Hi, I'm Deqing Fu (傅 德卿).
I’m a second-year Ph.D. student in Computer Science at University of Southern California (USC). My main research interests are deep learning theory, natural language processing and interpretability of AI systems. I'm (co-)advised by Prof. Vatsal Sharan of USC Theory Group and Prof. Robin Jia of USC NLP Group; and I'm working closely with Prof. Mahdi Soltanolkotabi.
Before that, I did my undergraduate and master's at the University of Chicago, in Mathematics (with Honors), Computer Science (with Honors), and Statistics.
I have a broad interest in machine learning and deep learning. My interests include, but not limited to, deep learning theory, interpretability of large language models, and deep generative models.
Links: Google Scholar, Semantic Scholar, GitHub, and CV
Email: [First][Last] at USC dot EDU
Papers
2024
Pre-trained Large Language Models Use Fourier Features to Compute Addition [paper]
Tianyi Zhou, Deqing Fu, Vatsal Sharan, Robin Jia
Arxiv, 2024
IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations [paper, website]
Deqing Fu*, Ghazal Khalighinejad*, Ollie Liu*, Bhuwan Dhingra, Dani Yogatama, Robin Jia, Willie Neiswanger
Arxiv, 2024
*Equal Contribution. Co-first authors ordered alphabetically.
Simplicity Bias of Transformers to Learn Low Sensitivity Functions [paper]
Bhavya Vasudeva*, Deqing Fu*, Tianyi Zhou, Elliot Kau, You-Qi Huang, Vatsal Sharan
Arxiv, 2024
*Equal Contribution.
DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models [paper, codes, website]
Ollie Liu*, Deqing Fu*, Dani Yogatama, Willie Neiswanger
Arxiv, 2024
*Equal Contribution.
2023
Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study with Linear Models [paper, codes]
Deqing Fu, Tian-Qi Chen, Robin Jia, Vatsal Sharan
Arxiv, 2023
SoCalNLP Symposium 2023 Best Paper Award.
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback [paper]
Jiao Sun*, Deqing Fu*, Yushi Hu*, Su Wang, Royi Rassin, Da-Cheng Juan, Dana Alon, Charles Herrmann, Sjoerd van Steenkiste, Ranjay Krishna, Cyrus Rashtchian
Arxiv, 2023
*Equal Contribution. Work done while at Google.
SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples [paper, codes]
Deqing Fu, Ameya Godbole, Robin Jia.
Empirical Methods in Natural Lanaguge Processing (EMNLP), 2023
2022 and Earlier
Topological Regularization for Dense Prediction [paper]
Deqing Fu, Bradley Nelson.
International Conference on Machine Learning and Applications (ICML-A), 2022 (Oral Presentation)
Harnessing the Conditioning Sensorium for Improved Image Translation [paper]
Cooper Nederhood, Nicholas Kolkin, Deqing Fu, Jason Salavon.
International Conference on Computer Vision (ICCV), 2021
Comparison of Two Gradient Computation Methods in Python [paper]
Sri Hari Krishna Narayanan, Paul Hovland, Kshitij Kulshreshtha, Devashri Nagarkar, Kaitlyn MacIntyre, Riley Wagner, Deqing Fu.
Neural Information Processing Systems (NIPS) Autodiff Workshop, 2017
Education
University of Southern California (2022-2027)
Ph.D. in Computer Science
Advisors: Vatsal Sharan & Robin Jia
University of Chicago (2020-2022)
M.S. in Statistics
University of Chicago (2016-2020)
B.S. (with Honors) in Mathematics
B.S. (with Honors) in Computer Science
with specialization in Machine Learning
B.A. in Statistics
Experience
05.2023 - 08.2023 Intern @ Google, Bard & AssistantNLP
05.2022 - 08.2022 Intern @ Google, Knowledge Engine
06.2021 - 09.2021 Intern @ Google, Lens
Services
Reviewer: ECCV 2022, CVPR (2023, 2024), EMNLP 2023
Honors and Awards
Provost's Fellowship @USC
Susanne H. Rudolph Scholarship @UChicago
Liew Family College Research Fellows Fund @UChicago
Jeff Metcalf Internship Award @UChicago
Dean’s List 2016-2020 @UChicago