Find Academic Supervisors

Charley WuView profile

Professor

• Computational Cognitive Science
• Human Learning Under Uncertainty
• Generalization and Inference
• +7 more

Charley Wu is an Independent Research Group Leader and W3 Professor of Computational Cognitive Science, currently transitioning from the University of Tübingen to Technische Universität Darmstadt. He leads the Human and Machine Cognition Lab (HMC Lab), jointly funded by the Excellence Cluster 'Machine Learning for Science' and the Tübingen AI Center, soon to be based at TU Darmstadt under a LOEWE Start Professorship and an ERC Starting Grant. University of Tübingen (former affiliation) Technische Universität Darmstadt (current/transitioning to) Human and Machine Cognition Lab (HMC Lab) Excellence Cluster 'Machine Learning for Science' Tübingen AI Center Charley Wu's research lies at the intersection of cognitive science and artificial intelligence, focusing on how humans learn and make decisions under uncertainty. Using computational models, statistical learning, and virtual reality experiments, he investigates the cognitive shortcuts and strategies people use to generalize and explore efficiently in complex environments. His work also explores social learning and collective intelligence through biologically inspired multi-agent systems. His recent publications, including a key paper in Nature Human Behaviour on generalization guiding exploration, reflect a strong trend toward integrating machine learning techniques with human behavioral data. The research emphasizes efficient inference, compressed representations, and compositional structures in cognition, bridging gaps between human and artificial intelligence. Notable scientific awards include: ERC Starting Grant: C⁴: Compositional Compression in Cognition and Culture LOEWE Start Professorship Dr. Wu is actively mentoring and expanding his research group, currently recruiting three fully-funded PhD students and one postdoctoral researcher. His lab is supported by competitive grants and institutional funding, indicating strong research momentum and future directions in computational models of cognition, AI-human alignment, and collective learning. He collaborates with leading researchers such as Fiery Cushman and Sam Gershman from his postdoctoral work at Harvard University. The Human and Machine Cognition Lab (HMC Lab) is a dynamic research environment focused on understanding the computational principles of human learning. As it transitions to TU Darmstadt, the lab will continue to explore fundamental questions in cognition using cutting-edge methodologies, including online experiments, multi-agent simulations, and AI-driven modeling.

Enkelejda KasneciView profile

Professor

• Human-Centered Technologies
• Artificial Intelligence in Education
• Multimodal Interaction
• +4 more

Prof. Dr. Enkelejda Kasneci is a Distinguished Professor at the Technical University of Munich (TUM), leading the Chair of Human-Centered Technologies for Learning. She holds dual affiliations within TUM School of Social Sciences and Technology and TUM School of Computation, Information and Technology. Her research integrates AI, eye-tracking, and immersive technologies to advance educational paradigms. She directs the TUM Center for Educational Technologies and chairs the MSc program 'AI in Society.' Education: PhD in Computer Science from University of Tübingen (2013), M.Sc. from University of Stuttgart (2007). Earlier roles include Assistant Professor and Dean of Studies at University of Tübingen. Research Focus: Human-centered AI applications in education, multimodal interaction design, and privacy-preserving eye-tracking. Her work bridges technology and pedagogy through projects like AI tutor PEER, VR Classroom, and Privacy-Preserving Eye-tracking. Key Projects: Leads EU-funded projects VIVA (€1.125M), DigiProMIN (€163K), and SARA Kids (€244.8K). Active in policy initiatives like Europe’s AI Imperative. Awards: TUM Heinz Maier-Leibnitz Medal (2024), Liesel Beckmann Distinguished Professorship (2022), and Südwestmetall Research Prize (2014). Grants & Advising: Over €5M in secured funding across 12+ projects. Supervises 14+ PhD researchers and mentors postdocs in AI education and HCI. Labs & Teams: IT-Stiftung EdTech Lab houses advanced VR/eye-tracking setups. Research group includes 20+ members spanning AI, HCI, and educational technology.

