Guest Lecture: Angel Lozano
December 06, 2024
12:30 p.m. - 1:30 p.m. CAT
A203
December 06, 2024
12:30 p.m. - 1:30 p.m. CAT
A203
An Academic's Perspective on 6G Research
Angel Lozano, professor at Universitat Pompeu Fabra (Barcelona)
Angel Lozano is a professor at Universitat Pompeu Fabra (UPF) in Barcelona, Spain. He earned a Ph.D. in electrical engineering from Stanford University in 1998 and then joined Bell Labs in 1999, where he remained until 2008. Lozano served as an adjunct associate professor at Columbia University from 2005 to 2008 and became an IEEE Fellow in 2014. He has held various editorial roles in IEEE journals and chaired the IEEE Communication Theory Technical Committee (2013-2014). Lozano has published extensively, holds 15 patents, and co-authored the textbook "Foundations of MIMO Communication." He has received multiple awards for his papers, held an ERC Advanced Grant from 2016-2021, was recognized as a Highly Cited Researcher in 2017, and is a Distinguished Lecturer of the IEEE for 2023-2025
With the 6th generation of wireless networks on the horizon, the time is right to reflect on what has been accomplished, on old and new challenges, on the research tools at our disposal, and on the evolution of wireless communications. This lecture intends to contribute to this reflection, not from the perspective of a manufacturer, operator, or user, but from a researcher's perspective. With this perspective, the presentation is organized around various ideas that relate to 6G, but that also have broader conceptual implications.
December 5 2025
10:00 AM - 3:00 PM CAT
Carnegie Mellon University Africa
NBA Africa Triple-Double Accelerator
CMU-Africa partners with NBA Africa as the venue host for the second annual Triple-Double Accelerator Demo Day.
CMU-Africa, PEZ Building
December 5 2025
6:00 PM - 8:00 PM CAT
Carnegie Mellon University Africa
F203
December 11-17 2025
Carnegie Mellon University Africa
2025 African - Inclusive Digital Industries School Program
This program offers an intensive learning experience designed to deepen knowledge and skills in key areas such as artificial intelligence and machine learning applied to material engineering, digital manufacturing, and optimization techniques.