Chiraag Kaushik

About Me

I'm a fifth-year PhD student at the School of Electrical and Computer Engineering and the Center for Machine Learning at Georgia Tech, advised by Vidya Muthukumar and Justin Romberg. I completed my BS in Electrical and Computer Engineering in May 2021 from Rice University and an MS in Mathematics from Georgia Tech in May 2025. My work has been generously supported by the NSF Graduate Research Fellowship.

My research interests span high-dimensional and overparameterized machine learning and signal processing. In particular, I am interested in furthering scientific and mathematical understanding of commonly observed empirical phenomena in modern machine learning systems. This has led me to study

• Generalization and robustness, especially in the context of typically ad-hoc training procedures like data augmentation and in modern representation learning
• Feature learning, or the ability of modern ML architectures to adapt to low-dimensional structure in data
• Models that interpolate, or perfectly fit, training data

During Summer 2025, I was a research intern in the Machine Perception and Reasoning team at Dolby Laboratories.

In my free time, I enjoy reading, running, and listening to/practicing Indian classical music.

Publications

Conference papers

• MANGO: Disentangled Image Transformation Manifolds with Grouped Operators
  Brighton Ancelin, Yenho Chen, Peimeng Guan, Chiraag Kaushik, Belen Martin-Urcelay, Alex Saad-Falcon, Nakul Singh
  SampTA 2025
  [arxiv]
  
  
• Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks
  Chiraag Kaushik, Justin Romberg, and Vidya Muthukumar
  NeurIPS 2024
  [arxiv]
  
  
• Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
  Chiraag Kaushik (equal cont.), Ran Liu (equal cont.), Chi-Heng Lin, Amrit Khera, Matthew Jin, Wenrui Ma, Vidya Muthukumar, and Eva L. Dyer
  ICML 2024
  [arxiv]
  
  
• Network topology change-point detection from graph signals with prior spectral signatures
  Chiraag Kaushik, T.M. Roddenberry, and Santiago Segarra
  ICASSP 2021
  [arxiv][code]
  
  

Journal papers

• New equivalences between interpolation and SVMs: Kernels and structured features
  Chiraag Kaushik, Andrew McRae, Mark Davenport, and Vidya Muthukumar
  SIMODS 2024
  [arxiv]
  
  
• The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective
  Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, and Vidya Muthukumar
  JMLR 2024
  [arxiv][code]
  
  

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