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Christopher John Rozell is the Julian T. Hightower Chaired Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology's College of Engineering. He serves as Executive Director of the Institute for Neuroscience, Neurotechnology & Society (INNS) and directs the Sensory Information Processing Lab (SIPLab). His research bridges computational neuroscience, machine learning, and neurotechnology, with clinical applications in treatment-resistant depression and neuromodulation therapies. Education: B.S.E. in Computer Engineering & B.F.A. in Music, University of Michigan (2000) M.S. and Ph.D. in Electrical Engineering, Rice University (2002, 2007) Postdoctoral Scholar, Redwood Center for Theoretical Neuroscience, UC Berkeley Research Focus: Dr. Rozell's interdisciplinary work spans computational neuroengineering, theoretical neuroscience, and artificial intelligence. He develops data analysis tools inspired by neural processing and creates therapeutic neurotechnologies. Key areas include: computational psychiatry (developing DBS therapies for depression), neural dynamics modeling, brain-computer interfaces, and societal impacts of neurotechnology. His lab focuses on high-dimensional data analysis, neural coding principles, and scalable neuromodulation approaches. Publication Trends (2023-2025): Recent works concentrate on deep brain stimulation mechanisms for depression, computational modeling of neural/autonomic dynamics, and machine learning applications in neuroscience. Dominant themes include biomarker discovery for treatment response, neural interoception modulation, probabilistic modeling of latent states, and brain-computer interface taxonomy. Clinical translation of neurotechnology is a consistent focus across publications. Awards & Honors: Elected AIMBE Fellow (2025) NIH BRAIN Initiative Photo/Video Award (2024) Congressional Panelist for BRAIN Initiative 10th Anniversary (2024) Sigma Xi Best Faculty Paper (2024) Neuro Open Science International Prize (2022) W. Howard Ector Outstanding Teacher Award (2019) McDonnell Foundation 21st Century Science Award (2014) NSF CAREER Award (2014) Leadership & Training: Dr. Rozell co-founded Neuromatch, Inc. to build global computational neuroscience communities. He advises Motif Neurotech and the Institute of Neuroethics. His mentees have received prestigious fellowships (Schmidt, Fulbright, NIH K99/R00) and hold leadership positions across academia and industry. Research is supported by NIH BRAIN Initiative, NSF, and private foundations. Labs & Initiatives: Directs the Sensory Information Processing Lab (theoretical neuroscience/neuroengineering) and the Institute for Neuroscience, Neurotechnology & Society (addressing ethical/societal implications). Neuromatch promotes open, accessible computational neuroscience training globally.

• Computational Neuroengineering
• Artificial Intelligence
• Neuroscience
• +7 more

Dr. Joshua T. Vogelstein is an Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University, holding joint appointments in Biostatistics, Applied Mathematics & Statistics, Neuroscience, and Computer Science. He leads the NeuroData lab, focusing on big data science, machine learning, and connectomics. Education: PhD and MSE in Neuroscience and Applied Mathematics from Johns Hopkins (2009), BS in Biomedical Engineering from Washington University (2002). Notable achievements include co-founding the Open Connectome Project (acquired by APL) and Gigantum (acquired by NVIDIA). Recognized with the NSF CAREER Award (2020), F1000 Prime (2014), and multiple Johns Hopkins Discovery Awards. Research emphasizes statistical connectomics, network science, and applying AI to biomedical challenges. Key contributions include mapping the first insect brain connectome (Science 2023) and developing open-source tools like CloudReg and BrainLine. Collaborates with Microsoft Research and industry partners, co-founding ventures like Global Domain Partners and Mind-X. Advised over 60 trainees, teaches machine learning and data science. Promotes open science through NeuroData's ecosystem of tools and data. Current work explores organoid intelligence, prospective learning, and neural network dynamics.

