Prof. Adali honored as UMBC Presidential Research Professor

Every year, UMBC honors a faculty member with the Presidential Research Professor Award. CSEE professor Tülay Adali was selected this year as UMBC's Presidential Research Professor for 2024-2027.

Professor Adali received the Ph.D. in electrical engineering from North Carolina State University, Raleigh, NC, in 1992 and joined the UMBC’s faculty the same year.  In 2015, she was named a Distinguished University Professor in recognition of her outstanding contributions to statistical signal processing and machine learning and excellence in teaching and mentoring the next generation of engineers and scholars who continue to advance the field of signal processing and machine learning.

Dr. Adali's commitment to her professional community is evident in her diverse roles and responsibilities. She currently serves as Editor-in-Chief of the prestigious IEEE Signal Processing Magazine and has held significant positions such as the Chair of the IEEE Brain Technical Community and the IEEE Signal Processing Society Vice President for Technical Directions from 2019 to 2022. Her professional involvement includes the organization of many conferences and workshops, including the IEEE International Conference on Acoustics, Speech, and Signal Processing, where she has served in various capacities.

Professor Adali is a Fellow of the IEEE, AIMBE, and AAIA, a Fulbright Scholar, and an IEEE SPS Distinguished Lecturer. She has received the SPS Meritorious Service Award, Humboldt Research Award, IEEE SPS Best Paper Award, the SPIE Unsupervised Learning and ICA Pioneer Award, the University System of Maryland Regents' Award for Research, and the NSF CAREER Award.

Her current research interests are in the areas of statistical signal processing, machine learning, and applications in medical image analysis and fusion.

Dr. Adali leads the Machine Learning for Signal Processing laboratory, and primarily with support from the NSF and the NIH, she and her research associates, research students, and network of collaborators develop theory and tools for processing signals that arise in today's growing array of applications and pose challenges for traditional signal processing techniques with a focus on medical image analysis and fusion.