Mayank R. Mehta is a Professor at the University of California, Los Angeles (UCLA), holding joint appointments in the Departments of Physics & Astronomy, Neurology, and Neurobiology. He is a member of the Brain Research Institute and the W. M. Keck Center for Neurophysics at UCLA. His research bridges experimental and theoretical neuroscience, focusing on how neuronal networks encode space-time, the role of brain rhythms in learning and memory, and the impact of sleep and virtual reality on neural dynamics. His recent publications highlight breakthroughs in understanding hippocampal spatiotemporal selectivity, dendritic activity during behavior, and the causal influence of visual cues on memory neurons. Notable findings include the discovery that dendrites generate ten times more spikes than neuronal cell bodies and the modulation of hippocampal theta rhythms in virtual reality. Research Themes: Neurophysics of spatial-temporal coding Dendritic contributions to learning Virtual reality and brain plasticity Neural oscillations in memory consolidation Key Collaborators: Bert Sakmann (Max Planck Florida Institute) Thomas Hahn (Bernstein Center Heidelberg/Mannheim) Maryam Ghorbani (UCLA) Mehta's lab at UCLA trains graduate and postdoctoral researchers in cutting-edge techniques combining hardware development, electrophysiological recordings, and biophysical modeling. His work has significant implications for treating learning and memory disorders like Alzheimer's disease.
Dmitri Strukov is a Professor at the University of California, Santa Barbara in the Department of Electrical and Computer Engineering. His work spans material science, electrical engineering, and computer science, focusing on novel computing paradigms using emerging memory devices. Education: PhD in Electrical and Computer Engineering from SUNY Stony Brook, MS in Applied Physics and Mathematics from Moscow Institute of Physics and Technology. Research Interests include neuromorphic computing , non-volatile memory applications , and mixed-signal circuits for machine learning and hardware security. His group develops memristive crossbar arrays and 3D NAND flash for energy-efficient systems. Scientific Leadership features Fellow of IEEE and Distinguished Lecturer roles. His work has been recognized with best paper awards at ASPLOS’19 and Computing Frontiers’13. Students: Mentored PhD graduates in neurocomputing, security, and memristor design including Z. Fahimi, S. Larimian, M.R. Mahmoodi, and X. Guo. Grants: Funded by AFOSR, ARO, DARPA, NSF, and industry leaders like Google and Samsung. Labs: Utilizes UCSB’s nanofabrication center and advanced tools for memristor characterization.
John Baillieul is Distinguished Professor at Boston University with joint appointments in Mechanical Engineering, Systems Engineering and Electrical & Computer Engineering. He directs experimental laboratories for real-time control of lightweight robotic systems and applies nonlinear control theory to complex multi-body, networked, and bio-inspired systems. Education: Ph.D., Harvard University Research Interests: Baillieul’s work spans robotics, nonlinear control, and networked systems. Early contributions resolved motion-planning for kinematically redundant manipulators; current themes include neuromimetic learning, vision-based navigation, and resilience of infrastructure networks such as power grids. His group couples rigorous geometric control with real-time hardware to create lightweight, high-performance robots and to uncover fundamental information limits in feedback systems. Recent Publication Trends (2021-2025): Over the past five years his output has concentrated on three synergistic directions: (i) neuromimetic and Koopman-based data-driven methods for estimating and controlling nonlinear systems, (ii) vision-based guidance and sparse optical-flow primitives for agile autonomous flight, and (iii) network-theoretic decomposition and information-rate studies for resilient operation of power grids and collective dynamics. Honors & Awards: IEEE Fellow 2025 Roger W. Brockett Control Systems Award Former Editor-in-Chief, IEEE Transactions on Automatic Control Affiliations & Service: He is a member of Boston University’s Center for Information and Systems Engineering (CISE), has served as Editor-in-Chief of IEEE Transactions on Automatic Control, and remains active in editorial and organizational roles across the IEEE control systems community.
Alexei Koulakov is a Professor at Cold Spring Harbor Laboratory (CSHL) and the Charles Robertson Professor of Neuroscience. His research focuses on applying mathematical and computational approaches to unravel the principles of brain organization, particularly in sensory systems like olfaction and vision. Koulakov's work explores how neural circuits form during development, the role of genetic and experiential factors, and the evolutionary basis of brain architecture. Education: PhD in Physics from the University of Minnesota (1998). Key Research Areas: Olfactory system development, neural network modeling, and AI inspired by biological computation. Koulakov's recent publications emphasize cross-disciplinary integration of neuroscience and AI, including NeuroAI initiatives and DeepNose models predicting olfactory percepts. His team investigates how innate abilities are encoded genomically and how experience shapes neural networks. Scientific contributions include studies on primacy coding in olfaction, stochastic learning mechanisms , and high-throughput neural mapping . Awards include the Charles Robertson Professorship , reflecting his leadership in theoretical neuroscience. Koulakov collaborates extensively, with notable work on genomic bottlenecks , odor mixture interactions , and neural integrator models . His lab at CSHL is at the forefront of NeuroAI research, leveraging brain circuit insights to advance artificial intelligence.