• Machine Learning
• Neuroscience
• Network Science
• +4 more

Sara Magliacane is an Assistant Professor at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab . She leads research at the intersection of causality and machine learning , focusing on improving AI robustness, generalization, and safety through causal reasoning. Her work spans causal representation learning , causal discovery , and causality-inspired ML in domains like reinforcement learning and dynamical systems. PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano/Torino BSc in Computer Engineering (2008), Università degli Studi di Trieste Her research explores causal variable identification from high-dimensional data (e.g., images, sequences) and causal graph discovery for domain adaptation. Methods include CITRIS , FANS-RL , and SNAP , with applications in embodied AI and biomedical data. The group emphasizes theoretical guarantees and scalable algorithms for real-world systems. Recent work trends include temporal causal modeling , intervention-efficient learning , and nonstationary reinforcement learning . Publications cover topics like causal discovery in partially observed settings , causal graph pruning , and sample-efficient concept learning , often combining neurosymbolic approaches with deep learning. Scientific Awards : ELLIS Scholar Sara supervises PhD students across universities (UvA, University of Pisa) and collaborates with institutions like TU Delft , Harvard , and IBM Research . She co-organizes workshops at premier conferences (NeurIPS, ICML, AISTATS) and teaches causality courses at the University of Amsterdam and Harvard Data Science Initiative. Her lab, Amsterdam Machine Learning Lab (AMLab) , investigates causal structure in embodied agents , safe reinforcement learning , and hybrid dynamical system modeling . The group maintains active partnerships with institutions such as MIT-IBM Watson AI Lab , Qualcomm , and Adyen .

• Causality
• Causal Representation Learning
• Causal Discovery
• +4 more

Jakub Vohryzek is a postdoctoral researcher at the Computational Neuroscience (CNS) Group at University Pompeu Fabra in Barcelona, supervised by Prof. Gustavo Deco. His work focuses on spacetime connectomics and whole-brain modeling, particularly in neurodegenerative disorders and psychedelic neuroscience. He holds a DPhil from the University of Oxford, where he studied under Prof. Morten Kringelbach. Research Interests: Spacetime connectomics Psychedelic-induced brain state transitions Neurotwin models for personalized medicine Cognitive and clinical applications of whole-brain dynamics Current projects include developing neurotwin models under a European grant for neurodegenerative treatments, investigating brain state dynamics in mindfulness therapy, and modeling psychedelic effects on Alzheimer’s disease. His recent work emphasizes low-dimensional brain network interactions and functional hierarchy perturbations. His research has explored connectivity profiles, oscillatory restoration in dementia, and algorithmic agent approaches to neuropsychiatric disorders. He collaborates on open-science initiatives like Brainhack and advocates for inclusive conference design.

• Computational Neuroscience
• Spacetime Connectomics
• Neurotwin Models
• +5 more

Mehdi Khamassi is a Research Director at the French National Center for Scientific Research (CNRS), assigned to the Institute of Intelligent Systems and Robotics (ISIR) at Sorbonne University in Paris, France. He holds an engineering background in computer science (specializing in AI and statistical modeling) from the National School of Computer Science for Industry and Business (2003), a Cogmaster in cognitive science from Pierre and Marie Curie University (2003), and a PhD in cognitive neuroscience from UPMC/Collège de France (2007). Recruited by CNRS in 2010, he co-organizes the Symposium of Biology of Decision-Making (SBDM) and co-directs the modeling major for the Cogmaster program. His research integrates computational modeling , neuroscience experiments , and robotic systems to study decision-making and learning mechanisms. Key interests include: Reinforcement learning in biological and artificial systems Role of social/non-social rewards in adaptive behavior Ethical implications of autonomous decision-making in AI Neuro-robotic models of hippocampal-prefrontal interactions Recent publications (2023-2025) demonstrate strong focus on reinforcement learning paradigms, AI alignment with human values, neurorobotics, and computational neuroscience. Work frequently bridges machine learning theory with empirical validation in biological systems or robotic platforms. He leads research within the ACIDE team at ISIR, exploring adaptive coordination of learning strategies in brains and robots. Current collaborations include NTUA (Greece), University of Oxford, and University of Trento.

