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)

Lipschitz constant estimation for Neural Networks via sparse polynomial optimization

F. Latorre; P. T. Y. Rolland; V. Cevher 

2020-04-26. 8th International Conference on Learning Representations, Addis Ababa, ETHIOPIA, April 26-30, 2020.

A reflected forward-backward splitting method for monotone inclusions involving Lipschitzian operators

V. Cevher; C. B. Vu 

Set-valued and Variational analysis. 2020-03-19. 

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

K. Parameswaran; Y-T. Huang; Y-P. Hsieh; P. T. Y. Rolland; C. Shi et al. 


Scalable Learning-Based Sampling Optimization For Compressive Dynamic MRI

T. Sanchez; B. Gözcü; R. Van Heeswijk; A. Eftekhari; E. Ilıcak et al. 

2020. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 4-8, 2020.

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

J. Lin; V. Cevher 

Journal of Machine Learning Research. 2020. Vol. 21, num. 20, p. 1-44.


e-mail address: [email protected]

telephone: 0041 21 6930111

Additional links