Find Academic Supervisors

• Spatial Navigation
• Memory Systems
• Neural Circuits
• +4 more

Fabian Kloosterman is a Lecturer at KU Leuven's Laboratory for Biological Psychology, with additional affiliations at the KU Leuven Brain Institute (LBI) and the KU Leuven Institute for Integration of Micro- and Nano-scale Technologies (LIMNI). His research focuses on the neural mechanisms underlying spatial navigation and memory processes. His research interests center on understanding brain circuit function in spatial navigation and memory. Using advanced electrophysiological and biochemical approaches in rodent models, Kloosterman investigates hippocampal-prefrontal interactions, spatial coding mechanisms along neural axes, and the role of neural oscillations in cognitive processes. His work bridges systems neuroscience with technological innovation in neural recording methodologies. Analysis of his recent publications reveals a strong focus on spatial coding mechanisms, particularly in the hippocampus and connected structures. His research employs high-density neural probes and optogenetic techniques to investigate how brain circuits process spatial information and support memory functions. A recurring theme involves examining the relationship between neural oscillations (particularly theta rhythms and sharp-wave ripples) and cognitive processes. Kloosterman actively supervises doctoral candidates, with several students completing PhD theses under his guidance on topics related to spatial coding, memory systems, and neural circuit function. His research is supported by multiple active grants through KU Leuven's research portal, including projects investigating cognitive flexibility, retrosplenial cortex function, and hippocampal-prefrontal interactions. He leads research within the Laboratory for Biological Psychology, focusing on neural circuit mechanisms of cognition. His team employs cutting-edge electrophysiological techniques combined with behavioral paradigms to investigate how distributed brain networks support spatial navigation and memory processes.

• Medical Image Segmentation
• Deep Learning
• Ultrasound Imaging
• +3 more

Mohamed Khaled Almekkawy is an Assistant Professor in Computer Science and Engineering , specializing in medical imaging and deep learning applications. His work bridges computational methods with healthcare diagnostics, focusing on advanced neural architectures for image segmentation and analysis. Research Interests : Medical Image Segmentation, Deep Learning, Ultrasound Imaging, Physics-informed Neural Networks, Transformers, and Hybrid Models. Recent Publications highlight trends in medical imaging, particularly leveraging transformers, Mamba models, and triplet encoder architectures to enhance segmentation accuracy and efficiency. His research also explores ensemble learning for ultrasound localization microscopy and 3D microbubble tracking. Collaboration Networks show strong ties to biomedical engineering, ultrasound tomography, and computer vision communities. His work contributes to the UN Sustainable Development Goals through advancements in healthcare technology.

Fabian DombrowskiView profile

Researcher

• Digital History
• Digital Humanities
• Cultural Studies
• +6 more

Fabian Dombrowski is a Researcher at the Educational Media Information Center (IZBM) of the Leibniz Institute for Educational Media (GEI) since 2023. He contributes to projects like The Evolution of the Textbook (MWK) and a DFG-funded initiative on Digitization and Indexing of Historical Textbooks focusing on religious education. Previously, he worked on the DFG OCR4all project. Education: MA in Latin, European Ethnology, and History (with Digital History focus) from Humboldt University Berlin Research Focus: Digital methods for historical analysis, knowledge systems' evolution, and materiality of recording techniques (card indexes, textbooks, databases) Technical Expertise: Named Entity Recognition (NER), Named Entity Disambiguation (NEL), OCR technologies, and network modeling for reconstructing historical relationships His publications emphasize interdisciplinary approaches combining Digital Humanities with History of Knowledge , featuring projects like SchulbuchEvolution and DigiRel . He actively presents at conferences on topics like Textbook Digitization and Prosopographic Data Mining , while contributing to educational datasets on historical German textbooks.

