Yi-Lin Sung (original) (raw)
Chapel Hill, NC
ylsung@cs.unc.edu
CS PhD student at UNC
About me
PROFESSIONAL PATH
I am on the job market. With a strong background in cutting-edge areas like Multimodal LLMs, LLM Self-Refinement, Efficiency (PEFT, Quantization, Model Merging), MoE, Personalization, and Text-to-Image Generation. If you're looking for someone who can drive innovation in these fields, feel free to connect with me via
!
I am a fifth-year PhD student at UNC, Chapel Hill. I currently work in the MURGe-Lab, and am advised by Mohit Bansal. My research interests are in the areas of Deep Learning, Machine Learning, and Computer Vision. Recently, I am particularly interested in multi-modal learning and efficient fine-tuning, where my goal is to train large models with limited resources and deploy them to benefit human's daily life. Before joining MURGe-Lab, I also worked with Colin Raffel and Marc Niethammer.
I also spent time working as a research scientist intern in tech company in summers. In 2024 Summer, I interned at Google with Otilia Stretcu on VLM reasoning. In 2023 summer, I interned at Meta with Abhimanyu Dubey, Filip Radenovic and Abhishek Kadian on text-to-image generation. In 2022 summer, I worked at Microsoft with Linjie Li, Kevin Lin and Zhe Gan on VL model merging.
News
Publications
MY WORK
2024
RSQ: Learning from Important Tokens Leads to Better Quantized LLMs
Yi-Lin Sung, Prateek Yadav, Jialu Li, Jaehong Yoon, Mohit Bansal
arXiv:2503.01820
Glider: Global and Local Instruction-Driven Expert Router
Pingzhi Li, Prateek Yadav, Jaehong Yoon, Jie Peng, Yi-Lin Sung, Mohit Bansal, Tianlong Chen
arXiv:2410.07172
DAM: Dynamic Adapter Merging for Continual Video QA Learning
Feng Cheng, Ziyang Wang, Yi-Lin Sung, Yan-Bo Lin, Mohit Bansal, Gedas Bertasius
WACV 2025
SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data
Jialu Li, Jaemin Cho, Yi-Lin Sung, Jaehong Yoon, Mohit Bansal
NeurIPS 2024
2023
ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models
Yi-Lin Sung, Jaehong Yoon, Mohit Bansal
ICLR 2024
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy
Pingzhi Li, Zhenyu Zhang, Prateek Yadav, Yi-Lin Sung, Yu Cheng, Mohit Bansal, Tianlong Chen
ICLR 2024
Unified Coarse-to-Fine Alignment for Video-Text Retrieval
Ziyang Wang, Yi-Lin Sung, Feng Cheng, Gedas Bertasius, Mohit Bansal
ICCV 2023
An Empirical Study of Multimodal Model Merging
Yi-Lin Sung, Linjie Li, Kevin Lin, Zhe Gan, Mohit Bansal, Lijuan Wang
EMNLP Findings 2023
2022
Vision Transformers are Parameter-Efficient Audio-Visual Learners
Yan-Bo Lin, Yi-Lin Sung, Jie Lei, Mohit Bansal, Gedas Bertasius
CVPR 2023
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning
Yi-Lin Sung, Jaemin Cho, Mohit Bansal
NeurIPS 2022
VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks
Yi-Lin Sung, Jaemin Cho, Mohit Bansal
CVPR 2022
2021
Training Neural Networks with Fixed Sparse Masks
Yi-Lin Sung*, Varun Nair*, Colin Raffel
NeurIPS, 2021.
The Maximum A Posteriori Estimation of DARTS
Yi-Lin Sung, Jun-Liang Lin, Cheng-Yao Hong, Tyng-Luh Liu
ICIP, 2021.
2020
Video Summarization with Anchors and Multi-head Attention
Yi-Lin Sung, Cheng-Yao Hong, Yen-Chi Hsu
ICIP, 2020.
2019
Difference-Seeking Generative Adversarial Network -- Unseen Data Generation
Yi-Lin Sung, Sung-Hsien Hsieh, Soo-Chang Pei, Chun-Shien Lu
ICLR, 2020.
Tetris Battle -- A New Environment for Single mode and Double Mode Game
Yi-Lin Sung
NeurIPS Workshop on Deep Reinforcement Learning, 2019.
Work Experience
PREVIOUS JOBS
Graduate Research Assistant@MURGe-Lab
Computer Science Department, UNC Chapel Hill
Aug. 2021 - Present
- Advisor: Mohit Bansal
Research Intern
May. 2024 - Aug. 2024
- Mentors: Otilia Stretcu, Chun-Ta Lu, Alan Luo, Ranjay Krishna
Research Intern
Meta
May. 2023 - Aug. 2023
- Mentors: Abhimanyu Dubey, Filip Radenovic, Abhishek Kadian
Research Intern
Microsoft
May. 2022 - Aug. 2022
- Mentors: Linjie Li, Kevin Lin and Zhe Gan
Summer Intern@UNC-biag
Computer Science Department, UNC Chapel Hill
Jan. 2021 - May. 2021
- Advisor: Marc Niethammer
Teaching Assistant
Computer Science Department, UNC Chapel Hill
Jan. 2021 - May. 2021
- Course: Deep learning.
AI Researcher
Cinnamon AI Taiwan
Mar. 2020 - Dec. 2020
Research Assistant
Institute of Information Science, Academia Sinica
Sep. 2018 - Mar. 2020
- Advisor: Dr. Tyng-Luh Liu
Research Intern
Institute of Information Science, Academia Sinica
July 2018 - Aug. 2018
- Advisor: Dr. Tyng-Luh Liu
Teaching Assistant
Graduate Institute of Communication Engineering, National Taiwan University
Jan. 2018 - June 2018
- Course: Machine Learning and Having It Deep and Structured.