• Biomechanics
• Rehabilitation Engineering
• Digital Health
• +4 more

Brian Horsak is a Professor and Head of the Center for Digital Health and Social Innovation at Fachhochschule Steyr. He holds an endowed professorship in Applied Biomechanics and Rehabilitation Research, focusing on integrating advanced technologies like VR/AR, machine learning, and wearable devices into clinical gait analysis and motor rehabilitation. His roles include leading the Institute of Health Sciences and contributing to the Department of Health Sciences and Media and Digital Technologies. Education: Dr. rer. nat. (2012, University of Vienna), Habilitation in Kinesiology (2020, University of Vienna), Master's in Sports Science (2002–2008, University of Vienna). Research interests revolve around improving patient care through biomechanical innovations, including musculoskeletal simulations, gait pattern analysis, and rehabilitation technologies. He leads projects like ReMoCap-Lab (motion capture for motor rehabilitation) and chairs the Applied Biomechanics in Rehabilitation Research initiative. Key achievements include the Lower Austria Innovation Prize (2021), multiple best paper awards, and grants for projects like TRUST AI and VReeze. His work bridges clinical practice with digital health solutions, emphasizing explainable AI (XAI) in gait classification and VR-based balance training. Notable contributions include developing the GaitRec dataset and studies on smartphone-based motion capture reliability. He collaborates internationally, publishing widely in Gait & Posture , Scientific Reports , and IEEE journals. Current projects focus on AI-driven gait analysis, musculoskeletal modeling, and XR applications in healthcare.

• Artificial Intelligence
• Biomedical Signal Processing
• Sleep Research
• +4 more

Maarten De Vos is a Professor at the Department of Electrical Engineering (ESAT) , KU Leuven , with dual appointments in the Faculty of Medicine and Faculty of Engineering Science . He leads interdisciplinary research at the intersection of artificial intelligence and biomedical signal processing.

• Plant Biochemistry
• Specialized Metabolism
• Solanaceae Family
• +4 more

Cornelius Barry is an Associate Professor in the Department of Horticulture at Michigan State University, with affiliations to the Plant Breeding, Genetics and Biotechnology program, AgBioResearch, and the Molecular Plant Sciences Graduate Program. He joined MSU in July 2007 with a 75% research and 25% teaching appointment. Education: PhD and BSc from the University of Nottingham and University College of Wales Current roles: Director of NSF REU Site: Plant Genomics @ MSU His research focuses on the evolution of biochemical diversity within the Solanaceae family , particularly specialized metabolites like terpenoids, flavonoids, and alkaloids. He investigates their roles in plant defense, pollinator attraction, and human applications through genomics, metabolite profiling, and synthetic biology. Recent publications show expertise in alkaloid biosynthesis , trichome chemistry , and metabolic pathway engineering . Key collaborations include teams at Boyce Thompson Institute, Texas A&M, and University of Nottingham. He advises graduate students in Genetics , Biochemistry & Molecular Biology , and Molecular Plant Sciences programs.

Garvin BrodView profile

Research Professor

• Educational Psychology
• Cognitive Development
• Memory and Learning
• +9 more

Prof. Dr. Garvin Brod is a Research Professor at the DIPF | Leibniz Institute for Research and Information in Education and Goethe University Frankfurt, specializing in individualized learning and cognitive development. He is affiliated with the Department of Educational Psychology within the College of Psychology & Sports Science. His research focuses on technology-supported educational interventions, memory and learning dynamics, and the development of knowledge acquisition processes. Brod leads projects like ACHILLES (learning success diagnostics), PROMPT (digital prompting techniques), and VokSi (vocabulary learning), addressing self-regulated learning, reading education, and cognitive strategies. The 13 most recent publications highlight trends in educational technology, self-regulated learning mechanisms, and cognitive development. Key themes include mobile learning interventions, memory retention strategies, and the role of executive functions in belief revision. Brod’s work bridges experimental psychology with practical EdTech solutions for children’s learning challenges. As a visiting fellow at St John's College, University of Cambridge, and head of DIPF’s departments for Individualized Support and Education and Development, Brod collaborates on data-sharing initiatives like ShaReD. He holds a doctorate from Humboldt University and a diploma in psychology from Saarland University.