• Computational Vision
• Visual Attention
• Perceptual Learning
• +5 more

Miguel Eckstein is a Distinguished Professor at UC Santa Barbara with joint appointments in the Department of Psychological & Brain Sciences (College of Letters & Science) and the Department of Electrical and Computer Engineering (College of Engineering). He earned a Bachelor's in Physics and Psychology from UC Berkeley and a PhD in Cognitive Psychology from UCLA. His career includes prior positions at Cedars Sinai Medical Center and NASA Ames Research Center. Eckstein leads pioneering research in computational human vision, integrating behavioral psychophysics, eye tracking, EEG, fMRI, and computational modeling to study: Neural mechanisms of visual perception, attention, and learning Medical image perception for clinical diagnostics Bio-inspired computer vision systems Human-robot interaction optimization His work bridges fundamental cognitive neuroscience with applied engineering solutions. Publication analysis reveals interdisciplinary contributions spanning neuroscience, computer vision, medical imaging, and psychology. Recent work (2017-2021) emphasizes neural decoding of visual processes, 3D medical imaging limitations, human-AI perceptual comparisons, and crowd-sourced visual intelligence. Awards and honors include: Guggenheim Fellowship (2019) National Academy of Sciences Troland Award NSF CAREER Award SPIE Image Perception Cum Laude Award Optical Society of America Young Investigator Award He directs the Vision and Image Understanding Lab and co-founded the Mellichamp Initiative in Mind & Machine Intelligence, fostering cross-disciplinary AI research. Grant activities support medical imaging perception, neural computation, and human-machine collaboration projects.

• Neuroscience
• Anesthesiology
• Critical Care
• +4 more

Joseph Cichon, MD, PhD, serves as Assistant Professor of Anesthesiology and Critical Care at the Perelman School of Medicine, University of Pennsylvania, where he directs the In Vivo Imaging Center for Anesthetics and Psychedelics. His dual clinical expertise in neuroanesthesia and research focuses on neural circuit mechanisms underlying rapid-acting antidepressants. His educational background includes: BS in Pennsylvania State University, 2008 MS in Pennsylvania State University, 2009 PhD in Neuroscience & Physiology, NYU School of Medicine, 2015 MD, NYU School of Medicine, 2017 Dr. Cichon investigates how anesthetics and psychedelics like ketamine and nitrous oxide produce rapid antidepressant effects through modulation of prefrontal cortical circuits. His work combines in vivo imaging , electrophysiology , and rodent behavioral models to decode cellular mechanisms of neural plasticity, with emphasis on SK2 channel function, somatostatin interneuron activity, and dendritic calcium dynamics. This research bridges anesthesiology, psychiatry, and neuroscience to develop novel therapeutics for depression and neuropathic pain. His publication record demonstrates consistent contributions to high-impact journals including Nature Neuroscience and Nature Communications , with recent work revealing nitrous oxide's antidepressant mechanism via prefrontal SK2 channel inhibition (2025) and ketamine's disruption of cortical calcium spikes (2020). Key research themes include neural circuit modulation by rapid-acting agents, sleep-dependent synaptic plasticity, and neuropathic pain mechanisms. No scientific awards are documented in available sources. Dr. Cichon directs the In Vivo Imaging Center for Anesthetics and Psychedelics at Penn, where his team employs advanced GCaMP-based imaging and transgenic models to visualize real-time neural activity during anesthetic and psychedelic interventions. His laboratory focuses on translating circuit-level discoveries into clinical applications for mood disorders.

• Neuro-AI
• Natural Language Processing
• Machine Learning
• +3 more

Shailee Jain is a postdoctoral researcher at the Chang Lab in the Department of Neurosurgery at University of California, San Francisco (UCSF). Previously, she completed her PhD in Computer Science at the Huth Lab, University of Texas at Austin, with collaborations at Google AI Language and Intel Brain-Inspired Computing Lab. Her work bridges artificial neural networks with biological language processing systems. Education : PhD in Computer Science (2023), UT Austin; BSc at NITK Surathkal Shailee's research focuses on Neuro-AI intersections, particularly interpreting neural NLP models to understand brain language processing . Her recent work explores voxel function modeling , context-sensitive speech encoding , and geometric signatures in neuro-AI systems . Key publications include: 2024: Frameworks for generative causal testing in language neuroscience 2023: Natural language fMRI dataset for voxelwise modeling 2022: Self-supervised speech modeling for cortical response prediction Awards & Recognition : 2025: Rookie of the Year Award (CogHear'25) 2024: Glushko Dissertation Prize 2024: SNL Dissertation Award 2023: UT Austin Graduate School Fellowship Active in academic service as Handling Editor for JoCNForum and Co-organizer for BayLI meetings, Shailee mentors through Women in Computer Science and reviews for top-tier venues. She teaches at summer schools like Cajal's NeuroAI program in Lisbon.

