Fabiano Locatelli was born in 1991 in Bergamo (Italy). He received both the B.Sc. and the M.Sc. degree in Telecommunication Engineering from Politecnico di Milano (Italy) in 2014 and 2017, respectively. He is currently pursuing a Ph.D at the Polytechnic University of Catalunya, UPC.

During 2017, as a visitor, he spent 9 months in the Optical Network and System Department of Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), in order to work on his master thesis, titled: "Modelling direct modulating lasers and VCSELs based modules for programmable transmitters in future optical metro networks".

In May 2018 he joined CTTC as an Early Stage Researcher in the framework of the Horizon 2020 Marie Sk?odowska-Curie Actions, ONFIRE (Future Optical Networks for Innovation, Research and Experimentation) project.

His current research interests include data plane technologies for
disaggregated optical networks: suitable transmission schemes enhanced thanks to white boxes approach and flexible paradigm, including system margins reduction and new optical performance monitoring techniques agnostic to the signal waveform.

Ankush Mahajan received Bachelor’s degree in Electronic and Communication Engineering from S.V.I.T.S Indore, India and M.Tech degree from Indian Institute of Technology (ISM), Dhanbad, India. After M.Tech, he joined Centre of Excellence group at Sterlite Tech Limited, Aurangabad in September 2015. In Sterlite Tech, he was associated with long haul transmission system and access network research group. He was responsible for testing and characterization of various type of optical fibers, especially G.652.D and G.657.A2 in Terabit Transmission system and FTTH laboratories of Sterlite Tech.  

In September 2018, he joined CTTC as an Early Stage Researcher (ESR-2) for ONFIRE (Future Optical Networks for Innovation, Research and Experimentation) project under the framework of the Horizon 2020 Marie Sklodowska-Curie Actions. In ONFIRE, he is particularly working on advance Machine Learning (ML) algorithms and mechanisms to estimate or predict quality of transmission (QoT) services, failure and anomaly detection etc. to enable end-to-end services with efficient resource utilization. These ML models will be a part of feasible cognitive architecture operating on top of a SDN controlled / orchestrated infrastructure.