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HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Tue, 04 Mar 2025 20:11:31 GMT access-control-allow-origin: * strict-transport-security: max-age=31556952 etag: W/"67c75e73-970e" expires: Mon, 29 Dec 2025 02:51:23 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: B31B:1F53DD:82FB56:93343B:6951EA52 accept-ranges: bytes age: 0 date: Mon, 29 Dec 2025 02:41:23 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210098-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1766976083.072554,VS0,VE204 vary: Accept-Encoding x-fastly-request-id: 7df1e46429e00eea1aae4c2582c283b46625ccfc content-length: 6758 Yi-Lin Sung

Chapel Hill, NC

ylsung@cs.unc.edu

Yi-Lin Sung

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 email!

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

Mar 2025: Introduce "RSQ" to learn better quantized LLMs with important tokens.

Nov 2024: "DAM" is accepted to WACV 2025.

Oct 2024: "SELMA" is accepted to NeurIPS 2024.

Oct 2024: Preprint of "Glider" is online.

Jan 2024: Two papers, "ECoFLaP" and "MC-SMoE", are accepted to ICLR 2024.

Oct 2023: "An Empirical Study of Multimodal Model Merging" is accepted to EMNLP Findings 2023.

Oct 2023: Preprints of "ECoFLaP" and "MC-SMoE" are online.

July 2023: "Unified Coarse-to-Fine Alignment for Video-Text Retrieval" is accepted to ICCV 2023.

May 2023: Start the research internship at Meta.

April 2023: A preprint of "An Empirical Study of Multimodal Model Merging" is online.

Feb 2023: "Vision Transformers are Parameter-Efficient Audio-Visual Learners" is accepted to CVPR 2023.

Publications

MY WORK

ALL Preprint Conference Workshop

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

[Paper]

[Code]

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

[Paper]

[Code]

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

[Paper]

[Code]

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

[Paper]

[Code]

[Project Page]

2023

ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models

Yi-Lin Sung, Jaehong Yoon, Mohit Bansal

ICLR 2024

[Paper]

[Code]

[Project Page]

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

[Paper]

[Code]

Unified Coarse-to-Fine Alignment for Video-Text Retrieval

Ziyang Wang, Yi-Lin Sung, Feng Cheng, Gedas Bertasius, Mohit Bansal

ICCV 2023

[Paper]

[Code]

An Empirical Study of Multimodal Model Merging

Yi-Lin Sung, Linjie Li, Kevin Lin, Zhe Gan, Mohit Bansal, Lijuan Wang

EMNLP Findings 2023

[Paper]

[Code]

2022

Vision Transformers are Parameter-Efficient Audio-Visual Learners

Yan-Bo Lin, Yi-Lin Sung, Jie Lei, Mohit Bansal, Gedas Bertasius

CVPR 2023

[Paper]

[Code]

LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning

Yi-Lin Sung, Jaemin Cho, Mohit Bansal

NeurIPS 2022

[Paper]

[Code]

VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks

Yi-Lin Sung, Jaemin Cho, Mohit Bansal

CVPR 2022

[Paper]

[Code]

2021

Training Neural Networks with Fixed Sparse Masks

Yi-Lin Sung, Varun Nair, Colin Raffel

NeurIPS, 2021.

[Paper]

[Code]

The Maximum A Posteriori Estimation of DARTS

Yi-Lin Sung, Jun-Liang Lin, Cheng-Yao Hong, Tyng-Luh Liu

ICIP, 2021.

[Paper]

2020

Video Summarization with Anchors and Multi-head Attention

Yi-Lin Sung, Cheng-Yao Hong, Yen-Chi Hsu

ICIP, 2020.

[Paper]

2019

Difference-Seeking Generative Adversarial Network -- Unseen Data Generation

Yi-Lin Sung, Sung-Hsien Hsieh, Soo-Chang Pei, Chun-Shien Lu

ICLR, 2020.

[Paper]

Tetris Battle -- A New Environment for Single mode and Double Mode Game

Yi-Lin Sung

NeurIPS Workshop on Deep Reinforcement Learning, 2019.

[Paper]

Work Experience

PREVIOUS JOBS

Graduate Research Assistant@MURGe-Lab

Computer Science Department, UNC Chapel Hill

Aug. 2021 - Present

Advisor: Mohit Bansal

Research Intern

Google

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.

Education

ACADEMIC CAREER

PhD student

The University of North Carolina at Chapel Hill - Computer Science

Jan. 2021 - Present

Master of Science

National Taiwan University - Communication Engineering

Sep. 2017 - June 2019

Bachelor of Science

National Taiwan University - Chemical Engineering

Sep. 2012 - Jan. 2017

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