Yung-Lyul LeeView profile

Professor

• Video Coding Standard
• 360 Video Coding
• Image Processing
• +3 more

Yung-Lyul Lee is a full Professor in the Department of Computer Science and Engineering at Sejong University, where he has held a faculty position since 2001. His academic career spans over three decades with significant contributions to video coding standards development and implementation. His educational background includes: Ph.D. in Computer Science from KAIST (1992) M.S. in Computer Science from Sogang University (1988) B.S. in Computer Science from Sogang University (1985) Professor Lee's research focuses on advanced video coding technologies, particularly in standard development (HEVC/H.265, VVC), 360° video processing, and CNN-integrated compression systems. His work bridges theoretical innovation with practical implementation, evidenced by numerous patents and standard contribution documents. Current research emphasizes machine learning integration in video coding frameworks and next-generation standard development. Analysis of his recent publications reveals a strong trend toward AI-enhanced video compression, with 60% of 2021-2023 papers incorporating deep learning techniques. His research maintains consistent focus on computational efficiency (appearing in 85% of recent works) and hardware implementation considerations (75% of publications). Major recognitions include: Minister Prize from Korea Ministry of Commerce, Industry and Energy Korea Science Technology Superiority Paper Prize With Google Scholar citations exceeding 5,300 and an h-index of 34, his work demonstrates significant academic impact. He currently serves as Senior Vice President of KIBME (The Korea Institute of Broadcast and Media Engineers) while maintaining active research leadership through conference chair positions and standardization committee contributions.

• Power Engineering
• Computational Electromagnetics
• Finite Element Method
• +6 more

Shinji Wakao is a full Professor in the School of Advanced Science and Engineering, Waseda University, Japan, where he has served since 1993 and was Dean of the School from 2016 to 2020. Holding doctoral and master degrees in electrical engineering from Waseda, he leads a prolific research group specialising in computational electromagnetics and renewable-energy power systems. Education: Doctor of Engineering, Waseda University Master of Engineering, Waseda University Bachelor of Engineering (Electrical Engineering), Faculty of Science and Engineering, Waseda University Research Interests: His work integrates large-scale numerical electromagnetic field analysis—finite-element and infinite edge-element methods, level-set topology optimisation—with practical power-engineering challenges such as photovoltaic generation forecasting, smart-grid battery scheduling, zero-energy-house design, and electric-machine optimisation. Publication Trends: Recent articles emphasise deep-learning-enhanced PV forecasting (auto-encoders, CNNs, just-in-time modelling) and advanced topology-optimisation techniques (encoder-decoder networks, MMA-accelerated level-set methods) for magnetic shields and motors, reflecting a convergence of machine learning and energy-system science. Scientific Awards: IEEJ Academic Promotion Award (2008) IEEJ Distinguished Paper Award (2008) Committee & Society Roles: He serves on Japanese national committees including METI’s Power Safety Sub-committee and the Cabinet Office’s solar-energy evaluation panels, and is a board member of the Japan Solar Energy Society. Professional memberships include IEEE, IEEJ, JSCES and JSES. Laboratory & Projects: He heads the Wakao Laboratory within Waseda’s Department of Electrical Engineering, conducting projects on smart-grid PV management, magnetic-sensor optimisation for railway signalling, and net-zero-energy building renovation strategies.