Joseph S. Friedman is an Associate Professor of Electrical & Computer Engineering at the University of Texas at Dallas, leading the NeuroSpinCompute Laboratory within the Erik Jonsson School of Engineering and Computer Science. His research focuses on unconventional computing paradigms leveraging nanotechnology, including neuromorphic systems, spintronics, and memristive devices. He specializes in nanomagnet-based logic architectures, neuromorphic computing with domain walls and skyrmions, and hardware security for emerging technologies. His research explores energy-efficient computing through novel paradigms such as reversible skyrmion logic, neuromorphic networks using magnetic tunnel junctions, and stochastic Bayesian inference circuits. He has pioneered spintronic neurons demonstrating 94% accuracy in handwritten digit recognition and developed secure logic locking mechanisms using nanomagnet logic. His work integrates experimental fabrication with SPICE modeling, emphasizing scalable beyond-CMOS systems. Recent advancements include toggle SOT-MRAM architectures, quantum circuit design for neutral atom systems, and neuromorphic networks leveraging superconducting flux quanta. He advises over 20 graduate and undergraduate students, fostering innovation in AI hardware and unconventional computing. Notable projects include the NeuroSpinCompute Lab's domain wall neuromorphic networks, secure logic locking schemes, and collaborations with institutions like Sandia National Labs on neuromorphic reservoir computing. Current research trends emphasize low-energy spintronic architectures, hybrid quantum-classical systems, and neuromorphic applications in edge computing. His research is supported by NSF grants CCF-1910800 and CCF-2146439, focusing on neuromorphic and spintronic systems. He regularly contributes to conferences like IEEE Rebooting Computing and SPIE Spintronics, showcasing breakthroughs in nanomagnetic logic and neuromorphic inference.
Sebastian Risi is a Professor at the IT University of Copenhagen , where he directs the Creative AI Lab and co-directs the Robotics, Evolution and Art Lab (REAL) . His work bridges computational evolution, deep learning, and collective intelligence for applications in robotics, art, and video game design. His research focuses on self-organizing AI systems that grow or assemble through local interactions, inspired by biological development. Key areas include neuroevolution , neural cellular automata , and generative modeling , with applications in adaptive robotics, game content creation, and damage-resilient AI. Recent publications highlight trends in self-assembling neural architectures (NDPs) and 3D functional machine generation (Minecraft experiments). Awards include ERC Consolidator Grant (2022), Best Paper at FDG’21 , and Google Faculty Award (2019). Scientific Awards : ERC Consolidator Grant (GROW-AI), Best Paper FDG’21, Runner-Up IEEE Games’20, GECCO 2017 Competition Winner, Sapere Aude Grant, Amazon/Google Faculty Awards He advises on projects like GROW-AI (EU-funded), AI-TESTER (game testing), and C2SIM (military systems). Media coverage includes Science , Wired , and Popular Science .
Gabriel Koch Ocker is an Assistant Professor in the Department of Mathematics & Statistics at Boston University, specializing in theoretical and computational neuroscience. His research investigates how neural activity encodes sensory information, shapes behavior, and evolves through learning mechanisms. Research Focus: Structure-function relationships in neuronal networks Methodology: Dynamical systems, stochastic processes, statistical physics Collaborations: Experimental validation of computational models Recent publications analyze integrate-and-fire networks, dendritic calcium spiking, inhibition-stabilized circuits, and metastability in stochastic neuronal systems. His group combines mathematical rigor with biological relevance to explore neural coding, plasticity, and functional hierarchy in cortical structures. Key contributions include tensor decomposition approaches to correlation analysis, reconciling recording technique discrepancies, and developing field-theoretic frameworks for compartmental modeling. Work spans from molecular-level channel dynamics (Kv7 channels) to brain-area-level functional organization.
