Gerd Bruder is an Associate Professor at the University of Central Florida , affiliated with the Department of Computer Science . He previously held positions as a Research Associate Professor at UCF's Institute for Simulation and Training (2016-2022), Post-Doc at University of Hamburg (2014-2016), and Post-Doc at University of Würzburg (2011-2014). Habilitation : Computer Science, University of Hamburg (2017) Ph.D. : Computer Science, University of Münster (2011) M.Sc. : Computer Science (minor in Mathematics), University of Münster (2009) Research Interests : Gerd Bruder's work focuses on Extended Reality (XR) systems, spanning Virtual Reality (VR) , Augmented Reality (AR) , and Mixed Reality (MR) . Key areas include human factors , digital twins , 3D user interfaces , and perceptual modeling , with applications in healthcare, military operations, architecture, and smart environments. Recent Publications highlight innovations in XR locomotion , trust calibration with autonomous agents, spatial cognition , and haptic-visual integration . His 2025 articles address task-switching efficiency in XR and collaborative mixed reality design. Scientific Awards : Best Paper at ACM VRST 2023 2021 TechConnect Innovation Award Best Demo at ACM SUI 2021 Multiple Best Paper Awards (2016-2023) Best Poster & Demo Awards (2015-2020) Patents include systems for medical simulation , smart environment interruption management , and adaptive AR interfaces . His work bridges academic research and practical applications across VR, AR, and MR domains.
Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University. He holds multiple affiliations including membership in Bio-X, Faculty Affiliate status at the Institute for Human-Centered Artificial Intelligence (HAI), and membership in both the Wu Tsai Human Performance Alliance and Wu Tsai Neurosciences Institute. Dr. Wu earned his Ph.D. and S.M. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology before joining Stanford. Dr. Wu's research program focuses on creating AI systems that understand and interact with the physical world through the integration of computer vision, machine learning, robotics, and cognitive science. His work emphasizes physics-based modeling combined with deep learning to develop systems capable of perceiving, reasoning about, and predicting physical interactions. Key research areas include 3D scene understanding, neurosymbolic AI approaches, multimodal perception (combining vision, sound, and language), and embodied intelligence for robotics applications. His lab develops novel frameworks that bridge the gap between neural networks and symbolic reasoning to create more interpretable and robust AI systems. Analysis of Dr. Wu's recent publications reveals a strong trajectory toward integrated multimodal understanding for embodied AI. His work increasingly combines vision, sound, and language processing with physical reasoning to create systems that can interact meaningfully with the physical world. There's a clear progression from foundational computer vision research toward practical robotics applications, with significant emphasis on foundation models for robotics, sim2real transfer techniques, and creating comprehensive datasets for embodied AI research. Dr. Wu's exceptional contributions have been recognized with numerous prestigious awards including the NSF CAREER award (2024), Young Investigator Programs from ONR (2024) and AFOSR (2023), the Okawa research grant (2024), and being named to IEEE Intelligent Systems' 'AI's 10 to Watch' (2024). He has received multiple best paper awards at leading conferences including ICRA (2024), SIGGRAPH Asia (2023), and CoRL (2023). Dr. Wu actively mentors a large cohort of students across multiple levels, serving as primary advisor for doctoral candidates, master's students, and numerous independent researchers. His research is supported by substantial funding from major technology companies including Google, Meta, Amazon, Samsung, and J.P. Morgan, as well as government agencies like NSF, ONR, and AFOSR, reflecting the significance and impact of his work in physical AI and multimodal perception systems. Dr. Wu leads a dynamic research group at Stanford that collaborates extensively with the Wu Tsai Neurosciences Institute and Institute for Human-Centered AI. Current projects include developing neurosymbolic models for computer graphics, creating multisensory datasets like OBJECTFOLDER 2.0 for sim2real transfer in robotics, and building foundation models for embodied intelligence that can understand and manipulate objects with human-like physical intuition.
