I’m a postdoctoral researcher in the Algorithms, Data Structures and Foundations of Machine Learning research group, led by Prof. Kasper Green Larsen at Aarhus University (Department of Computer Science).

I did my PhD at the Inria centre at Université Côte d’Azur (completed in 2023) under the supervision of Dr. Emanuele Natale.

I’m on the 2025 job market.

Interests/Experience

I’m interested in understanding large, complex systems, typically through probabilistic methods. Currently, my research focuses on the theory of machine learning, particularly (statistical) learning theory and deep learning. I’m also interested in the empirical aspects of many fields, ranging from analogue computing to high-performance computing. In particular, I have a taste for coding; at all levels: From algorithm design to low-level optimisations.

I hold a major in mathematics and a master’s degree in combinatorics from Universidade Federal do Ceará. There, I worked with Prof. Fabricio Benevides on extremal and probabilistic combinatorics. I took part in a neuromorphic computing project while working at Hewlett Packard Enterprise.

Publications

• da Cunha, A., Høgsgaard, M.M., Paudice, A., Sun, Y., 2025. Revisiting Agnostic Boosting. Conference on Neural Information Processing Systems (NeurIPS). ArXiv

• da Cunha, A., Høgsgaard, M.M., Larsen, K.G., 2024. Optimal Parallelization of Boosting. (Oral presentation) In Conference on Neural Information Processing Systems (NeurIPS). ArXiv

• da Cunha, A., Larsen, K.G., Ritzert, M., 2024. Boosting, Voting Classifiers and Randomized Sample Compression Schemes. In International Conference on Algorithmic Learning Theory (ALT). ArXiv

• da Cunha, A., d’Amore, F., Natale, E., 2023. Convolutional neural networks contain structured strong lottery tickets. In Conference on Neural Information Processing Systems (NeurIPS). OpenReview

• da Cunha, A.C.W., d’Amore, F., Giroire, F, Lesfari, H., Natale, E., Viennot, L., 2023. Revisiting the Random Subset Sum problem. European Symposium on Algorithms (ESA). LIPIcs

• da Cunha, A., Natale, E., Viennot, L., 2022. Proving the Strong Lottery Ticket Hypothesis for Convolutional Neural Networks. In International Conference on Learning Representations (ICLR). OpenReview

• Becchetti, L., da Cunha, A.C.W., Clementi, A., d’Amore, F., Lesfari, H., Natale, E., Trevisan, L., 2022. On the Multidimensional Random Subset Sum Problem. Preprint. ArXiv

• da Cunha, A.C.W., Natale, E., Viennot, L., 2023. Neural Network Information Leakage Through Hidden Learning. In International Conference on Optimization and Learning (OLA). HAL

Patents

• Da Cunha, A.C.W., Natale, E., Viennot, L., Institut national de recherche en sciences et technologies du numérique, 2022. Résistance équivalente modulable à partir de résistances imprécises (programmable equivalent resistances from imprecise resistors). France Patent deposit n°FR2210217.

• Ambrosi, J.C., Da Cunha, A.C.W., Cavalcante, J.R.A., Hewlett Packard Enterprise Development LP, 2021. System for a flexible conductance crossbar. U.S. Patent 11,200,948. Google Patents

• Aguiar, G.D.S., Silveira, F.P., Lee, E.S., Antunes, R.J.D.R., Souza, J.G.D.C.E., Foltin, M., Cavalcante, J.R.A., Leite, L., Cunha, A.C.W.D., Frazao, M.V., Trajano, A.F.R., Hewlett Packard Enterprise Development LP, 2022. SYSTEM AND METHOD FOR PROCESSING CONVOLUTIONS ON CROSSBAR-BASED NEURAL NETWORK ACCELERATORS FOR INCREASED INFERENCE THROUGHPUT. U.S. Patent 12,254,395 B2. Google Patents

Peer-review service

• Reviewer for SEA21, AAAI22, ICLR22, NeurIPS23, STOC24, NeurIPS24, SODA25, ICLR25 (invited), AISTATS25 (invited), NeurIPS25