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Lissy Pellaco

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About me

Lissy Pellaco received summa cum laude the M.Sc. degree in Multimedia Signal Processing and Telecommunication Networks from the University of Genoa, Italy, in 2016. After an international assignment on Industrial Automation Networks at ABB in Cleveland, Ohio, she is currently enrolled in a Ph.D. program in Machine Learning applied to Radio Networks at KTH, the Royal Institute of Technology in Stockholm, Sweden. She is also affiliated to the Wallenberg AI, Autonomous Systems and Software Program (WASP), a research program based on the interaction between Sweden's ICT universities and major technological companies, such as Sony, Ericsson, and Saab.

In her Ph.D. program, Lissy Pellaco is investigating the merger of traditional parameterized and machine learning-based approaches to wireless communications, both satellite and terrestrial. She is also a reviewer for IEEE Transactions on Signal Processing, IEEE Transactions on Communications, IEEE Communications Letters, ICML 2020-21, NeurIPS 2020-21, ICLR 2022. Since February 2019, she is part of the coordination group of the Female PhD Student Network at the School of Electrical Engineering and Computer Science in KTH.

In June 2021, she received the Outstanding Student Paper Award at conference ICASSP. In July 2019, she attended the 36th edition of the Machine Learning Summer School (https://sites.google.com/view/mlss-2019). In December 2018, she received the graduate award issued by the Italian Association of Electrical, Electronics, Automation, Information and Communication Technology (AEIT) to the most promising graduate in ICT Engineering. In April 2018, she was awarded the Excellence grant by the Executive Committee of the Doctoral Program council of the school of  Electrical Engineering and Computer Science at KTH. Also, in 2013 she was the recipient of the “ASING” and “IRIS INGEGNERIA” awards issued by the University of Genoa to the most brilliant first-year B.Sc. students.


Courses

Discrete Time Signals and Systems (EQ1120), assistant | Course web