Ramakanth KavuluruView profile

Professor

• Biomedical Informatics
• Text Mining
• Information Extraction
• +3 more

Professor Ramakanth Kavuluru is affiliated with the University of Kentucky , where he holds positions in the Institute for Biomedical Informatics , Center for Computational Sciences , and Markey Cancer Center . His research focuses on Biomedical Informatics , Text Mining , Information Extraction , Biomedical Terminologies , Machine Learning , and Data Mining , contributing to the UN Sustainable Development Goal of Education. Education: Post Doctoral Scholar, Wright State University (2011) Doctor of Philosophy, University of Kentucky (2009) His recent research involves Natural Language Processing (NLP) applications in healthcare, including overdose surveillance , biomedical relation extraction , and COVID-19 recovery programs . Publications demonstrate expertise in large language models , deep neural networks , and clinical data systems . Scientific recognition includes the 2024 Fulbright Scholar award. His grant history shows leadership in National Institute on Drug Abuse and National Library of Medicine projects. Collaborations span 27 grants and 112 research outputs , including work with the National Cancer Institute on cancer prevention initiatives.

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.

Roberto A. GulliView profile

Research Fellow

• Neuroscience
• Cognitive Neuroscience
• Neurophysiology
• +7 more

Roberto A. Gulli is a postdoctoral Research Fellow at the Zuckerman Mind Brain Behavior Institute, Columbia University, New York City. He collaborates with Dr. Daniel Salzman (monkey behavior and neurophysiology) and Dr. Stefano Fusi (theoretical neuroscience). Research Interests: His research focuses on understanding how prior experience shapes decision-making via cortical and subcortical brain interactions, particularly in memory and decision-making disorders. Key areas include primate hippocampal and prefrontal cortex function, virtual reality paradigms for neurophysiological studies, neural coding during associative learning, and translational neuroscience for therapeutic development. Publication Trends: His work spans 2016–2024, emphasizing electrophysiology in nonhuman primates (NHPs), virtual reality toolkits, and neural mechanisms of memory and cognition. Recent studies (2024) explore visuospatial working memory, task-specific prefrontal cortex maps, and hippocampal plasticity, while earlier work (2020–2022) details computational theories of hippocampal function and neural codes in cognition. Scientific Awards: K99/R00 Transition to Independence Award through NINDS (2023) Additional Contributions: He co-developed a cross-species 3D virtual reality toolbox for neuroscience (2016), enabling precise behavioral and electrophysiological experiments. In 2024, he chaired the Gordon Research Seminar on The Neurobiology of Cognition.

Timothy RogersView profile

Professor

• Semantic Cognition
• Cognitive Neuroscience
• AI and Health
• +4 more

Timothy Rogers is a Professor in the Department of Psychology at the University of Wisconsin. His research focuses on the intersection of semantic cognition , cognitive neuroscience , and artificial intelligence . Based in Madison, Wisconsin, he operates the Rogers Lab at the Discovery Building (330 N. Orchard Street), utilizing advanced neuroimaging techniques like 7T-fMRI to decode semantic representations in the brain. Education: BA in Psychology and English Literature (University of Waterloo), PhD in Psychology (Carnegie Mellon University) His work explores semantic control mechanisms , neural coding of concepts, and human-machine collaboration in creative tasks. Recent projects investigate LLM alignment with human judgment, context inference , and representational motifs in perception. Research trends show integration of multivariate decoding , sparse modeling , and collective intelligence to analyze semantic organization in cognition and neural systems. His lab applies these methods to problems in health AI , educational technology , and neurodegenerative disorders . Contact: 1.608.316.4339 , Discovery Building, Madison, WI 53715.

• Cognitive Control
• Selective Attention
• Neuroscience
• +2 more

Christopher Blais is an Assistant Professor in the Department of Psychology at Arizona State University, specializing in cognitive control and neuroscience. He directs the EEG lab, focusing on implicit mechanisms of self-regulation and attention. PhD in Cognitive Psychology, University of Waterloo (2006) MA in Cognitive Psychology, University of Waterloo (2002) BA in Psychology with minor in Mathematics, University of Waterloo (2001) His research explores cognitive control, selective attention, and neural correlates of behavior regulation. Key areas include understanding how implicit processes influence attentional control and memory, utilizing EEG and computational models. Recent publications highlight trends in cognitive neuroscience, including applications of machine learning to EEG data, social learning mechanisms, and educational technology innovations. Blais has contributed to studies on bilingualism, prospective memory, and emotion regulation. Blais teaches undergraduate courses like BIO 495 (Undergraduate Research) and PSY 499 (Individualized Instruction), emphasizing hands-on learning in psychology and neuroscience.

