# About me

I am a Postdoctoral Researcher at Ecole Polytechnique Federale de Lausanne (EPFL) since November 2020. My research interests lie in machine learning and computer vision, and more precisely in learning (robust) representations and generative modeling.

## News

- September 2022: The following papers have been accepted at
**NeurIPS 2022**: a) 'Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)', b) 'Generalization Properties of NAS under Activation and Skip Connection Search', c) 'Sound and Complete Verification of Polynomial Networks', d) 'Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study'. - August 2022: The slides used in the tutorial on polynomial networks (organized at CVPR'22) have been released.
- July 2022: I was awarded a
**best reviewer award (top 10%)**at**ICML 2022**. - July 2022: The following papers have been accepted at
**ECCV 2022**: 'Augmenting Deep Classifiers with Polynomial Neural Networks' and 'MimicME: A Large Scale Diverse 4D Database for Facial Expression Analysis'. More information soon. - June 2022: Organizer of the tutorial on 'Polynomial Nets' in conjunction with CVPR'22: https://polynomial-nets.github.io/.
- April 2022: I was awarded a
**highlighted reviewer award**at**ICLR 2022**. - March 2022: The following paper has been accepted at
**CVPR 2022**: '*Cluster-guided Image Synthesis with Unconditional Models*'. - February 2022:
**My talk**on polynomial networks at the UCL Centre for Artificial Intelligence has been uploaded online. - January 2022: The following papers have been accepted at
**ICLR 2022**: '*Controlling the Complexity and Lipschitz Constant improves Polynomial Nets*' and '*The Spectral Bias of Polynomial Neural Networks*'. - October 2021: The following paper has been accepted at
**NeurIPS 2021**: 'Conditional Generation Using Polynomial Expansions'. - July 2021: I was awarded a
**best reviewer award (top 10%)**at**ICML 2021**. - The following paper has been accepted at
**Proceedings of the IEEE (2021)**: '*Tensor methods in computer vision and deep learning*'