Professor Rafal Bogacz is a leading academic at the University of Oxford, affiliated with St Edmund Hall and the MRC Brain Network Dynamics Unit . He teaches computational neuroscience and statistics at both undergraduate and postgraduate levels, including the MSc in Neuroscience and BSc in Biomedical Science programs. MSc: Wroclaw University of Technology PhD: University of Bristol Postdoctoral Researcher: Princeton University His research focuses on computational neuroscience , particularly modeling brain networks involved in action selection , decision making , and Parkinson's disease pathophysiology. Key themes include: Developing predictive coding models of cortical computations Understanding basal ganglia neural circuits in healthy and diseased states Designing closed-loop deep brain stimulation paradigms Recent publications highlight work in neural plasticity , dopamine signaling , and computational psychiatry , with a notable Wellcome Discovery Award supporting research on learning in neurons . The Bogacz Group maintains strong collaborations with experimental neuroscientists and shares open datasets through the MRC BNDU Data Sharing Platform . Wellcome Discovery Award (2025): For learning in neurons Europe PMC Open Access (multiple): For numerous PLoS, Nat Neurosci, and J Neural Eng publications As a computational neuroscientist, Professor Bogacz supervises D.Phil. students and leads research programs that bridge theoretical neuroscience with clinical applications . The group actively participates in MRC BNDU training initiatives and public engagement activities like Schools Open Day demonstrations.
Pawel Ladosz is a Lecturer in Engineering Systems for Robotics at the Department of Mechanical and Aerospace Engineering, The University of Manchester. His research focuses on applying machine learning and computer vision to mobile robots, particularly in extreme environments such as total darkness or cluttered spaces. He is actively involved in developing autonomous navigation systems, wireless signal mapping, and high-level decision-making for robotic swarms. He teaches courses including Robotic Systems Design Project and Autonomous Mobile Robots. Education: PhD in Establishing and Optimising Unmanned Airborne Relay Networks (Loughborough University, 2014–2019) MEng in Aerospace Engineering (The University of Manchester, 2010–2014) Research Interests: Ladosz’s work emphasizes reinforcement learning for robotics, vision-based autonomous systems, and exploration in challenging environments. His projects often intersect with UN Sustainable Development Goals, contributing to innovations in robotic autonomy and sensor networks. Awards: He received the 2nd Autonomous Flying Technology Competition award in 2021, recognizing his contributions to autonomous flight systems. His research has also led to the establishment of the Centre for Robotic Autonomy in Demanding and Long-Lasting Environments (CRADLE), fostering cross-disciplinary collaborations. Grants & Projects: As Principal Investigator in the Aerospace Engineering initiative (2010–2035), he explores UAV communication networks and trajectory planning. His work addresses urban environment challenges, including relay positioning and signal prediction. Labs/Teams: Ladosz contributes to CRADLE, advancing robotic autonomy in extreme scenarios. His lab focuses on integrating AI and robotics for real-world applications.
Prof. Julijana Gjorgjieva is a tenured W3 Professor of Computational Neuroscience at the School of Life Sciences Weihenstephan, Technical University of Munich (TUM). She leads an independent research group at the Max Planck Institute for Brain Research and is affiliated with the Bernstein Center for Computational Neuroscience. Her research focuses on the principles governing neural circuit development, balancing learning plasticity with functional stability through computational and theoretical approaches. Key interests include synaptic organization, energy-efficient neural computation, and evolutionary optimality principles. Education & Career: B.Sc. Mathematics, Harvey Mudd College (2006) M.A.St. in Applied Mathematics, University of Cambridge (2007) Ph.D. Applied Mathematics, University of Cambridge (2011) Postdoctoral Fellowships: Harvard University (2011-2014), Brandeis University (2014-2016) Max Planck Research Group Leader (2016-2022) W2/W3 Professor at TUM since 2016 Research Interests: Computational neuroscience, theoretical modeling of neural circuits, synaptic plasticity mechanisms, homeostatic regulation, and the interplay of development and evolution in shaping brain architecture. She employs mathematical frameworks to study how circuits achieve robustness while enabling adaptive learning. Awards: Heinz Maier-Leibnitz Prize (2022) Eric Kandel Young Neuroscientist Prize (2021) ERC Starting Grant (2018) Multiple postdoctoral and early-career fellowships Grants & Funding: Includes DFG Collaborative Research Center on Neural Homeostasis, HFSP grants, and EU Horizon 2020 initiatives. Active in mentoring and promoting computational neuroscience through programs like Neuromatch Academy. Labs & Collaborations: Leads a multidisciplinary lab integrating experimental and theoretical approaches. Collaborates with institutions such as the Max Planck Society and international computational neuroscience networks.