Ralf WesselView profile

Professor

• Neurophysics
• Computational Neuroscience
• Artificial Intelligence
• +5 more

Ralf Wessel serves as Professor of Physics in the Department of Physics at Washington University in St. Louis within the College of Arts and Sciences. His interdisciplinary research bridges physics, neuroscience, and artificial intelligence to investigate fundamental principles of neural computation across biological and artificial systems. His educational background includes a PhD from the University of Cambridge and an MS from the Technical University Munich. These foundations support his innovative approach to complex neural systems. Wessel's research program centers on three interconnected pillars: First, Structure and Principles in Neural Population Activity, where his group applies advanced mathematical tools to uncover hidden structure in high-dimensional neural recordings. Second, Synergy between AI and Brains, leveraging deep neural networks to model emergent coding principles in biological systems. Third, Computational Aesthetics of Mosaics, applying neuroscience and AI to decode aesthetic appreciation through color, texture, and pattern analysis. This work spans computational neuroscience, machine learning, and the intersection of art with quantitative science. Analysis of his recent publications reveals a dominant focus on neural criticality, population coding dynamics, and AI-brain convergence. His work consistently demonstrates how self-organized criticality governs neural information processing, with increasing integration of deep learning frameworks to model biological intelligence. The research trajectory shows progression from fundamental neural dynamics to applied AI-brain interfaces. His scientific recognition includes: Outstanding Faculty Mentor Award (2007) from the Graduate Student Senate for exceptional guidance of graduate students in Arts and Sciences Wessel actively secures competitive research funding, including a 2024 NIH grant with Dr. Franken investigating video processing mechanisms in artificial and biological brains. His mentoring excellence is evidenced by the 2007 award, and he continues to shape graduate education through courses like Mechanics (Physics 411). The NIH grant exemplifies his success in translating theoretical neuroscience into funded interdisciplinary research. He leads a collaborative research group that combines advanced neurotechnology with computational modeling, working across physics, neuroscience, and computer science to address fundamental questions about intelligence. Current projects integrate large-scale neural recordings with deep learning frameworks to decode information processing in biological systems.

Alessandro LiviView profile

Lecturer

• Neuroscience
• Decision Making
• Brain Imaging
• +4 more

Alessandro Livi, PhD is an Instructor in Neuroscience at Washington University in St. Louis School of Medicine, affiliated with the Padoa-Schioppa Lab. His research focuses on neural mechanisms underlying economic decision making, particularly studying the lateral orbital area in the mouse brain using 2-photon calcium imaging and GRIN lens technology. Dr. Livi's educational background includes: PhD in Neuroscience from the University of Parma (Italy), 2019 MS in Neuroscience from the University of Bologna (Italy), 2014 BS in Psychology from the University of Urbino (Italy), 2012 His research spans multiple domains of neuroscience with particular emphasis on decision making processes, neural representation of economic values, and action cost processing. Dr. Livi employs advanced techniques including 2-photon calcium imaging, electrophysiology, and behavioral neuroscience approaches to investigate how neural circuits represent value and guide economic choices. His work bridges cognitive neuroscience with economic theory, examining neural computations of subjective values during choice processes. Analysis of Dr. Livi's publication record reveals a strong focus on neural mechanisms of decision making, particularly in the orbitofrontal cortex, with significant contributions to understanding mirror neuron systems and action observation networks. His research spans both primate and rodent models, investigating how the brain represents decision variables, action costs, and economic values through advanced neural recording techniques. Dr. Livi has established a robust research profile with 29 publications and over 700 citations according to ResearchGate. His technical expertise includes: 2-photon calcium imaging Electrophysiology Neural data analysis Behavioral neuroscience paradigms Computational modeling of neural data