Home

Can Yaras

Ann Arbor, Michigan
cjyaras@gmail.com | canyaras.com

Education

University of Michigan Ann Arbor, MI
Ph.D., Electrical and Computer Engineering 2021-Present
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

Research Interests

  • Efficient computation and optimization processes for deep learning training + inference.
  • Hardware-algorithm codesign of machine learning primitives.

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

Other Publications

(⍺-β) 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. IEEE Signal Processing Magazine.
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. AISTATS'26.
A.S. Xu, C. Yaras, P. Wang, Q. Qu. Understanding How Nonlinear Layers Create Linearly Separable Features for Low-Dimensional Data. AISTATS'26.
P. Wang, X. Li, C. Yaras, Z. Zhu, L. Balzano, W. Hu, Q. Qu. Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination. JMLR.
C. Yaras, S. Chen, P. Wang, Q. Qu. Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning. CPAL'25.
C. Yaras, P. Wang, Z. Zhu, L. Balzano, Q. Qu. Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold. NeurIPS'22.
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.
C. Yaras, B. Huang, K. Bradbury, J.M. Malof. Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery. IEEE JSTARS.

Preprints

A.S. Xu, C. Yaras, M. Asato, Q. Qu, L. Balzano. Emergent Low-Rank Training Dynamics in MLPs with Smooth Activations. arXiv.

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