• Decision Making
• Reinforcement Learning
• Cognitive Neuroscience
• +5 more

Prof. Dr. Moritz Helias is a University Professor and leads the Theory of Multi-Scale Neuronal Networks group at the Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience, Forschungszentrum Jülich. His research bridges biological and artificial neural networks, focusing on dynamics, information processing, and the physics of AI. The group is part of a larger interdisciplinary institute that integrates theory, simulation, and data analysis to understand the brain. Institution: Forschungszentrum Jülich School: Institute for Advanced Simulation Department: IAS-6, Computational and Systems Neuroscience Position: Professor and Group Leader Email: m.helias@fz-juelich.de His research interests lie at the intersection of statistical physics and neuroscience. He investigates how structure shapes dynamics in both biological and artificial networks, aiming to uncover general principles of information processing. Using methods from statistical physics, his work enables a unified framework for understanding collective phenomena, learning, and generalization. Key areas include spiking neural networks, renormalized field theory, and the theoretical foundations of AI. The recent publications reflect a strong trend toward multi-scale modeling of neural systems, integrating statistical physics with neuroscience. Topics include spiking network dynamics, mean-field theory, renormalization, and applications of machine learning in physics. The work spans biological realism and artificial intelligence, with implications for neuromorphic computing and brain-inspired AI architectures. While no scientific awards are listed in the provided texts, his group actively contributes to open science through tools like NEST and theoretical frameworks that influence both neuroscience and AI. Prof. Helias supervises a research group focused on theoretical and computational approaches, contributing to collaborative projects involving large-scale simulations and data analysis. His team works closely with experimentalists and theorists to validate models and advance understanding of brain function. The group is also involved in developing simulation technologies and theoretical tools that support reproducible neuroscience. The Theory of Multi-Scale Neuronal Networks group is embedded within a vibrant research environment at IAS-6, collaborating with teams in statistical neuroscience, computational neurophysics, and future simulation architectures. This fosters a loop between data, theory, and simulation, enabling cutting-edge research on brain function and artificial intelligence.

• Theoretical Neuroscience
• Statistical Physics of Neural Networks
• Dynamics of Neuronal Networks
• +6 more

Dr. Wen-hao Zhang is an Assistant Professor at the Lyda Hill Department of Bioinformatics and a member of the O'Donnell Brain Institute at UT Southwestern Medical Center since 2021. He directs the Computational Neuroscience Lab (CNL), focusing on bridging neural circuits and cognition via normative theories and biologically plausible models. Education : PhD in Theoretical Neuroscience (2016, Institute of Neuroscience, Chinese Academy of Sciences), BE in Biomedical Engineering (2009, Shanghai Jiao Tong University). Career : Postdoctoral roles at Carnegie Mellon University, University of Pittsburgh, and University of Chicago. Research Interests : The lab investigates neural information processing using techniques like nonlinear dynamics, Bayesian inference, and Lie group theory. Their work spans Causal inference and decision-making in neural circuits Grid cell mechanisms and spatial navigation Brain-inspired machine learning algorithms Collaborations : Partnerships with experimental neuroscientists such as Todd Roberts (UTSW) and theorists like Tai Sing Lee (CMU) ensure theoretical models align with empirical data. The lab has published in top venues like ICLR and NeurIPS, focusing on multisensory integration and neural coding. Lab Members : Current PhD students include Eryn Sale, Zimei Chen, Yi Ren, and Armand Rathgeb. Former visiting students like Xinruo Yang (University of Pittsburgh) and Xiangyu Ma (HKUST) have contributed to interdisciplinary projects.

• Computational Neuroscience
• Neural Circuits
• Bayesian Inference
• +6 more

Nikil Dutt is a Chancellor’s Professor at the University of California, Irvine (UCI), with academic appointments in Computer Science, Electrical Engineering and Computer Science (EECS), and Cognitive Sciences. He is affiliated with UCI's Center for Embedded Computer Systems (CECS), Center for Cognitive Neuroscience and Engineering (CENCE), Calit2, CPCC, and LUCI. His research focuses on embedded systems, electronic design automation, computer architecture, and healthcare IoT. Dutt has authored/co-authored seven books and holds IEEE Fellow and ACM Distinguished Scientist titles. Education: B.E. (Mechanical Engineering) from Birla Institute of Technology and Science (Pilani, India), 1980; M.S. (Computer Science) from Pennsylvania State University, 1983; Ph.D. (Computer Science) from University of Illinois at Urbana-Champaign, 1989. Research Interests: Embedded systems, brain-inspired architectures, neuromorphic computing, and healthcare IoT. Current projects include the Information Processing Factory (IPF) for autonomous systems and CareDex for disaster resilience in aging communities. Awards: Multiple Best Paper Awards, NSF grants, and fellowships. Serves as editor for ACM TECS, IEEE TVLSI, and former Editor-in-Chief of ACM TODAES. Active in academic service, including ESWEEK Steering Committee roles. Grants: NSF IPF, UNITE, CareDex, and industry partnerships (e.g., Facebook). Research addresses energy-efficient data centers, autonomous driving systems, and wearable health technologies. Labs/Teams: Leads the Dutt Research Group (DRG), focusing on self-aware systems, edge computing, and neuromorphic architectures.

