Can Yaras

Princeton, New Jersey
cy4900@princeton.edu | canyaras.com

Academic Appointments

Princeton University Princeton, NJ

Postdoctoral Research Fellow, Princeton Language and Intelligence 2026-present

Education

University of Michigan Ann Arbor, MI

Ph.D., Electrical and Computer Engineering 2021-2026

Advisors: Qing Qu, Laura Balzano

University of Michigan Ann Arbor, MI

M.S., Electrical and Computer Engineering, GPA – 4.0 2021-2023

Duke University Durham, NC

B.S.E., Electrical and Computer Engineering, GPA – 3.99 2017-2021

Major in Mathematics, Minor in Computer Science
Summa Cum Laude

Select Publications

C. Yaras, A.S. Xu, P. Abillama, C. Lee, L. Balzano. MonarchAttention: Zero-Shot Conversion to Fast, Hardware-Aware Structured Attention. NeurIPS'25, Spotlight.

C. Yaras, P. Wang, L. Balzano, Q. Qu. Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation. ICML'24, Oral.

Recognition & Awards

Rising Star Award
CPAL'26

Rackham Predoctoral Fellowship
Awarded to top 1.5% of University of Michigan ECE PhD students, 2025-2026

Best Poster Award
Among 116 posters, MMLS'24

Oral Presentation
Awarded to top 1.8% of accepted papers, Efficient LLMs session, ICML'24

Scholar Award
NeurIPS'22

Work Experience

Google – Student Researcher Sunnyvale, CA

Built fast static analyzers for benchmarking TPU kernels. 2025

Google – Student Researcher New York, NY

Worked on efficiently scaling up large language models through sparsity. 2023

Journal Articles

L. Balzano, T. Ding, B.D. Haeffele, S.M. Kwon, Q. Qu, P. Wang, Z. Wang, C. Yaras. An Overview of Low-Rank Structures in the Training and Adaptation of Large Models: From Implicit Low-Dimensionality to Efficient Training and Fine-Tuning. IEEE Signal Processing Magazine, vol. 43, no. 3, pp. 52–70, 2026. Authors in alphabetical order.

P. Wang, X. Li, C. Yaras, Z. Zhu, L. Balzano, W. Hu, Q. Qu. Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination. Journal of Machine Learning Research, vol. 26, no. 220, pp. 1–61, 2025.

C. Yaras, K. Kassaw, B. Huang, K. Bradbury, J.M. Malof. Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 1988–1998, 2024.

T. Sarwar, C. Yaras, X. Li, Q. Qu, P.C. Ku. Miniaturizing a Chip-Scale Spectrometer Using Local Strain Engineering and Total-Variation Regularized Reconstruction. Nano Letters, vol. 22, no. 20, pp. 8174–8180, 2022.

Conference Proceedings

A.S. Xu, C. Yaras, M. Asato, Q. Qu, L. Balzano. Emergent Low-Rank Training Dynamics in MLPs with Smooth Activations. In Advances in Neural Information Processing Systems 39 (NeurIPS 2026), 2026. To appear.

K. Lee, C. Delacour, K. Callahan-Coray, K. Jiang, C. Yaras, S. Oymak, T. Srimani, K.Y. Camsari. Stochastic Sparse Attention for Memory-Bound Inference. In Proceedings of the 43rd International Conference on Machine Learning (ICML 2026), PMLR vol. 306, pp. 63656–63682, 2026.

S.M. Kwon, A.S. Xu, C. Yaras, L. Balzano, Q. Qu. Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective. In Proceedings of the 29th International Conference on Artificial Intelligence and Statistics (AISTATS 2026), PMLR vol. 300, pp. 4285–4293, 2026.

A.S. Xu, C. Yaras, P. Wang, Q. Qu. Linearly Separable Features in Shallow Nonlinear Networks: Width Scales Polynomially with Intrinsic Data Dimension. In Proceedings of the 29th International Conference on Artificial Intelligence and Statistics (AISTATS 2026), PMLR vol. 300, pp. 172–180, 2026.

C. Yaras, A.S. Xu, P. Abillama, C. Lee, L. Balzano. MonarchAttention: Zero-Shot Conversion to Fast, Hardware-Aware Structured Attention. In Advances in Neural Information Processing Systems 38 (NeurIPS 2025), pp. 3834–3860, 2025. Spotlight.

C. Yaras, S. Chen, P. Wang, Q. Qu. Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning. In Proceedings of the Second Conference on Parsimony and Learning (CPAL 2025), PMLR vol. 280, pp. 1365–1387, 2025.

C. Yaras, P. Wang, L. Balzano, Q. Qu. Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation. In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), PMLR vol. 235, pp. 56946–56965, 2024. Oral.

C. Yaras, P. Wang, Z. Zhu, L. Balzano, Q. Qu. Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 11547–11560, 2022.

Preprints

S.M. Kwon, A.S. Xu, C. Yaras, D. Song, L. Balzano, Q. Qu. The Effect of Training Task Diversity on In-Context Learning through the Lens of Low-Dimensional Subspaces. arXiv:2606.06814, 2026.

Teaching Experience

Graduate Student Instructor University of Michigan

EECS 598: Machine Learning Theory 2024

Undergraduate Teaching Assistant Duke University

MATH 122: Calc. II
CS 250: Computer Architecture
ECE 495: Applied Prob. for Stat. Learning 2018-2020