• Cartography
• Geospatial Analysis
• Urban Planning
• +4 more

Prof. Liqiu Meng serves as Chair of Cartography and Visual Analytics at the Technical University of Munich (TUM). He specializes in advanced geospatial research, digital cartography, and human-technology collaboration frameworks. Current Faculty at TUM Chair of Cartography and Visual Analytics Research Focus: His work bridges cartographic theory with cutting-edge technology, covering topics like 3D urban modeling, AI ethics visualization, geovisual analytics, and spatiotemporal data interpretation. Urban Morphology Analysis AI Ethics Cartography Geovisual Analytics 3D City Data Integration Location-Based Service Design Publications: Recent works (2025-2024) demonstrate expertise in explainable AI for urban analysis, multi-agent systems for geospatial interaction, and advanced spatial modeling techniques. Contact: liqiu.meng@tum.de | contact.lfk@ed.tum.de

Rachel Devorah Wood RomeView profile

Assistant Professor

• Electronic Music
• Creative Coding
• Live Coding
• +12 more

Rachel Devorah Wood Rome is an Assistant Professor in the Electronic Production and Design (EPD) department at Berklee College of Music, where she teaches creative coding, live coding, and music technology. Her work bridges electronic music, improvisation, and critical theory, with a focus on sonic cyberfeminism, archival practices, and the social meanings of sound and space. Ph.D. in Music Composition and Computer Technologies, University of Virginia (2018) M.L.I.S., San José State University (2017) M.A. in Music Composition, Mills College (2013) B.Mus. in Horn Performance, CUNY Queens College (2007) Her research explores superhuman prolongation, opaque complexity, and the re-signification of archaic tools in sonic media. She values machines for their patience and memory, and her creative practice emphasizes critical agency in technology design and use. She performs under the name 'mehetabel' when using archival materials in improvised electronic music. The most recent publications and performances reflect a deep engagement with AI and music, live coding, feminist media theory, and spatialized sound. Her works often involve real-time data processing, generative systems, and collaborative improvisation, situating sound within broader social, political, and technological contexts. Ruth Anderson Installation Prize (IAWM) New Music USA Grant Fellowships from MIT OpenDocLab, Adrian Piper Foundation, Ina GRM, New Museum, and Jefferson Scholars Foundation Residencies at EMS Stockholm, STEIM Amsterdam, MassMoCA, and Cove Park Rome advises emerging artists in electronic music and creative coding, many of whom have gone on to establish their own practices. She is also active in academic service, including curriculum development, faculty governance, and labor organizing as a Councilor for the Berklee Faculty Union. She leads initiatives in DEI, sound design, and coding pedagogy within the EPD department. She has been involved in interdisciplinary collaborations with artists, technologists, and institutions worldwide, presenting work in galleries, festivals, and academic conferences across North America, Europe, and Asia. Her leadership extends to nonprofit arts organizations, where she has served as Executive Director and grantwriter.

Robert J.K. JacobView profile

Professor

• Human-Computer Interaction (HCI)
• Brain-Computer Interfaces (BCI)
• User Interface Design
• +5 more

Robert J.K. Jacob is a Professor of Computer Science at Tufts University, affiliated with the School of Engineering's Department of Computer Science. His research focuses on Human-Computer Interaction (HCI), particularly implicit brain-computer interfaces (BCI) using fNIRS and EEG technologies. He has held visiting positions at University College London, Université Paris-Sud, and MIT Media Lab. Education: Ph.D. in Computer Science from Johns Hopkins University. Research Interests : Jacob's work explores novel interaction techniques, adaptive interfaces, and BCI applications. Current projects emphasize real-time fNIRS-based systems for effortless user input, cognitive workload assessment, and neuroadaptive technologies. His lab investigates how brain signals can enhance user interfaces in domains like music learning, gaming, and urban design. Recent Trends in Articles : Recent publications highlight advancements in BCI design, neuroadaptive systems, and interdisciplinary applications of fNIRS. Work spans theoretical frameworks (e.g., NeuroCHI ethics) to practical tools like the Tufts fNIRS dataset. Key themes include improving BCI calibration, exploring AI's role in urban environments, and integrating affective computing into artistic interfaces. Awards : ACM Fellow (2016) ACM CHI Academy Membership (2007) Best Paper Award at CHI 2016 Advising & Grants : Supervised 15+ Ph.D. alumni in HCI and BCI. Served as Vice-President of ACM SIGCHI and co-chair of UIST/CHI conferences. Active in editorial roles for Human-Computer Interaction and ACM Transactions on Computer-Human Interaction . Labs & Teams : Directs the Tufts HCI Lab in the Joyce Cummings Center. Collaborates with interdisciplinary teams on projects like the Marble Track Audio Manipulator and Reality-Based Interaction Framework.

