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Mondelli Group

Data Science, Machine Learning, and Information Theory

We are at the center of a revolution in information technology, with data being the most valuable commodity. Exploiting this exploding number of data sets requires to address complex inference problems, and the Mondelli group works to develop mathematically principled solutions.

These inference problems span different fields and arise in a variety of applications coming from engineering and natural sciences. In particular, the Mondelli group focuses on wireless communications and machine learning. In wireless communications, given a transmission channel, the goal is to send information encoded as a message while optimizing for certain metrics, such as complexity, reliability, latency, throughput, or bandwidth. In machine learning, given a model for the observations, the goal is to understand how many samples convey sufficient information to perform a certain task and what are the optimal ways to utilize such samples. Both the vision and the toolkit adopted by the Mondelli group are inspired by information theory, which leads to the investigation of the following fundamental questions: What is the minimal amount of information necessary to solve an assigned inference problem? Given this minimal amount of information, is it possible to design a low-complexity algorithm? What are the fundamental trade-offs between the parameters at play (e.g., dimensionality of the problem, size of the data sample, complexity)?


Current Projects

Fundamental limits and efficient algorithms for deep learning | Non-convex optimization in high-dimensions | Optimal code design for short block lengths


Depope A, Mondelli M, Robinson MR. 2024. Inference of genetic effects via approximate message passing. 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP: International Conference on Acoustics, Speech and Signal Processing, 13151–13155. View

Esposito AR, Mondelli M. Concentration without independence via information measures. IEEE Transactions on Information Theory. View

Súkeník P, Mondelli M, Lampert C. Deep neural collapse is provably optimal for the deep unconstrained features model. 37th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, NeurIPS, . View

Bombari S, Kiyani S, Mondelli M. 2023. Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 2738–2776. View

Shevchenko A, Kögler K, Hassani H, Mondelli M. 2023. Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 31151–31209. View

View All Publications

ReX-Link: Marco Mondelli


Since 2019 Assistant Professor, Institute of Science and Technology Austria (ISTA)
2017 – 2019 Postdoc, Stanford University, Stanford, USA
2018 Research Fellow, Simons Institute for the Theory of Computing, Berkeley, USA
2016 PhD, EPFL, Lausanne, Switzerland 

Selected Distinctions

2019 Lopez-Loreta Prize
2018 Simons-Berkeley Research Fellowship
2018 EPFL Doctorate Award
2017 Early Postdoc Mobility Fellowship, Swiss National Science Foundation
2016 Best Paper Award, ACM Symposium on Theory of Computing (STOC)
2015 Best Student Paper Award, IEEE International Symposium on Information Theory (ISIT)
2015 Dan David Prize Scholarship

Additional Information

Download CV
View Marco Mondelli’s website

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