Professor Catherine Allerton is a faculty member in the Department of Anthropology at the London School of Economics and Political Science (LSE). She specializes in the materialities and mobilities of everyday life, with a focus on place, kinship, childhood, migration, and temporalities in island Southeast Asia. Her fieldwork spans a two-placed village in Flores, Indonesia, and Kota Kinabalu, Sabah, East Malaysia. Her research emphasizes interdisciplinary ethnographic methods, particularly involving child participants to challenge adult-centric approaches. She launched the 'Childhood in the Migrant City' exhibition using child-taken photographs from her ESRC-sponsored research. Current projects include exploring discordant temporalities between migration systems and childhood development, as well as completing a book manuscript on moral economies of belonging. Key research interests include the social and political dimensions of landscapes, the anthropology of children, and the impacts of legal status on marginalized youth. She teaches an LSE course on 'Children and Youth in Contemporary Ethnography' and actively supervises PhD students focusing on childhood, migration, citizenship, and Southeast Asian studies. Her work addresses exclusionary practices tied to race, religion, and legal frameworks in Malaysia and Indonesia. Languages spoken include Bahasa Manggarai, English, Indonesian, and Malay. No part-time status is mentioned, indicating full-time faculty commitment.
Professor Emily S. Cross is a cognitive and social neuroscientist holding dual appointments at the University of Glasgow's School of Psychology & Neuroscience (UK) and Western Sydney University's MARCS Institute (Australia). She directs the Social Brain in Action Laboratory (SoBA), specializing in how embodied experiences shape social perception. Cross earned a BA in psychology and dance from Pomona College, MSc from University of Otago as a Fulbright Fellow, and PhD in cognitive neuroscience from Dartmouth College, followed by postdoctoral training at University of Nottingham and Max Planck Institute. Her research explores experience-dependent plasticity through dance, robotics, and neuroimaging techniques (fMRI, TMS), focusing on four key areas: neural signatures of embodied expertise, neuroaesthetics, visual learning across lifespan, and social engagement with robots. This interdisciplinary work bridges neuroscience, performing arts, and robotics. Cross's research shows strong focus on human-robot interaction dynamics, cultural perceptions of robotics, and the neural correlates of aesthetic experiences. Recent work investigates how social experience shapes human-robot interaction, cross-cultural differences in robot acceptance, and computational approaches to movement analysis. Publications consistently demonstrate methodological innovation through VR, economic games, and large-scale motion capture libraries. Honors & Awards: Jacob Brownowski Award (British Science Association, 2017) Philip Leverhulme Prize in Psychology (2018) World's 50 Most Renowned Women in Robotics (2020) Elected member: Young Academy of Europe Elected member: Royal Society of Edinburgh's Young Academy of Scotland Her ERC Starting Grant funds groundbreaking work on human-robot interaction. Cross leads the Social Brain in Action Lab with focus on mentoring next-generation scientists and research ethics training. The lab employs neuroimaging, training paradigms, and diverse methodologies to study action observation across dance, music, and robotics domains.
