Archives

Dr.Öğretim Üyesi Hasan Ayaz Derexel Üniversitesi Pennsylvania Üniversitesi   Tarih/Zaman: Tuesday, October 25th, 5:00 pm Zoom Meeting ID: 912 6646 5546 (Passcode: 896970)   https://zoom.us/j/91266465546?pwd=WVlJSHMyQU5TM3g2MUxZT1laUWhUdz09   Özet: The understanding of the brain functioning and its utilization for real world applications is the next frontier. Existing studies with traditional neuroimaging approaches have accumulated overwhelming knowledge but are […]

Konuşmacı: Taghi Khaniyev, Bilkent Üniversitesi Tarih ve Zaman: 3 Ağustos, 2022, Çarşamba 12:00. Yer: EA-409   Özet: Connectome is the representation of the structural connectivity (the physical wiring) and functional connectivity (interactions between different brain regions) of an organism’s nervous system as a mathematical network (a set of nodes and the connections between them). Recent […]

Hibrit Seminer: Canlı olarak Zoom’da ve aynı zamanda UMRAM Seminer Odasında   Zoom Link:  https://zoom.us/j/3750698016?pwd=Vk14RndrYnRBYkJQT0Y1bW5hWFpXUT09 ID: 375 069 8016 Pass: 086558 Tarih/Zaman: Cuma, 3 Haziran 2022, 13:30-14:30   Konuşmacı: Ekin Karaşan, PhD Candidate, University of California, Berkeley Özet: Displacement Spectrum Imaging (DiSpect) is a new method for probing complex tissue dynamics such as blood flow, diffusion and […]

Professor Bennett A. Landman Vanderbilt University   Tarih/Zaman: Tuesday, April 26th, 4:00 pm Zoom Meeting ID: 914 5092 0667 (Passcode: 896970) https://zoom.us/j/91450920667?pwd=VjlFc1pSRXliaGdmZzJIS2xqNEFyUT09   Özet: Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many national-scale studies. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency. I will discuss successful applications of deep […]

Dr. Saiprasad Ravishankar Michigan State University Tarih/Saat: Friday, March 25th, 4:30 pm Zoom Meeting ID: 945 8994 6946 (Passcode: 896970) https://zoom.us/j/94589946946?pwd=MDJtU0R6RXhUdk1kU2hTUG1CSWNydz09 Özet: In this talk, we present approaches for learning sparsity-based and deep neural network-based regularizers for inverse problems in imaging. Our methods allow learning robust models from very limited training data. We will first […]