PLENARY SPEAKERS

Arnaud Doucet

Google DeepMind

Arnaud Doucet is a Senior Staff Research Scientist at Google DeepMind. He earned his Ph.D. in Electrical Engineering from the University of Paris-XI Orsay in 1997. Over the years, he has held research positions at Oxford University, Cambridge University and the University of Melbourne before joining Google Deepmind in 2023. His research focuses on Bayesian methods, computational statistics, Monte Carlo methods, and generative modeling. Doucet has received several prestigious honors, including being named an Institute of Mathematical Statistics (IMS) Fellow in 2017, delivering the IMS Medallion Lecture in 2016, and receiving the Guy Silver Medal from the Royal Statistical Society in 2020. His recent work explores denoising diffusion models and computational optimal transport, with numerous publications featured in top statistical and machine learning journals.

Volkan Cevher

Ecole Polytechnique Fédérale de Lausanne (EPFL)

Volkan Cevher is an Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) and an Amazon Scholar. He earned his B.Sc. in Electrical Engineering as valedictorian from Bilkent University in 1999 and completed his Ph.D. at the Georgia Institute of Technology in 2005. Before joining EPFL, he held research positions at the University of Maryland and Rice University. His research focuses on machine learning, optimization, signal processing, and information theory. He has been recognized with several prestigious awards, including being named an IEEE Fellow in 2024, receiving the ICML AdvML Best Paper Award in 2023, and winning the Google Faculty Research Award in 2018. Additionally, he was awarded an ERC Consolidator Grant in 2016 and an ERC Starting Grant in 2011. His recent publications explore topics such as stochastic optimization, federated learning, and adversarial robustness, with multiple papers featured in leading AI and machine learning conferences.

Alexandre Gramfort

Meta

Alexandre Gramfort is a Senior Research Scientist at Meta Reality Labs in Paris, specializing in machine learning for building neuromotor interfaces using surface EMG signals. Previously, he was a Research Director at Inria, leading the MIND Team, and an Assistant Professor at Telecom Paris. His work spans machine learning, signal processing, and neuroscience applications. He is well known for his open-source contributions such as the scikit-learn software he co-created in 2010. He has received prestigious grants, including an ERC Starting Grant for SLAB in 2015 and an ANR Chaire on AI for BrAIN in 2019. He has also taught optimization, machine learning and neuroimaging courses at Institut Polytechnique de Paris and Université de Paris since 2015. His recent research focuses on generic neuromotor interfaces, domain adaptation, and electrophysiological data analysis.

Urbashi Mitra

University of Southern California

Urbashi Mitra is the Gordon S. Marshall Chair in Engineering at the University of Southern California, with previous academic roles at Ohio State University and Bellcore. She holds B.S. and M.S. degrees from the University of California, Berkeley, and a Ph.D. from Princeton University. Dr. Mitra has made significant contributions to IEEE, serving as the inaugural editor-in-chief for IEEE Transactions on Molecular, Biological and Multi-Scale Communications, a Distinguished Lecturer for IEEE Communication and Signal Processing Societies, and in leadership roles including chairing the ComSoc Communication Theory Technical Committee, the SPS Signal Processing for Communications and Networks Committee, and the Transactions on Wireless Communications Steering Committee. She is an IEEE Fellow and has received numerous prestigious awards, including the ComSoc Women in Communications Engineering Technical Achievement Award,  U.S. Fulbright Scholar  and UK Royal Academy of Engineering Distinguished Visiting Professorship distinctions, a  USC Viterbi School of Engineering Senior Research Award and the NSF CAREER Award.