Surya Ganguli is an Associate Professor in the Department of Applied Physics at Stanford University, with courtesy appointments in Neurobiology and Electrical Engineering. He serves as Senior Fellow at the Stanford Institute for Human-Centered AI and is affiliated with the Stanford Neuroscience Institute , Bio-X , and Wu Tsai Neurosciences Institute . His research spans theoretical neuroscience, machine learning, and statistical mechanics. Ph.D. , UC Berkeley, Theoretical Physics (2004) M.A. , UC Berkeley, Physics (2000) M.A. , UC Berkeley, Mathematics (2004) M.Eng. , MIT, Electrical Engineering and Computer Science (1998) B.S. , MIT, Physics (1998) B.S. , MIT, Mathematics (1998) B.S. , MIT, Electrical Engineering and Computer Science (1998) His lab explores how higher-level cognitive phenomena emerge from neural network dynamics, focusing on perception, memory, attention, and decision-making . Research themes include statistical mechanics of learning , neural representational geometry , and biologically plausible learning rules . Current work examines nonlinear interactions in neural networks through the Schmidt Science Polymath Award (2023). Key article trends reveal expertise in neural coding limits (2022 Nature), synaptic plasticity models (2022 Neural Computation), and deep learning theory (2022 NeurIPS publications). His 2019 Annual Review chapter on statistical mechanics of deep learning established foundational insights into network criticality. Scientific Awards : NSF Career Award (2019) Simons Foundation Investigator (2016) McKnight Scholar Award (2015) James S. McDonnell Foundation Scholar (2014) Sloan Research Fellow (2013) As advisor, he mentors 10+ doctoral students across Applied Physics, Neurosciences, and Computer Science, including Vamshi Balanaga and Mason Kamb. His lab collaborates with experimental teams at Stanford and beyond, supported by grants from NSF , Simons Foundation , and Swartz Foundation . The Neural Dynamics & Computation Lab unites physicists, mathematicians, and neuroscientists to decode cognition through interdisciplinary methods.
Dr. Cesar Dario Cadena Lerma is a Lecturer at the Department of Mechanical and Process Engineering and a tenured Senior Scientist at the Institute of Robotics and Intelligent Systems (IRIS) at ETH Zurich. He leads the Perception, Mapping and Navigation team within the Robotics Systems Lab (RSL), co-founded and directs the ETH RobotX initiative focusing on educational robotics, and previously held roles at ETH Zurich's Autonomous Systems Lab, University of Adelaide, and George Mason University. His research focuses on robotics perception, particularly in SLAM (Simultaneous Localization and Mapping), semantic scene understanding, and robust perception systems for dynamic environments. Education: PhD in Computer Science and System Engineering from the University of Zaragoza, followed by postdoctoral research at George Mason University and The University of Adelaide. Professional roles include managing director of ETH RobotX and leadership in multi-modal mapping frameworks like maplab 2.0. Research interests emphasize integrating perception and learning in robotics, with a focus on semantic mapping, data association, place recognition, and navigation in unstructured environments. His work bridges traditional SLAM techniques with modern deep learning approaches to create robust, modular systems. Key contributions include the PHASER registration algorithm, SCIM obstacle avoidance framework, and C-Blox dense mapping system. Awards include the Best Paper Award at the 2017 IEEE International Symposium on Safety, Security, and Rescue Robotics. His articles span topics like semantic pointcloud filtering, volumetric mapping, and embodied domain adaptation. He collaborates widely, with over 50 peer-reviewed publications in top venues such as IEEE Robotics and Automation Letters and International Journal of Robotics Research.
Tianmin Shu is an Assistant Professor in the Department of Computer Science at Johns Hopkins University , with a joint appointment in the Department of Cognitive Science . He is the founding director of the Social Cognitive AI (SCAI) Lab and was previously a Research Scientist at MIT, working with Josh Tenenbaum and Antonio Torralba. His research goal is to advance human-centered AI by engineering machine social intelligence —building systems that understand, reason about, and interact with humans in real-world settings. His work is inherently interdisciplinary, integrating machine learning, computer vision, robotics, and social cognition. Research Interests: Theory of Mind Reasoning: Developing models that infer human mental states from multimodal behavioral data. Embodied Assistance: Creating agents capable of assisting humans in physical environments through verbal and non-verbal collaboration. Learning from Human Feedback: Extracting reward-relevant preferences from rich human input to guide agent behavior. Social Scene Understanding: Recognizing and reasoning about group activities and social roles from visual and physical cues. Computational Social Cognition: Modeling how humans perceive and interpret social and physical interactions. Scientific Awards: Outstanding Paper Award at ACL 2024 for "MMToM-QA: Multimodal Theory of Mind Question Answering" Grants & Collaborations: Tianmin Shu has led or co-organized several high-impact workshops and tutorials, including the NeurIPS 2023 Tutorial on "Language Models Meet World Models" and the RSS 2024 Workshop on "Social Intelligence in Humans and Robots". His lab has also developed open-source platforms like VirtualHome-Social and SimWorld for multi-agent interaction research. Lab & Team: As director of the Social Cognitive AI (SCAI) Lab at Johns Hopkins University, Tianmin Shu leads a multidisciplinary team focused on building socially intelligent systems. His lab is located in Malone Hall 213 and collaborates closely with the Departments of Computer Science and Cognitive Science.
