13 March 2020 (13:40):  IEEE AP/MTT/EMC/ED Turkey Seminar Series (S.68)

Speaker: Assoc. Prof. Tolga Çukur, Bilkent University

Topic: "Breaking Barriers in Structural and Functional Neuroimaging Through Machine Learning"

Location: Middle East Technical University, Ankara, Turkey

Abstract: MRI offers an unrivaled opportunity to noninvasively examine the structure and function of the human brain in vivo. Yet, MRI exams are hindered by limitations on spatiotemporal resolution and contrast of acquired images as well as inefficient experimental designs. Classical approaches to reconstruction, processing and analysis of imaging data often fail to address these limitations. In this talk, I will share an overview of recent efforts in my lab to devise novel machine learning techniques in order to surpass fundamental barriers in structural and functional MRI. I will showcase examples where machine learning empowers rapid, high-quality and high-sensitivity assessments in neuroimaging.

Bio: Dr. Çukur received his B.S. degree from Bilkent University in 2003, and his Ph.D. degree from Stanford University in 2009, both in Electrical Engineering. He was a postdoctoral fellow at Helen Wills Neuroscience Institute at University of California, Berkeley till 2013. Currently, he is an Associate Professor in the Department of Electrical and Electronics Engineering, UMRAM, and Neuroscience Program at Bilkent University. His lab develops computational imaging methods for understanding the anatomy and function of biological systems in normal and disease states. He is the recipient of TUBITAK Career Award (2015), TUBA-GEBIP Outstanding Young Scientist Award (2015), BAGEP Young Scientist Award (2017), IEEE Turkey Research Encouragement Award (2017), Science Heroes Association Young Scientist of the Year Award (2017), METU Prof. Dr. Mustafa Parlar Foundation Research Incentive Award (2019), and he is a senior member of IEEE (2017).

  Date and Time




  • Ankara, Ankara
  • Turkey



Assoc. Prof. Tolga Cukur


Breaking Barriers in Structural and Functional Neuroimaging Through Machine Learning