Martina Profile Photo

Martina Cardone

Associate Professor
Keller Hall; 200 Union St. SE
Minneapolis, MN 55455
Phone: 612-625-1242
 
 
 

 

Research Interests

The primal goal of my research is two-fold: (i) develop theoretical frameworks that offer engineering guidelines and insights for the study of practically relevant problems and (ii) design of computationally feasible techniques aimed to achieve the fundamental theoretical performance limits as close as possible. Examples of topics that have been investigated include the design of: (i) high data rates transmission schemes for 5G heterogeneous/cognitive networks; (ii) low-complexity scheduling and selection algorithms for half-duplex relay networks; (iii) privacy-preserving data publishing techniques; (iv) secure communication strategies to limit network vulnerabilities; (v) distributed caching schemes that leverage probabilistic social interactions to realize bandwidth savings. The study of such topics touches upon properties and concepts from several research fields, such as information theory, optimization, network coding and algorithms. For more information about my research, please see my publications.

 

Note for Prospective Students: I am looking for motivated students to join my research group. If you are interested in joining my research group, please send me an email along with your CV explaining your research interests and background.

News

April 24, 2026

I gave a seminar titled "Focal Loss: An Information-Theoretic Perspective" at Arizona State University. Thank you Prof. Nicolò Michelusi for inviting me!

January 29, 2026

Mohammad passed his preliminary written exam!

January 22, 2026

Our paper "Functional Properties of the Focal-Entropy" has been accepted at AISTATS 2026!

September 19, 2025

I gave a talk titled "Focal Loss: Statistical and Information-Theoretic Perspectives" at Allerton 2025. Thank you Prof. Ilan Shomorony and Prof. Lav Varshney for inviting me!

September 17, 2025

Our paper “A Reinforcement Learning Based Hybrid Scheduling Mechanism for mmWave Networks” has been accepted for publication in the IEEE Transactions on Machine Learning in Communications and Networking.