Jerry YC Hu's Research Page

Teaching

[Spring 2025,2024] TA, "Advanced Topics on Deep Learning" (CS 496), Northwestern University CS.
[Spring 2025,2024] TA, "Mathematical Foundations of Machine Learning" (CS 416 & STAT 435), Northwestern University CS & Stats & Data Science.
[Winter 2022] TA, "Data Science Pipeline" (COMP_SCI 326), Northwestern University CS.
[Fall 2020] Instructor, "Vibrations, Waves, & Electromagnetism." Notes & recordings upon request, University of Maryland College Park Physics.
[2018-2021] TA for multiple courses (PHYS121, PHYS122, PHYS131, PHYS132, PHYS260, PHYS261, PHYS603...), University of Maryland College Park Physics.
[Spring 2018] TA, "Gravity, S-Matrix & String Theory," NTU Physics.
[Fall 2017] TA, "Quantum Field Theory I," NTU Physics.

Collaboration and Mentoring
(in reverse chronological order)

Maojiang Su, University of Science and Technology of China (School of the Gifted Young) BS'24 -> CS PhD at NU (Fall'25)

In-Context Deep Learning via Transformer Models [arXiv]
Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models [ICLR'25a]

Zoe Meta, High School Outreach Student @ Vernon Hills High School -> MIT (Class of 2029)

Statistical theory of LoRA

Sophia Pi, Northwestern CS+Econ+Math Methods in Social Science BS'26

On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs) [NeurIPS'24b]

Chenghao Qiu, BSCS'25 at Tianjin University -> CS PhD study at TAMU (Fall'25)

Robust Compression and Fast Adaptation for Genomic Foundation Models

Thomas Yuan-Lung Lin, High School Outreach Student @ WLSH'23 -> NTU Physics + Econ (Class of 2027)

- On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis [ICML'24]

Stephen Cheng, Northwestern EE BS'25 + CS MS'25 -> CS PhD at UMD (Fall'25)

Financial Data Prediction Models, Statistical Theory of Diffusion Models and Transformer

Wei-Po Wang, NTU Physics BS'24 -> Phys PhD at Johns Hopkins University (Fall'25)

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency [ICLR'25b]
Outlier-Efficient Hopfield Layers for Large Transformer-Based Models [ICML'24]

Morris Huang, NTU Physics MS'24 -> CS PhD at North Carolina, Chapel Hill (Fall'25)

On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality [ICLR'25c]
BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Hopfield Model [ICML'24]

Hong-Yu Chen, NTU Physics MS'24 -> CS PhD study at NU (Fall'24)

- Outlier-Efficient Hopfield Layers for Large Transformer-Based Models [ICML'24]

Bo-Yu Chen, High School Outreach Student @ HSNU'23 -> NTU Physics + CS (Class of 2027) with NTU Fu Bell Scholarship (Highest Distinction across University)

- STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction [ICLR'24]
- On Sparse Modern Hopfield Model [NeurIPS'23]

Chenwei Xu, MSCS'24 at NU -> Stats & DS PhD study at NU (Fall'24)

- BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Hopfield Model [ICML'24]
- On Sparse Modern Hopfield Model [NeurIPS'23]
- Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e [ML4Phys Workshop @ NeurIPS'23] [READS Collaboration, Fermilab]
- Feature Programming for Multivariate Time Series Prediction [ICML'23]

Dennis Wu, MSCS'24 at NU -> CS PhD study at NU (Fall'24)

- Provably Optimal Memory Capacity for Modern Hopfield Models [NeurIPS'24]
- Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models [ICML'24]
- STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction [ICLR'24]
- On Sparse Modern Hopfield Model [NeurIPS'23]

Zhenyu Pan, MSECE'24 at U. of Rochester -> CS PhD study at NU (Fall'24)

Zhenji Wang, UMD'21 Math -> MS'23 at Columbia U. -> ML PhD study at U. of Tsukuba

- Differential Geometry and Heat Kernel Expansion, 2021 Spring

A nice PhD checklist from Aaditya Ramdas

An open letter to graduate students and other procrastinators: it’s time to write (from Dennis J. Hazelett)

https://www.nature.com/articles/s41587-025-02584-1

“To boldly go where no man has gone before.”

By far, Star Trek has one of the most famous opening narrations in the history of the filming industry. Resonating a feeling of the 1960’s futuristic optimism, the iconic phrase was first spoken by William Shatner who starred as Captain James T. Kirk in the original series.

“Space: the final frontier. These are the voyages of the starship Enterprise. Its five-year mission: to explore a strange new world, to seek out new life and new civilizations, to boldly go where no man has gone before.”

Teaching

Collaboration and Mentoring (in reverse chronological order)

A nice PhD checklist from Aaditya Ramdas

An open letter to graduate students and other procrastinators: it’s time to write (from Dennis J. Hazelett)

“To boldly go where no man has gone before.”

Collaboration and Mentoring
(in reverse chronological order)