Fabrizio Falchi is a researcher at the Artificial Intelligence for Media and Humanities (AIMH) Lab of the Institute of Information Science and Technologies (ISTI) within Italy's National Research Council (CNR). He also maintains an associate position at the Biorobotics Institute of Scuola Superiore Sant'Anna. His work focuses on developing advanced multimedia retrieval systems, with the VISIONE platform being his most notable contribution, which has won international competitions including the Video Browser Showdown in 2024 and placed second in 2023. Falchi's educational background includes: Ph.D. in Information Engineering from University of Pisa (Italy) Ph.D. in Informatics from Faculty of Informatics of Masaryk University of Brno (Czech Republic) M.B.A. from Scuola Superiore Sant'Anna in Pisa His research spans deep learning, convolutional neural networks, deep features extraction, similarity search algorithms, distributed indexing systems, multimedia information retrieval, computer vision applications, and peer-to-peer systems. Falchi has made significant contributions to fine-grained visual understanding, cross-modal retrieval (particularly image-text matching), and robustness of deep learning systems against adversarial attacks. His work demonstrates a strong focus on practical applications of these technologies, particularly in video retrieval systems and safety monitoring solutions. Analysis of Falchi's recent publications reveals a strong focus on video and image retrieval systems, with the VISIONE platform being central to his work. His research shows increasing emphasis on fine-grained understanding in computer vision, cross-modal retrieval, and addressing practical challenges like cross-resolution face recognition. Recent work demonstrates innovation in making these systems more efficient through techniques like knowledge distillation (ALADIN) and leveraging virtual worlds for training data. His publications consistently bridge theoretical advances with practical applications in surveillance, safety monitoring, and multimedia search. Falchi's work has received significant recognition: Best paper award at CBMI 2024 for 'Is ClLIP the main roadblock for fine-grained open-world perception?' VISIONE 2024 won the Video Browser Showdown competition in Amsterdam VISIONE obtained second place at Video Browser Showdown 2023 in Bergen Best Paper Award for 'Learning Safety Equipment Detection using Virtual Worlds' at CBMI 2019 Falchi collaborates extensively with researchers at ISTI-CNR, particularly within the AIMH Lab. His work on VISIONE involves collaboration with Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Claudio Gennaro, Nicola Messina, Lucia Vadicamo, and Claudio Vairo. As co-chair of Ital-IA 2023, the 3rd National Conference on Artificial Intelligence, he plays an active role in the academic community. He is a member of ACM (since 2012), the Computer Vision Foundation, the Italian Association for Computer Vision Pattern Recognition and Machine Learning (CVPL), and the CINI Lab on Artificial Intelligence and Intelligent Systems. Falchi is a key member of the Artificial Intelligence for Media and Humanities (AIMH) Lab at ISTI-CNR, where he leads research on video retrieval systems. The lab has developed the award-winning VISIONE platform, which combines multiple scientific results in content-based video retrieval. His team focuses on developing systems that enable users to search for target videos using textual prompts, drawing objects and colors, or images as query examples. The lab's work demonstrates strong interdisciplinary collaboration, bridging computer science with practical applications in media, safety monitoring, and urban environments.
Richard Kempter is a Full Professor at the Humboldt-Universität zu Berlin, where he leads the Theoretical Neuroscience research group within the Institute for Theoretical Biology, Department of Biology. His research focuses on the neural basis of learning and memory through computational and mathematical modeling of synapses, neurons, and neural networks. He is affiliated with several major research centers including the Bernstein Center for Computational Neuroscience, the Einstein Center for Neurosciences Berlin, and the CRC 1315 Memory Consolidation. Professor Kempter's research interests span theoretical and computational neuroscience with a particular focus on the neural mechanisms underlying learning and memory. His work employs biophysical modeling and mathematical analysis to study synaptic short- and long-term plasticity, the dynamics of single neurons, and the interaction of neurons in recurrently coupled networks. A key aspect of his research investigates how neural systems maintain a balance between learning susceptibility and stability against pathological activity patterns, with model systems including the hippocampus and early auditory system. His research group has made significant contributions to understanding hippocampal sharp wave-ripple events, phase precession in spatial navigation, auditory processing in barn owls, and memory consolidation mechanisms. The group's work combines theoretical approaches with computer simulations to unravel the computational principles of neural circuits, showing particular interest in how neural tissue remains susceptible to learning while maintaining robust stability against pathological activity patterns. Scholarship of the State of Bavaria (03/1994-12/1995) Emmy Noether Fellowship Part I (09/1999-08/2001), funded by the Deutsche Forschungsgemeinschaft Emmy Noether Fellowship Part II (01/2003-09/2008) Guest Professor , HU Berlin, Department of Biology (10/2008-03/2010) Professor Kempter has advised numerous PhD and Master's students throughout his career, with many continuing in neuroscience research. His group maintains strong connections with experimental laboratories to bridge computational models with empirical findings, particularly in hippocampal function and auditory processing. The Theoretical Neuroscience Lab participates in collaborative projects investigating memory consolidation and neural coding principles, contributing significantly to our understanding of how neural circuits implement computational principles underlying learning and memory.
