Find Academic Supervisors

Adrien DoerigView profile

Visiting Professor

• Computational Neuroscience
• Cognitive Neuroscience
• Machine Learning
• +5 more

Adrien Doerig is a Visiting Professor in the Department of Education and Psychology at Freie Universität Berlin, where he leads research in the Cognitive Computational Neuroscience Lab. He is also affiliated with the Bernstein Center for Computational Neuroscience. His work bridges cognitive science, computational modeling, and neural mechanisms of perception. Doerig's research focuses on the intersection of computational neuroscience and artificial intelligence, with particular emphasis on visual perception, consciousness, and the development of biologically plausible neural network models. His work explores how the brain processes visual information, with special attention to phenomena like crowding, feature integration, and the temporal dynamics of perception. He has made significant contributions to understanding the limitations of convolutional neural networks for modeling human vision and has pioneered work on topographic neural networks that better capture cortical organization. A key trend in Doerig's recent publications is the exploration of connections between language models and visual processing in the brain. His groundbreaking 2025 Nature Machine Intelligence paper demonstrated that large language model representations align closely with visual representations in the human brain, opening new avenues for using language-based AI in modeling biological visual processing. His work consistently challenges existing paradigms while developing more biologically plausible computational frameworks. Doerig teaches a wide range of courses including Master's in Cognitive Neuroscience, Probability and statistical modeling (both theoretical and practical with focus on ANNs and neuroimaging), Introduction to programming, Applied computational cognitive neuroscience, and specialized courses on vision, language, affective & social neuroscience, and consciousness. He also supervises graduate students through the Cognitive Computational Neuroscience Lab, which includes postdocs, predocs, master's students, and interns working on cutting-edge research questions at the intersection of cognitive science and machine learning.

• Computational Neuroscience
• Neurophysics
• Dynamical Systems
• +4 more

Prof. Dr. Klaus R. Pawelzik is a Professor at the University of Bremen's Institute for Theoretical Physics, where he leads the Theoretical Bio- and Neurophysics research group. His research bridges theoretical physics and neuroscience, with laboratories located in the Cognium building on the university campus. His primary research interests include: Computational models of neural dynamics and information processing Neurophysics and mechanisms of cortical computation Brain-computer interfaces and neuroprosthetic systems Dynamical systems approaches to causality and network analysis Biologically plausible learning algorithms for spiking neural networks Attention mechanisms and sensory processing in primate brains Recent publications (2015-2020) demonstrate strong focus on attention mechanisms in visual processing, causal inference methods for dynamical systems, hardware implementations for neuroprosthetics, and biologically inspired learning algorithms. The work consistently integrates mathematical rigor with experimental neuroscience, featuring collaborations with neurophysiology labs and engineering groups. Prof. Pawelzik leads an active research team developing both theoretical frameworks and experimental platforms for neuroscience research. The lab specializes in open-source neurotechnology solutions, including wireless implantable devices for electrocorticography and FPGA-based processing systems for real-time neural signal analysis.

Naoki HirataniView profile

Assistant Professor

• Neuro-AI
• Theoretical Neuroscience
• Computational Neuroscience
• +3 more

Naoki Hiratani serves as Assistant Professor of Neuroscience in the Department of Neuroscience at Washington University in St. Louis School of Medicine. His research bridges theoretical neuroscience and artificial intelligence to decipher the brain's efficiency in learning and computation. Education PhD, The University of Tokyo (2011-2016) BS, The University of Tokyo (2007-2011) Research Focus : Dr. Hiratani pioneers Neuro-AI investigations into how micro/macro connectivity patterns and complex learning mechanisms enable the brain's superior energy efficiency and data-efficient learning compared to modern AI. His work employs advanced machine learning algorithms for neural data analysis, targeting brain-inspired AI development through theoretical modeling of sensory processing, olfactory systems, and neural circuit dynamics. Publication Trends : His 2015-2023 publications reveal consistent focus on biologically plausible learning (node perturbation, STDP), neural circuit modeling (olfaction, reservoir computing), and Bayesian neural computation . Recent work emphasizes individual variability analysis and task-order optimization for continual learning. Awards Swartz Foundation Postdoctoral Fellowship (2020-2023) Japan Science Promotion Society Doctoral Fellowship (2014-2016) Top Reviewer, ICML 2025 Mentorship & Lab : Leading the Hiratani Laboratory (Neuro-AI Research Lab), he actively recruits graduate students and postdocs. Current rotation students Jeeyoung and Liangyu contribute to projects including zebrafish brain development (with Goodhill Lab) and International Brain Laboratory data analysis. The lab recently accepted work on task-order optimization for ICML 2025.

