Irina Arévalo holds a PhD in Mathematics from Universidad Autonoma de Madrid and a PhD in Artificial Intelligence from Universidad Pablo de Olavide, and is currently an Assistant Professor at CUNEF. She specialized in Complex Analysis and Operator Theory and has taught graduate-level courses in mathematical analysis in Mathematics, Physics and Engineering degrees. After her PhD she worked as a Data Scientist, Researcher in Artificial Intelligence, and Analytics Manager in several industries, such as banking and tech.

Her current research interest are Distributed Machine Learning, especifically in Federated Learning. Her work has focused on new aggregations to build the federated global model, new encryption methods to ensure the security of the federated system, methodologies to federate Fuzzy Cognitive Maps, and explainability. Irina has published more than 10 papers in Complex Analysis, Applied Statistics, and Artificial Intelligence.

Positions held

Postdoctoral

• [September 2023-] Assistant Professor of Computer Science and Artificial Intelligence at CUNEF University, Madrid, Spain.


• [July 2022-September 2023] Analytics Manager at Lookiero, Bilbao, Spain.


• [April 2020 - June 2022] AI Researcher and Senior Data Scientist at ING, Madrid, Spain.


• [July 2018 - April 2020] AI Researcher and Data Scientist at BBVA, Madrid, Spain.


• [February 2018 - July 2018] Senior Data Scientist Consultant at Kernel Analytics, Madrid, Spain.


• [July 2017 - February 2018] Data Scientist at Vueling, Barcelona, Spain.

Predoctoral

• [January 2014 - July 2017] Teaching Assistant at Universidad Autonoma de Madrid, Madrid, Spain.

Research

Research Interests

Distributed Artificial Intelligence, Federated Learning, eXplainable Artificial Intelligence, Causal Machine Learning

Publications

Selected papers

• J. L. Salmeron and I. Arevalo (2024). Concurrent vertical and horizontal federated learning with fuzzy cognitive maps. Future Generation Computer Systems 107482.


• J. L. Salmeron and I. Arevalo (2024). Blind Federated Learning without initial model. Journal of Big Data 11:56.


• I. Arévalo and J.L. Salmeron (2024). A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets. IEEE Transactions on Emerging Topics in Computing 12(1), 357-367.


• J.L. Salmeron, I. Arévalo, and A. Ruiz-Celma (2023). Benchmarking federated strategies in Peer-2-Peer Federated Learning for biomedical data. Heliyon 6(6), E16925.


• I. Arévalo and D. Vukotić (2020). Weighted composition operators in functional Banach spaces: an axiomatic approach. Journal of Spectral Theory 10 (2), 673-701.


• I. Arévalo, MD Contreras, and L Rodríguez-Piazza (2019). Semigroups of composition operators and integral operators on mixed norm spaces. Revista Matemática Complutense 32, 767-798.


• I. Arévalo, R Hernández, MJ Martín, and D Vukotić (2018). On weighted compositions preserving the Carathéodory class. Monatshefte für Mathematik 187 (3), 459-477.


• I. Arévalo (2015). A characterization of the inclusions between mixed norm spaces. Journal of Mathematical Analysis and Applications 429 (2), 942-955.

Selected conference proceedings

• I. Arevalo, J. L. Salmeron, and I. de la Oliva. Privacy-Preserving Secure Distributed Computer Vision for Malaria Cells Detection, 2024 2nd International Conference on Federated Learning Technologies and Applications (FLTA), September 17 - 20, 2024, Valencia (Spain)


• I. Arevalo, J. L. Salmeron, and I. Romero. Privacy‐preserving distributed learning with chaotic maps, 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), May 23 - 24, 2024, Madrid (Spain)


• J. L. Salmeron and I. Arevalo. A privacy-preserving, distributed and cooperative FCM-based learning approach for Cancer Research, International Joint Conference on Rough Sets, June 29 - July 3, 2020, La Habana (Cuba)

Teaching

• [Spring 2025] Programming projects, 2nd year Degree in Computer Science, CUNEF.


• [Fall 2024] Fundamentals of Data Analysis and Research, MsC Data Science, CUNEF.


• [Fall 2024] Neural Networks and Deep Learning, 4th year Degree in Mathematical Engineering, CUNEF.


• [Fall 2024] Programming projects, 3rd year Degree in Computer Science and Business, CUNEF.


• [Spring 2024] Mathematics II, 1st year Degree in Business, CUNEF.


• [Spring 2024] Programming in R, 1st year Degree in Law and Business, CUNEF.


• [Fall 2023] Intro to Programming - Programming in R, 1st year Degree in Data Science and Business, CUNEF.


• [Fall 2023] Programming projects, 3rd year Degree in Computer Science and Business, CUNEF.


• [Fall 2016] Calculus I, 1st year Degree in Mathematics and Computer Science, UAM.


• [Spring 2016] Mathematical Analysis II, 1st year Degree in Physics, UAM.


• [Fall 2014] Mathematical Analysis I, 1st year Degree in Communications Engineering, UAM.

Students

• [Spring 2024] Marina del Hoyo, Master Thesis titled "Recommendation Systems in E-Commerce: An Application for a Digital Retail Business", CUNEF.


• [Spring 2024] Ángel Blanco, Master Thesis titled "Efecto de las técnicas de balanceo en problemas de clasificación desequilibrados", CUNEF.