• Embedded Systems
• Electronic Design Automation
• Computer Architecture
• +6 more

Ana Lucic is an Assistant Professor in Artificial Intelligence at the University of Amsterdam , with a joint appointment between the Institute for Logic, Language and Computation and the Informatics Institute . Her research focuses on interpretable machine learning applications for scientific discovery and societal impact. Formerly at Microsoft Research AI for Science and Partnership on AI PhD in Explainable Machine Learning from University of Amsterdam (2022) BSc/MSc in Mathematics from McMaster University Research Highlights: Develops mechanistic interpretability methods for deep learning architectures. Created Aurora , a foundation model for Earth system forecasting outperforming traditional operational models in air quality prediction and tropical cyclone tracking. Pioneers Clifford-Steerable CNNs for geophysical data analysis. Actively hiring PhD students for AI transparency research . Collaborative Networks: Contributions to ELLIS Summer School and ICML workshops . Collaborates with Microsoft Research AI for Science team on climate-related ML projects. Involved in organizing TerraBytes workshop at ICML 2025. Recent Advancements: Key role in publishing Aurora model in Nature (2025), demonstrating superior performance in Earth system forecasting. Supervises Ege Erdogan , new PhD student focused on mechanistic interpretability. Actively contributes to open-source AI development through GitHub repositories and technical discussions.

• Interpretable Machine Learning
• Artificial Intelligence
• Earth System Science
• +3 more

Archer Yang is an Associate Professor in the Department of Mathematics and Statistics at McGill University, with additional affiliations as an Associate Academic Member of Mila - Quebec AI Institute, Associate Member of the School of Computer Science, and Member of the Quantitative Life Science Program. His academic journey began with a PhD from the University of Minnesota under the supervision of Hui Zou, establishing his foundation in statistical methodology and machine learning. Dr. Yang's research spans three interconnected themes: statistical machine learning, applications in drug discovery, and computational genomics and healthcare. In statistical machine learning, he focuses on developing dimensionality reduction, probabilistic models, and causality-inspired methods to address complex high-dimensional data challenges. His work in drug discovery involves creating machine learning models to accelerate drug candidate identification and enhance understanding of drug efficacy and safety. In computational genomics and healthcare, he develops techniques to analyze genomic data, identify biomarkers, and explore the genetic basis of diseases, with the goal of improving precision medicine and predicting patient outcomes. His overarching objective is to bridge advanced data-driven methodologies with impactful applications in pharmacology, genomics, and healthcare. His recent publications reveal a strong trend toward applying machine learning to healthcare challenges, particularly in congenital heart disease analysis, mortality prediction, and drug discovery. His work demonstrates expertise in developing interpretable models that can handle complex, high-dimensional biomedical data while maintaining statistical rigor. The integration of causal inference methods with machine learning appears to be a growing focus in his research trajectory. ICML Spotlight Paper (top 2.6%, 313/12,107) Dr. Yang actively supervises a large research group including multiple postdoctoral fellows, PhD students, and Master's students. His lab has developed several notable software tools, including ml-mr for machine learning in Mendelian randomization. His supervision extends across statistics, computer science, and biomedical applications, reflecting the interdisciplinary nature of his work. He appears to maintain strong collaborative relationships with researchers in healthcare and genomics fields. His laboratory, the Archer Yang Lab, maintains active GitHub repositories focused on machine learning applications in healthcare and drug discovery, with particular emphasis on interpretable models and statistical methodology development. The lab appears to work at the intersection of theoretical statistics and practical biomedical applications, with projects spanning from algorithm development to clinical implementation.

• Statistical machine learning
• High-dimensional statistics
• Computational statistics
• +8 more

Pieter Simoens is an Assistant Professor at Ghent University and affiliated with the imec research institute. He works at the intersection of distributed artificial intelligence, edge computing, and collective intelligence, with a focus on AI applications for resource-constrained environments and robotic systems. His research explores innovative approaches to machine learning deployment in heterogeneous infrastructures, task planning for IoT-integrated robotics, and modeling collective decision-making processes. He has contributed to frameworks like DIANNE for distributed deep learning and developed methods for cognitive modeling in reinforcement learning scenarios. With over 100 publications, his recent work spans adaptive neural networks, privacy-preserving surveillance, UAV hyperspectral data analysis, and computational fairness in AI systems. He leads research initiatives within the Internet Technology and Data Science Lab (IDLab) and contributes to educational programs in software engineering and applied machine learning. Responsible for courses on software engineering, mobile development, system design, and applied machine learning Active in edge computing and neuromorphic algorithms research Develops AI solutions for robotics, surveillance, and industrial IoT applications