• Machine Learning
• Deep Learning
• Computer Vision
• +6 more

Yann André LeCun is the Jacob T. Schwartz Professor of Computer Science, Data Science, Neural Science, and Electrical and Computer Engineering at New York University, and serves as Chief AI Scientist at Meta. He holds appointments across multiple NYU institutions including the Courant Institute of Mathematical Sciences, the Center for Data Science, the Center for Neural Science, and the Tandon School of Engineering. LeCun leads the CILVR Lab (Computational Intelligence, Learning, Vision, Robotics) at NYU and is a key figure in Meta's FAIR (Fundamental AI Research) organization. LeCun's research spans machine learning, deep learning, computer vision, robotics, and computational neuroscience. He pioneered convolutional neural networks in the 1980s-90s, which became foundational to modern AI. His recent work focuses on self-supervised learning, energy-based models, and developing architectures for predictive world models that could enable machines to understand and interact with the physical world. LeCun advocates for open-source AI development through projects like Meta's Llama language models. His publication record shows consistent high-impact contributions since the 1980s, with recent work emphasizing self-supervised learning approaches like Joint Embedding Predictive Architectures (JEPA). The 15 most recent publications reveal a strong focus on representation learning, world models, and efficient learning paradigms that reduce reliance on massive labeled datasets. ACM Turing Award (2018) Princess of Asturias Award for Technical and Scientific Research (2022) Member of US National Academy of Engineering (2017) Member of US National Academy of Sciences (2021) Foreign Member of Académie des Sciences, France (2022) Queen Elizabeth Prize for Engineering (2025) VinFuture Grand Prize (2024) LeCun has advised approximately 30 PhD students who now lead AI research at major institutions worldwide. His lab has received significant funding from both government agencies and industry partners to advance fundamental AI research. The CILVR Lab fosters interdisciplinary collaboration across computer science, neuroscience, and engineering disciplines to tackle core challenges in artificial intelligence. LeCun actively engages with policymakers on AI governance, advocating for open research and targeted regulation. His work on open-source AI models represents a strategic approach to democratizing AI development while maintaining safety through community scrutiny. LeCun continues to push the boundaries of what machines can learn and understand about the physical world.

• Spiking Neural Networks
• Neuromorphic Information Processing
• Artificial Neural Networks
• +7 more

Professor Martin Bogdan serves as a Professor for Neuromorphic Information Processing at the University of Leipzig within the Faculty of Mathematics and Computer Science. His research spans multiple interdisciplinary domains including spiking neural networks, neuromorphic computing, and medical signal processing applications. University: University of Leipzig School: Faculty of Mathematics and Computer Science Department: Department of Neuromorphic Information Processing Office: Augustusplatz 10, Room P 531, 04109 Leipzig Contact: +49 (341) 97-32208 | bogdan@informatik.uni-leipzig.de Professor Bogdan completed his doctorate at the University of Tübingen in 1998 with research focused on signal processing of biological nerve signals for controlling prostheses using artificial neural networks. His educational background includes communications engineering at Offenburg University of Applied Sciences and studies at Université Grenoble I Joseph Fourier. Professor Bogdan's research interests center on neuromorphic information processing, with particular emphasis on spiking neural networks (SNNs), neurological plausible learning algorithms, and the incorporation of dynamics within synaptic efficiency. His work bridges computational neuroscience with practical applications in medical diagnostics, embedded systems, and real-time signal processing. His team has made significant contributions to understanding consciousness in locked-in syndrome patients through EEG analysis and developing innovative approaches to agricultural quality control using hyperspectral imaging and neural networks. The research demonstrates a consistent trajectory from foundational neural network architectures toward increasingly sophisticated neuromorphic computing applications. Analysis of Professor Bogdan's recent publication record reveals a strong focus on advancing spiking neural network architectures, particularly liquid state machines and synaptic dynamics models. His work increasingly bridges theoretical neuroscience with practical applications in medical diagnostics (particularly consciousness assessment in locked-in syndrome patients) and agricultural technology (using hyperspectral imaging for seed purity analysis). A notable emerging theme is the exploration of cognitive concepts like boredom in artificial intelligence systems, suggesting expanding interest in higher-order cognitive modeling within neuromorphic computing frameworks. Professor Bogdan leads the Department of Neuromorphic Information Processing at the University of Leipzig, supervising numerous PhD students and postdoctoral researchers. His team includes researchers specializing in various aspects of neural networks, signal processing, and applications in medical and agricultural domains. The department maintains active collaborations with medical institutions for clinical applications of their research, particularly in the area of brain-computer interfaces and consciousness assessment.