
Research Intern
Feb. 2024 - Dec. 2024
Redmond, WA

TikTok
Research Intern
May 2022 - Aug. 2022
San Jose, WA

NEC Labs America
Research Intern
May 2021 - Aug. 2021
Princeton, NJ

Academia Sinica
Research Assistant
Mar. 2018 - Aug. 2020
Taipei, Taiwan

MediaTek Inc.
Software Engineer
Mar. 2017 - Mar. 2018
Hsinchu, Taiwan

Realtek Inc.
Software Engineer
Dec. 2016 - Mar. 2017
Hsinchu, Taiwan

GOTrust Technology Inc.
Software Engineer
Jan. 2014 - Jun. 2014
Taichung, Taiwan