Prof. Volkan Cevher

Research Interests:

  • Machine Learning
  • Optimization
  • Signal Processing
  • Information Theory


Volkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta, GA in 2005. He was a Research Scientist with the University of Maryland, College Park from 2006-2007 and also with Rice University in Houston, TX, from 2008-2009. Currently, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University. His research interests include signal processing theory, machine learning, convex optimization, and information theory. Dr. Cevher is an ELLIS fellow and was the recipient of the Google Faculty Research Award on Machine Learning in 2018, IEEE Signal Processing Society Best Paper Award in 2016, a Best Paper Award at CAMSAP in 2015, a Best Paper Award at SPARS in 2009, and an ERC CG in 2016 as well as an ERC StG in 2011.

Publications (most recent)

A Newton Frank-Wolfe method for constrained self-concordant minimization

D. Liu; V. Cevher; Q. Tran-Dinh 

Journal Of Global Optimization. 2021-11-20. DOI : 10.1007/s10898-021-01105-z.

Forward-reflected-backward method with variance reduction

A. Alacaoglu; Y. Malitsky; V. Cevher 

Computational Optimization and Applications. 2021-08-19. DOI : 10.1007/s10589-021-00305-3.

A 16-Channel Wireless Neural Recording System-on-Chip with CHT Feature Extraction Processor in 65nm CMOS

A. Uran; K. Ture; C. Aprile; A. Trouillet; F. Fallegger et al. 

2021-05-17. 2021 IEEE Custom Integrated Circuits Conference (CICC), Virtual, April 25-30, 2021. DOI : 10.1109/CICC51472.2021.9431458.

A Plug-and-Play Deep Image Prior

Z. Sun; F. Latorre; T. Sanchez; V. Cevher 

2021. International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021), Toronto, Canada, June 6-11, 2021. DOI : 10.1109/ICASSP39728.2021.9414879.

Sifting through the Noise: Universal First-Order Methods for Stochastic Variational Inequalities

K. Antonakopoulos; T. M. Pethick; A. Kavis; P. Mertikopoulos; V. Cevher 

2021. NeurIPS 2021 : Thirty-fifth Conference on Neural Information Processing Systems, Sydney, Australia [Virtual only], December 6-14, 2021.


e-mail address: [email protected]

telephone: 0041 21 6930111

Additional links