Monika Dörfler

Monika Dörfler is a Professor in the Department of Mathematics at the University of Vienna. Her research focuses on applied harmonic analysis, audio signal processing, and the mathematics of machine learning. She specializes in time-frequency analysis, nonstationary Gabor frames, and deep learning applications in audio processing. Dörfler has contributed significantly to methods like the invertible constant-Q transform and has explored the intersection of convolutional neural networks with signal representation theory.

• Affiliations: Department of Mathematics, University of Vienna
• Roles: Professor, Privatdozent, Principal Investigator (PI) in grants including WWTF Hertha Firnberg and SALSA projects
• Teaching: Courses such as Applied Analysis and Bachelor seminar

Her research interests emphasize both theoretical foundations and practical applications of signal processing techniques. Notable contributions include work on Gabor frames in deep scattering networks and orthogonal projections for dimension reduction in learning problems. Dörfler has received awards for her papers, including Best Paper Awards at ISMIR19 and DAFx-11, and has organized international conferences like SAMPTA19 and DAFx2020.

• Awards: Best Paper Gold (DAFx-11), ISMIR19 Award, WWTF Grant Leadership
• Grants: Co-PI in aMOBY (Acoustic Monitoring of Biodiversity), PI in Audio-Miner and Hertha Firnberg grant

Her work addresses challenges in audio denoising, music information retrieval, and algorithmic biases in recommendation systems. Ongoing projects include "Filter them all! Gabor theory for image quality assessment" submitted to FWF in 2023.