Surjo R. Soekadar is the Einstein Professor of Clinical Neurotechnology at Charité – University Medicine Berlin. He leads the Clinical Neurotechnology Laboratory , which focuses on developing noninvasive neurotechnologies for treating neurological and psychiatric disorders through closed-loop brain stimulation and advanced brain-machine interfaces (BCI/BMI). His work integrates real-time EEG/MEG monitoring with electromagnetic stimulation to modulate pathological brain oscillations and enhance neuroplasticity in conditions like stroke, spinal cord injury, and psychiatric disorders. Education : Studied medicine in Mainz, Heidelberg, and Baltimore Clinical Training : Residency in Psychiatry and Psychotherapy at University of Tübingen Academic Journey : 2008-2011 Research Fellow at NINDS (USA); 2017 Venia Legendi at University of Tübingen; 2018 First Professor of Clinical Neurotechnology in Germany His research interests span: • Closed-loop neurostimulation combining real-time brain state monitoring with targeted intervention • Next-generation BCI using optically pumped magnetometers (OPM) for mobile MEG recordings • Neurorehabilitation through exoskeleton control and sensory feedback • Neurophysiological modeling of entropy measures and phase flows Recent publications highlight: • Adaptive deep brain stimulation protocols • Real-time phase-sensitive tACS applications • OPM-based BCI innovations • Stroke recovery mechanisms through corticospinal tract analysis Scientific recognition includes: International BCI Research Award BIOMAG Award NARSAD Young Investigator Award Funded by the European Research Council (ERC) , his lab trains doctoral students like David Haslacher (EEG/MEG integration), Khaled Nasr (multicoil TMS optimization), and Annalisa Colucci (entropy-driven BCI development). The team also explores quantum AI applications in clinical decision-making and bidirectional BCI systems using OPM and tES.
Eric Medvet is a professor specializing in evolutionary computation, genetic programming, and robotics. He is actively involved in research areas such as neuroevolution, soft robotics, and modular robotics. His work bridges theoretical advancements in evolutionary algorithms with practical applications in robotics and AI. Roles: Conference chair for EuroGP (2020-2022), co-chair of multiple workshops and sessions. Key Research: Focus on genetic programming, embodied intelligence, and the design of adaptive robotic systems. Research Interests: His work emphasizes the development of scalable and interpretable AI systems, particularly through evolutionary methods applied to robotics. He explores topics like neuroevolution for soft robots, quality diversity algorithms, and the integration of machine learning with evolutionary computation. Publications: His recent work highlights trends in interpretable AI, modular robotics control, and evolutionary algorithms for complex systems. Notable contributions include studies on MAP-Elites, graph-based genetic programming, and the application of LLMs in automated testing. Grants & Labs: Developed frameworks like JGEA for evolutionary computation experiments. Collaborates on projects integrating evolutionary methods with real-world robotics applications.
Benoît Lemaire is a permanent Lecturer at the University of Grenoble Alpes, affiliated with the Laboratoire de Psychologie et NeuroCognition (LPNC) and the CoMMet team (Consciousness, Memory and MetaCognition). His academic career spans computational cognitive modeling, with a focus on working memory, eye movement research, and educational technology applications. PhD in Computer Science/AI, Université Paris-Sud (1989-1992) Postdoctoral Research: University of Pittsburgh (1993), Swedish Institute of Computer Science (1994) Academic Roles: Maître de conférences (1996-), transitioning through Laboratoire des Sciences de l'Éducation (1994-1996), Laboratoire Leibniz (2004-2006), TIMC (2006-2010), and LPNC (2010-). Lemaire’s research integrates computational modeling with empirical studies across multiple domains: Working Memory : Time-based decay, interference effects, semantic compression, and attentional refreshing mechanisms. Eye Movements : Information search in texts, reading strategies, and visual-semantic integration. Educational Applications : Text assessment, metaphor comprehension, and adaptive learning systems. Inductive Learning : MDL-based models for concept learning and lexical knowledge acquisition. His recent publications (2021–2025) emphasize computational models of mental arithmetic, semantic knowledge impacts on memory, and similarity-based compression techniques. All work aligns with cognitive science and AI methodologies. Current affiliations include: Laboratoire de Psychologie et NeuroCognition (LPNC) – 2010- CoMMet team (Consciousness, Memory and MetaCognition) University of Grenoble Alpes – Permanent Lecturer
