Can Yaras
Jahn YAH-rahs | cjyaras [at] umich [dot] edu
cv | scholar | github
Hi! I’m a final-year PhD candidate in Electrical and Computer Engineering at the University of Michigan, advised by Qing Qu and Laura Balzano. I received my undergraduate degree from Duke University in Electrical and Computer Engineering, along with a major in Mathematics and a minor in Computer Science.
I’m currently interested in hardware-aware design of efficient machine learning algorithms via low-dimensional structure in learning and computation.
Publications
- C. Yaras, A.S. Xu, P. Abillama, C. Lee, L. Balzano. Zero-Shot Conversion to Monarch-Structured Attention. ICML’25, ES-FoMo III Workshop.
- C. Yaras*, S.Chen*, P. Wang, Q. Qu. Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning. CPAL'25.
- C. Yaras, P. Wang, L. Balzano, Q. Qu. Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation. ICML'24, Oral Presentation.
- C. Yaras, P. Wang, W. Hu, Z. Zhu, L. Balzano, Q. Qu. Invariant Low-Dimensional Subspaces in Gradient Descent for Learning Deep Matrix Factorizations. NeurIPS’23, M3L Workshop.
- C. Yaras*, P. Wang*, Z. Zhu, L. Balzano, Q. Qu. Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold. NeurIPS'22.
- P. Wang*, H. Liu*, C. Yaras*, L. Balzano, Q. Qu. Linear Convergence Analysis of Neural Collapse with Unconstrained Features. NeurIPS’22, OPT Workshop.
- 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 J-STARS.
Preprints
- C. Yaras, A.S. Xu, P. Abillama, C. Lee, L. Balzano. MonarchAttention: Zero-Shot Conversion to Fast, Hardware-Aware Structured Attention. In Submission.
- 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 Submission.
- (α-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. In Submission.
- A.S. Xu, C. Yaras, P. Wang, Q. Qu. Understanding How Nonlinear Layers Create Linearly Separable Features for Low-Dimensional Data. In Submission.
- P. Wang*, X. Li*, C. Yaras, Z. Zhu, L. Balzano, W. Hu, Q. Qu. Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination. In Submission.