Mingcheng Lu, Fudan University Math BS'27

• Transformer Approximations from ReLUs [arXiv]
• A Theoretical Analysis of Discrete Flow Matching Generative Models [arXiv]
• Learning Manifold Data with Flow Matching [ICML'26]

Xiwen Zhang, Fudan University Math BS'27

• On Structure State-Space Duality [ICML'26]
• Learning Constrained Boolean Functions with Softmax Attention [arXiv]

Po-Chiao Lin, NTU Physics BS'25

• Universal Approximation, Compositional Generalization, and Algorithm Emulation All In-Context [ICML'26]

Venkat Sripad Ganti, Northwestern Math + Statistics + Economics BS'27

• Chain-of-Thought Gradient Descent [ICML'26]

Jennifer Yuntong Zhang, University of Toronto BAS'26 Engineering Science → Yale CS MS (Fall'26)

• In-Context Algorithm Emulation in Fixed-Weight Transformers [ICLR'26]

Hude Liu, Fudan University Math BS'25

• In-Context Algorithm Emulation in Fixed-Weight Transformers [ICLR'26]
• Attention Mechanism, Max-Affine Partition, and Universal Approximation [NeurIPS'25a]
• Universal Approximation with Softmax Attention [ICML'26]

Maojiang Su, University of Science and Technology of China (School of the Gifted Young) BS'25 → CS PhD at Northwestern (Fall'25)

• A Theoretical Analysis of Discrete Flow Matching Generative Models [arXiv]
• High-Order Flow Matching: Unified Framework and Sharp Statistical Rates [NeurIPS'25b]
• In-Context Deep Learning via Transformer Models [ICML'25b]
• Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models [ICLR'25a]

Zoe Mehta, High School Outreach Student @ Vernon Hills High School → MIT (Class of 2029)

• Fast and Low-Cost Genomic Foundation Models via Outlier Removal [ICML'25a]

Sophia Pi, Northwestern CS + Economics + Mathematical Methods in Social Science BS'26 → UPenn CS PhD (Fall'26)

• Learning Manifold Data with Flow Matching [ICML'26]
• On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs) [NeurIPS'24b]
• On Flow Matching KL Divergence [arXiv]

Chenghao Qiu, Tianjin University CS BS'25 → CS PhD study at TAMU (Fall'25)

• Fast and Low-Cost Genomic Foundation Models via Outlier Removal [ICML'25a]

Thomas Yuan-Lung Lin, High School Outreach Student @ WLSH'23 → NTU (transferred) → University of Washington Physics (Class of 2027)

• Latent Variable Estimation in Bayesian Black-Litterman Models [ICML'25e]
• On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis [ICML'24a]

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

Teng-Yun Hsiao, NTU Physics BS'26

• In-Context Learning as Conditioned Associative Memory Retrieval [ICML'25c]
• Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models [ICML'24d]

Wei-Po Wang, NTU Physics BS'24 → Physics 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'24b]

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 Northwestern (Fall'24)

• Universal Approximation of Softmax Attention [ICML'26]
• Outlier-Efficient Hopfield Layers for Large Transformer-Based Models [ICML'24b]

Yi-Chen Lee, NTU Physics BS'26 → CS PhD study at Johns Hopkins University (Fall'26)

• High-Order Flow Matching: Unified Framework and Sharp Statistical Rates [NeurIPS'25b]
• On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality [ICLR'25c]

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

• Nonparametric Modern Hopfield Models [ICML'25c]
• 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 Northwestern → Stats & DS PhD study at Northwestern (Fall'24)

• BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Hopfield Model [ICML'24c]
• 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 Northwestern → CS PhD study at Northwestern (Fall'24)

• Provably Optimal Memory Capacity for Modern Hopfield Models [NeurIPS'24]
• Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models [ICML'24d]
• 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 University of Rochester → CS PhD study at Northwestern (Fall'24)

Zhenji Wang, UMD'21 Math → MS'23 at Columbia University → ML PhD study at University of Tsukuba

• Differential Geometry and Heat Kernel Expansion, 2021 Spring