Prof. Volkan Cevher ‒ LIONS ‐ EPFL

Research Interests:

Machine Learning
Optimization
Signal Processing
Information Theory

Biography

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. He was also a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University from 2010-2020. Currently, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and an Amazon Scholar. His research interests include machine learning, optimization theory and methods, and automated control. Dr. Cevher is an IEEE Fellow (’24), an ELLIS fellow, and was the recipient of the ICML AdvML Best Paper Award in 2023, Google Faculty Research award in 2018, the 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)

Adaptive Bilevel Optimization

K. Antonakopoulos; S. Sabach; L. Viano; M. Hong; V. Cevher

ACM / IMS Journal of Data Science. 2025. DOI : 10.1145/3728478.

Research Interests:

Biography

Publications (most recent)

Adaptive Bilevel Optimization

How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

Quantum-Peft: Ultra Parameter-Efficient Fine-Tuning

Certified Robustness Under Bounded Levenshtein Distance

Faster Inference Of Flow-Based Generative Models Via Improved Data-Noise Coupling

Efficient Interpolation Between Extragradient And Proximal Methods For Weak MVIS

Addressing Label Shift In Distributed Learning Via Entropy Regularization

Adversarial Training For Defense Against Label Poisoning Attacks

Hadamard product in deep learning: Introduction, Advances and Challenges

Membership Inference Attacks against Large Vision-Language Models

SAMPa: Sharpness-aware Minimization Parallelized

REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates

Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

Truly No-Regret Learning in Constrained MDPs

Mixed Nash for Robust Federated Learning

Universal Gradient Methods for Stochastic Convex Optimization

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Efficient local linearity regularization to overcome catastrophic overfitting

On the Generalization of Stochastic Gradient Descent with Momentum

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