• Distributed Artificial Intelligence
• Edge Computing
• Algorithms for Neuromorphic Computing
• +2 more

Jorge Manuel M. C. Pereira Batista is an Associate Professor at the Department of Electrical and Computer Engineering , University of Coimbra, Portugal. He serves as a senior researcher at the Institute for Systems and Robotics (ISR-UC) and leads the Computer Vision Group. His career spans academic roles, research coordination, and industry collaboration. Academic Affiliation: University of Coimbra (Electrical & Computer Engineering Department) Research Institute: Institute for Systems and Robotics (ISR-UC) His research focuses on Computer Vision , Pattern Recognition , and applications of Differential Geometry in these fields. Key subdomains include facial analysis, visual surveillance, real-time vision systems, and machine learning integration. From 2010–2023, his recent publications highlight advancements in probabilistic models , transformer architectures , multi-branch learning , and biomimetic robotics . He has coordinated multiple funded projects such as: STORK : Avian protection systems NeuroCity : Intelligent street lighting 4D Facial Dynamics : Identity recognition iTRAFFIC : BRISA highway traffic monitoring His work bridges theoretical innovation (e.g., Riemannian manifold applications) with practical deployments in transportation, energy, and healthcare sectors.

• Computer Vision
• Pattern Recognition
• Facial Analysis
• +5 more

Lisa Lee is a Research Scientist at Google DeepMind, focusing on creating AI agents that emulate biological learning and adaptability. She previously taught at Princeton University and received TA awards for Deep Reinforcement Learning and Probabilistic Graphical Models. Education: PhD in Machine Learning from Carnegie Mellon University (advised by Ruslan Salakhutdinov and Eric Xing); A.B. in Mathematics from Princeton University (advised by Sanjeev Arora). Her research centers on AI embodiment, intrinsic motivation, and hierarchical planning. She explores how evolutionary-inspired inductive biases and memory mechanisms can enable agents to generalize across physical and conceptual domains, as demonstrated in her work on robotic agility benchmarks and multimodal transformers. Notable scientific contributions include the Barkour quadruped robot benchmark, Gemini multimodal models, and theoretical work on causal language models. She co-organized key AI workshops at NeurIPS and ICML, and her awards include Princeton's TA of the Year for technical courses. Leadership: ICML Workflow Chair (2019), NeurIPS workshop co-organizer (2019, 2021), peer reviewer for top AI conferences.

• Artificial Intelligence
• Reinforcement Learning
• Continual Learning
• +5 more

Constantin Uhde is a PhD candidate at the Institute for Cognitive Systems (ICS) of the Technical University of Munich (TUM). His research focuses on integrating cognitive approaches with robotic planning to achieve robust, unsupervised task execution. He holds a Master's degree in Robotics, Cognition, and Intelligence from TUM and a Bachelor's in Mechatronics from Friedrich-Alexander University Erlangen. Research Interests include bio-inspired robotics, human-robot interaction, virtual reality applications in robotics, and path planning using multimodal sensor data. His work bridges theoretical cognitive science with practical robotic systems, emphasizing causal reasoning and transfer learning. Uhde teaches as a lecturer for the Advanced Seminar & Practical Projects in Cognitive Systems and has published in top-tier conferences such as IEEE CASE, IROS, and ICRA. His contributions range from visuomotor learning frameworks to benchmark datasets for symbolic grounding in robotics. He is part of the ICS lab team developing systems for humanoid robotics and neuroengineering applications, contributing to platforms like the Compact Omni-directional Mobile Platform and EEG Laboratory setups.

• Robotics
• Cognitive Systems
• Bio-inspired Robotics
• +4 more

Paul Tiesinga is Professor of Neuroinformatics at Radboud University's Faculty of Science, where he directs the Neuroinformatics group at the Donders Centre for Neuroscience. His research focuses on understanding information processing in the brain, particularly the role of oscillations ('brain waves') in cognitive functions and disease mechanisms. He combines computational modeling with experimental approaches including optogenetics to investigate cortical dynamics. As Director of Education for the Institute for Mathematics, Physics and Astronomy, he oversees academic programs while maintaining active research in: Computational modeling of cortical circuits Oscillation mechanisms in neural coding Data sharing policies for neuroscience Information transfer between brain areas His recent publications reflect advances in neurotechnology development, thalamocortical connectivity analysis, and novel methodologies for neural data interpretation.

• Computational Neuroscience
• Neurophysics
• Brain Oscillations
• +2 more