Michael WeyrichView profile

Professor

• Industrial Automation
• Digital Twins
• Large Language Models (LLMs)
• +7 more

Prof. Michael Weyrich is a faculty member at the Institute of Industrial Automation and Software Engineering (IAS) within the University of Stuttgart , leading the Cluster of Excellence IntCDC . His academic rank is Professor, and he focuses on Industrial Automation , Digital Twins , and Large Language Models (LLMs) for manufacturing and automotive systems. His research explores integrating LLMs into industrial automation for adaptive control, cloud offloading of vehicle functions, and semantic interoperability via Asset Administration Shells . He investigates modular production architectures , connected vehicle systems , and synthetic data generation for autonomous machinery. Recent publications highlight LLM-driven production planning , dynamic sensor calibration , and machine learning for fault detection in electric vehicle powertrains. His work emphasizes real-time data modeling and flexible microservice orchestration .

Samuel LederView profile

Researcher

• Robotics
• Architectural Design
• Computational Design
• +5 more

Samuel Leder is a doctoral researcher at the Institute of Computational Design and Construction (ICD) under the Cluster of Excellence IntCDC at the University of Stuttgart. His work focuses on the integration of robotics and architectural design, particularly in developing distributed robotic systems for timber construction. He has been actively involved in research projects such as RP 19-1 – Robotic Kinematic System for Parallel Construction and RP 19-2 – Co-Design for Distributed Cooperative Multi-Robot Systems . Additionally, he serves on the Equal Opportunity Commission at ICD. Bachelor of Design in Architecture (summa cum laude), Washington University in St. Louis Bachelor of Applied Science in Systems Science and Engineering (magna cum laude), Washington University in St. Louis MSc in Architecture via the Integrative Technologies and Architectural Design Research (ITECH) program, University of Stuttgart Samuel’s research explores the synergies between agent-based modeling , robotic systems , and architectural design . His work aims to create minimal robotic machines capable of constructing complex spatial assemblies, particularly with timber structures . He investigates the co-design of robots and the structures they build, emphasizing modular systems and kinematic behaviors . Recent publications highlight advancements in digital twins , adaptive assembly , and human-robot collaboration for timber construction. The 15 most recent articles reveal trends in collective robotic construction , agent-based modeling , and material-robot interaction . These works emphasize timber fabrication , modular systems , and interactive simulation for large-scale construction tasks. Key sub-fields include adaptive assembly , cyber-physical systems , kinematic control , and human-guided robotics . Scientific Awards: German Academic Exchange Service (DAAD) Award for Outstanding Achievement Deutschlandstipendium Samuel’s research is conducted within the ICD at University of Stuttgart , where he collaborates on the Wood Building Systems for Distributed Robotics associated project. His work bridges architecture , robotics , and computational design , aiming to redefine on-site construction methodologies through innovative robotic systems.

Thomas K. UchidaView profile

Associate Professor

• Biomechanics
• Dynamical Systems
• Musculoskeletal Modeling
• +7 more

Thomas K. Uchida is an Associate Professor in the Department of Mechanical Engineering at the University of Ottawa, a position he has held since May 2024. Prior to this promotion, he served as an Assistant Professor at the same institution from October 2018 to May 2024. Before joining the University of Ottawa, Dr. Uchida was an Engineering Research Associate (April 2015-August 2018) and Simbios Distinguished Postdoctoral Fellow (July 2012-April 2015) in the Department of Bioengineering at Stanford University. Dr. Uchida's research focuses on the modeling and simulation of dynamic systems, with particular emphasis on human movement biomechanics. His work spans multiple areas including: Simulation-guided design of assistive devices for improving mobility Modelling musculotendon dynamics and energy expenditure Parameter identification and model reduction methods Impact and contact dynamics Development of computational tools for biomechanical analysis He is a co-author of the book "Biomechanics of Movement: The Science of Sports, Robotics, and Rehabilitation" published by MIT Press, and actively contributes to the development of OpenSim, an open-source software platform for modeling musculoskeletal systems and generating simulations of human and animal movement. His work on OpenSim was featured on the cover of PLoS Computational Biology. Dr. Uchida's recent publications demonstrate strong activity in biomechanics, robotics, and computational modeling. His work bridges engineering principles with biological applications, particularly in understanding human movement mechanics. Key trends include applying machine learning to gait analysis, developing enhanced spine models, analyzing human balance stability with time delays, and advancing musculoskeletal simulation techniques. As an academic advisor, Dr. Uchida currently supervises seven graduate students: Firas Baklouti (expected completion August 2025) Shahin Sharafi Kazem Alambeigi Jiawei Gao Yuzhen Yan Manuel Lucas De Oliveira Blake Scott Miller Dr. Uchida collaborates with research teams focused on biomechanics and movement science. His work with OpenSim places him within an international community of researchers developing computational tools for biomechanical analysis, connecting mechanical engineering with biomedical applications in sports, robotics, and rehabilitation.

