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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



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.


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


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 



Honors and Awards