# About me

I am a Postdoctoral Researcher at Ecole Polytechnique Federale de Lausanne (EPFL) since November 2020.

My research focuses on reliable machine learning and the design and study of expressive models that are robust to noise and generalize well in out-of-distribution data. Concretely,- I have introduced polynomial networks (PNs) that capture high-degree interactions between inputs. My short-term goals are to understand the inductive bias and properties of existing architectures through empirical and theoretical studies. I am interested in the complete theoretical understanding of (neural/polynomial) networks, including their expressivity, trainability, generalization properties, and inductive biases. My recent work has provided the first characterization of the generalization of this class of functions, and I have focused on the spectral bias of high-degree polynomials, highlighting how PNs can learn higher frequency functions faster than regular feed-forward networks.
- My goal is to understand the extrapolation properties of existing networks and make improvements to their performance, especially in the context of conditional generative models. In the short-term, I will continue to explore the robustness of these models to malicious attacks, as well as the impact of adversarial perturbations on different classes. In the long-term, I plan to design models that are both robust and fair, and can generalize well to unseen attribute combinations.

## News

- May 2023: The following paper has been accepted at
**Transactions on Machine Learning Research (TMLR)**: `Federated Learning under Covariate Shifts with Generalization Guarantees'. More details soon. - April 2023: The following paper has been accepted at
**ICML 2023**: `Benign Overfitting in Deep Neural Networks under Lazy Training'. - April 2023: Awarded the DAAD AInet Fellowship, which is awarded to outstanding early career researchers. Topic: generative model in ML.
- March 2023: The following paper has been accepted at
**CVPR 2023**: '*Regularization of polynomial networks for image recognition*'. - February 2023: Organizer of the tutorial on 'Polynomial Nets' in conjunction with AAAI'23: https://polynomial-nets.github.io/.
- January 2023: The following paper has been accepted at
**Transactions on Machine Learning Research (TMLR)**: '*Revisiting adversarial training for the worst-performing class*'. - December 2022: The following paper has been accepted at
**Transactions on Pattern Analysis and Machine Intelligence**: '*Linear Complexity Self-Attention with 3rd Order Polynomials*'. - October 2022: I was recognized as a
**best reviewer**at**NeurIPS 2022**. - September 2022: The following papers have been accepted at
**NeurIPS 2022**:- 'Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)',
- 'Generalization Properties of NAS under Activation and Skip Connection Search',
- 'Sound and Complete Verification of Polynomial Networks',
- '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/previous_versions/index.html.
- 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*'