Kede MaView profile

Associate Professor

• Image Quality Assessment
• Computational Photography
• High Dynamic Range Imaging
• +7 more

Kede Ma is an Associate Professor in the Department of Computer Science at City University of Hong Kong (CityUHK). He received his B.E. from the University of Science and Technology of China (USTC) in 2012, and MASc and Ph.D. degrees from the University of Waterloo in 2014 and 2017, respectively. From 2018 to 2019, he was a Research Associate with the Howard Hughes Medical Institute and New York University. Prof. Ma has been named to the Highly Cited Researchers list by Clarivate Analytics in 2024 and currently serves on the editorial boards of IEEE Transactions on Image Processing, IEEE Transactions on Information Forensics and Security, and IEEE Signal Processing Letters. Prof. Ma leads the Multimedia Analytics (MA) Laboratory, an interdisciplinary research group focused on computational vision, computational modeling of human visual perception, perceptual multimedia signal processing, quality assessment, and multimedia forensics. His research spans computational photography, high dynamic range imaging and rendering, omnidirectional video analysis, camera processing pipeline design, and artificial intelligence safety in multimedia systems. His work integrates machine learning techniques including reinforcement learning, generative modeling, self-supervised learning, and continual learning for multimedia signal processing applications. His recent publications demonstrate a strong focus on image quality assessment, deep learning for multimedia processing, and multimedia forensics. His work bridges theoretical computer vision principles with practical applications in multimedia systems. The research trends show increasing integration of foundation models with specialized multimedia processing tasks, particularly in quality assessment and security applications. Highly Cited Researchers list by Clarivate Analytics (2024) Best Paper Award at IEEE International Conference on Virtual Reality and Visualization (2021) Best Paper Runner-Up at International Joint Conference on Artificial Intelligence Workshop (2021) Top 10% Award at IEEE International Conference on Image Processing (2015) Finalist for the Governor General's Gold Medal, University of Waterloo (2017) Spotlight presentation at NeurIPS (2022) Highlight paper at ICCV (2025) Oral presentation at ICLR (2025) Prof. Ma advises numerous PhD students and postdoctoral fellows in the MA Laboratory. His research is supported by various grants enabling work in multimedia analytics, image processing, and computer vision. The laboratory maintains active collaborations with researchers at institutions including SUSTech, ZJU, and HIT. Current projects focus on advancing image quality assessment methodologies, developing more robust deep learning techniques for multimedia forensics, and exploring new approaches to HDR imaging and omnidirectional video processing. The Multimedia Analytics Laboratory maintains a strong focus on both theoretical foundations and practical applications of multimedia processing. Current research directions include integrating large language models with image quality assessment, developing more robust deepfake detection methods, and advancing techniques for continual learning in multimedia applications. The lab emphasizes rigorous evaluation methodologies and maintains multiple datasets for multimedia quality assessment research.

Anthony TUNG Kum HoeView profile

Professor

• Database Systems
• Indexing and Searching Complex Objects
• Time Series Analysis
• +13 more

