NEW DIRECTIONS IN THEORETICAL NEUROSCIENCE

Dr. Burcu Ayşen Ürgen is an Associate Professor in the Departments of Psychology and Neuroscience at Bilkent University. She received her PhD in Cognitive Science from the University of California, San Diego (USA) in 2015. She completed her undergraduate studies in Computer Engineering at Bilkent University and her master’s degree in Cognitive Science at Middle East Technical University. Following the completion of her PhD, she worked as a postdoctoral researcher in the Department of Neuroscience, Faculty of Medicine, University of Parma, Italy. Dr. Ürgen’s primary research focuses on human perception and cognition, including visual, auditory, and multisensory processes. She employs behavioral methods alongside a broad range of invasive and non-invasive neuroimaging techniques—such as fMRI, EEG, and intracranial recordings—to investigate the neural basis of human perception and cognition. Her work frequently integrates state-of-the-art computational approaches, including machine learning, computer vision, and effective connectivity analyses. In addition to her fundamental research in cognitive neuroscience, Dr. Ürgen pursues interdisciplinary work at the intersection of social robotics and cognitive neuroscience, aiming to understand the human factors that enable successful interaction with artificial agents such as robots.

Bahar Güntekin, PhD (born 1977 in İzmir) is a Professor of Biophysics at Istanbul Medipol University School of Medicine, where she leads research on cognitive functions and EEG-based brain oscillatory dynamics in healthy individuals and patients with neurodegenerative disorders. She completed her PhD in Biophysics at Dokuz Eylül University in 2006, earning the Doctorate First Prize, and served as vice director of the Brain Dynamics Research Center at Istanbul Kültür University from 2006 to 2016, before she was appointed a full professor in 2016. She has chaired the ISTAART Electrophysiology Professional Interest Area of the Alzheimer’s Association. She serves on the board of the International Organization of Psychophysiology, as well as on the editorial boards of Scientific Reports, International Journal of Psychophysiology, Aging Brain, and other journals. She served on the Scientific and Technological Research Council of Turkey (TÜBİTAK) Health Sciences Advisory Board (2019–2022). Her awards include Outstanding Young Scientist from the Turkish Academy of Sciences (TÜBA) in 2015, the METU Parlar Foundation Research Incentive Award in 2016, and the Roy John Award from the EEG and Clinical Neuroscience Society in 2023. In 2023 she was elected an associate member of the Turkish Academy of Sciences.

Furkan Özçelik graduated from Istanbul Technical University in 2018 with a degree in Computer Engineering. During his master’s studies at the same university, he worked in the field of computer vision. He then pursued his doctoral studies in France at the Centre de Recherche Cerveau & Cognition (Brain and Cognition Research Center) at the University of Toulouse. There, he took part in projects at the intersection of neuro-AI and focused especially on neural decoding. Between 2023 and 2025, he worked as a postdoctoral researcher at New York University (NYU). He is currently continuing his research at Yale University. His work focuses on decoding visual representations in the brain and reconstructing these representations using deep generative models, within an interdisciplinary framework that brings together artificial intelligence and neuroscience.

Alper T. Erdogan received his B.S. degree in Electrical and Electronics Engineering from Middle East Technical University (METU) in 1993, and his M.S. and Ph.D. degrees in Electrical Engineering from Stanford University in 1995 and 1999, respectively. He worked as a Principal Research Engineer at a communications IC startup until 2002. He joined the Department of Electrical and Electronics Engineering at Koç University in 2002, where he is currently a Professor. His research interests include principled algorithms for unsupervised, self-supervised, and supervised learning in both artificial and biological neural networks; adaptive signal processing; structured matrix factorization; blind source separation and component analysis; and physical-layer communications algorithms.