Anthony TUNG Kum Hoe is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he has established himself as a leading researcher in database systems and data mining. He is also affiliated with the NUS Graduate School for Integrative Sciences and Engineering and serves as a SINGA supervisor. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (2001), an M.Sc. in Information Systems & Computer Science from NUS (1998), and a B.Sc. with 2nd Class Upper Honours in Information Systems & Computer Science from NUS (1997). Professor Tung's research spans several interconnected areas within database systems and data mining. His primary focus is on developing efficient methods for indexing and searching complex data structures including time series, trajectories, trees, graphs, and high-dimensional objects. He has pioneered work in visual query processing, keyword search, and ranking systems. His GENIE (Generic Inverted Index) and LAMP (semi-Lazy Mining Paradigm) projects represent significant contributions to big data analytics, particularly in handling the 'variety' aspect of big data by providing unified frameworks for processing diverse data structures while preserving semantic meaning. His research bridges theoretical database concepts with practical applications in visual data mining, collaborative analytics, and just-in-time model construction. His recent publications reveal a clear evolution from traditional database research toward more complex analytics on diverse data types. While maintaining his core expertise in database indexing and query processing, his work has expanded to incorporate machine learning techniques, particularly in areas like nearest neighbor search, anomaly detection, and predictive analytics. There's a noticeable trend toward interdisciplinary applications, with publications spanning computer vision, natural language processing, transportation systems, and social computing. His research group consistently publishes in top-tier venues including SIGMOD, VLDB, ICDE, and KDD, demonstrating both theoretical rigor and practical relevance. 2005 Best Paper Award for 'Indexing DNA Sequences Using q-grams' 2007 Invited panel speaker on 'Advice for a successful database researcher career in Asia' at SIGMOD 2010 Guest Lecturer for VLDB Database School 2012 VLDB 2012 Research PC Co-chairs 2015 10 Years Best Paper Award, DASFAA 2015 Invited to SIGMOD 2008 and SIGKDD 2008 Program Committees Professor Tung has supervised numerous PhD students and research associates throughout his career, including notable researchers like Zhang Zhenjie (recipient of the 2007 President Graduate Fellowship) and Wang Nan (published in SIGMOD'08). His research group has been consistently productive, with students publishing in top conferences including SIGMOD, ICDE, and VLDB. His professional service is extensive, having served as PC Chair for COMAD'06, Research PC Co-chair for VLDB 2012, and on program committees for virtually all major database and data mining conferences over the past two decades. His research has been supported by various grants that have enabled significant contributions to database technology. His GENIE and LAMP projects represent a cohesive research direction focused on developing systematic approaches to big data analytics. GENIE provides a unified platform for storage and retrieval of big data with various structures, while LAMP introduces a novel paradigm for predictive analytics that combines the strengths of lazy and eager learning approaches. These projects have evolved to incorporate GPU acceleration and parallel processing capabilities, reflecting his commitment to addressing real-world scalability challenges in data-intensive applications.

• Topology of Complex Systems
• Higher-order Networks
• Topological Neuroscience
• +7 more

Giovanni Petri is a Professor in the Network Science Institute at Northeastern University London, where he joined in June 2023. Previously, he held positions at CENTAI as a Principal Researcher and at IMT Lucca as a Guest Scholar, with earlier affiliations at ISI Foundation and Imperial College London. His educational background includes a PhD in Complex Networks from Imperial College London (2012), an MSc in Theoretical Physics from the University of Pisa (2008), and a BSc in Physics from the University of Pisa (2005). Petri's research spans the analysis of neuroimaging data and AI systems with topological techniques, the formalization of cognitive control models with tools of statistical mechanics and network theory, and the study of the predictability of socio-technical systems. His work in Topological Neuroscience explores brain architecture using algebraic topology, while his research in Cognitive Neuroscience focuses on neural mechanisms underlying human cognition. He is particularly known for his work on higher-order networks, using mathematical frameworks like hypergraphs and simplicial complexes to model systems with multi-way interactions. His recent publications (2023-2025) demonstrate a strong focus on higher-order network theory applied to neuroscience, with particular emphasis on topological approaches to brain connectivity, social contagion models, and the physics of complex systems. These works reveal consistent themes in understanding how multi-body interactions shape system dynamics across biological, social, and technological domains. European Research Council Consolidator Grant (RUNES: Reconstruction and unification of neural and ecological systems, 2024) As Principal Investigator of the NPLab, Petri advises numerous PhD and postdoctoral researchers including Marilyn Gatica, Andrea Santoro, and Simone Poetto. His RUNES project, funded by the ERC Consolidator Grant, represents a significant research initiative. The lab maintains active collaborations with CENTAI, Project CETI (Cetacean Translation Initiative), and various international institutions. The NPLab investigates the role of topology and geometry in the collective dynamics of complex systems, ranging from neuroscience to society, using statistical mechanics, algebraic topology, and innovative computational approaches. Current projects include Topological Neuroscience, Cognitive Neuroscience, Higher-order Networks, Project CETI, and RUNES.