Georg Martius is a Full Professor in the Department of Computer Science at the University of Tübingen's Faculty of Science and a Max Planck Research Group Leader at the MPI for Intelligent Systems. Since April 2023, he has been a core member of the DFG-funded Cluster of Excellence 'Machine Learning: New Perspectives for Science,' which received extended funding through 2032 for its mission to integrate machine learning into fundamental scientific discovery processes. His academic foundation includes a PhD from the University of Göttingen and Bernstein Center for Computational Neuroscience (2005), a Diploma in Computer Science from the University of Leipzig (2003), and a visiting research period at the University of Edinburgh's Division of Informatics. Postdoctoral positions followed at the Max Planck Institutes for Dynamics and Self-Organization (Göttingen, 2009), Mathematics in the Sciences (Leipzig, 2010), and IST Austria (2015). Professor Martius's research pioneers the intersection of reinforcement learning, robotics, and tactile sensing, with emphasis on developing autonomous systems capable of natural locomotion, dexterous manipulation, and physical-world understanding. His work bridges theoretical machine learning with practical hardware applications, particularly in creating differentiable simulators, superresolution tactile sensors, and biologically plausible learning frameworks for robotic control. Analysis of his 2024-2025 publications reveals dominant trends in offline reinforcement learning (especially goal-conditioned and diversity-maximization techniques), object-centric representation learning for video understanding, and tactile sensing innovations. A strong thread connects foundation models to world model construction, while his work on differentiable physics engines enables precise collision handling and contact dynamics for real-world robotic control. His leadership roles include directing the Distributed Intelligence research team at Tübingen and contributing to major collaborative initiatives like the Real Robot Challenge and Myochallenge 2022. The Cluster of Excellence appointment represents recognition of his contributions to transforming scientific methodology through machine learning, particularly in automating hypothesis generation and experimental design. Current projects focus on integrating large-scale machine learning with embodied intelligence, advancing tactile perception systems like the Minsight vision-based sensor, and developing neuroplasticity-inspired approaches for robust out-of-distribution detection. His work directly impacts fields requiring physical interaction intelligence, from autonomous navigation to medical robotics, with emphasis on sample-efficient learning from limited real-world data.
Chuang Gan is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences and the Department of Computer Science. His work focuses on advancing artificial intelligence, robotics, computer vision, and embodied agents through interdisciplinary research combining neural networks, physical simulations, and multimodal learning. Research interests include generative models, reinforcement learning, vision-language integration, and scalable autonomous systems. He explores topics like world modeling for robots, adaptive policy learning, and physics-driven AI. His projects often involve creating systems that learn from visual, auditory, and tactile inputs to perform complex tasks such as object manipulation, navigation, and decision-making in dynamic environments. Recent research trends emphasize embodied AI systems capable of long-horizon planning, compositional reasoning, and efficient learning from limited data. His work bridges theory and practice, with applications in robotics, simulation platforms, and multimodal generation. Key contributions include frameworks for 3D scene understanding, adaptive world models, and novel training paradigms for large language models. His research has been applied to robotics platforms like RoboDreamer and UBSoft, focusing on unbounded soft environments. Collaborations involve designing benchmarks for physical scene understanding (e.g., Physion++), and creating tools like DiffTactile for tactile simulation. His work often integrates principles from differential geometry, PDE dynamics, and game theory. Chuang Gan’s research group develops open-source tools and benchmarks, such as the SoftZoo robot co-design platform and the SOK-Bench situated reasoning benchmark. His team emphasizes scalable alignment methods beyond human supervision and explores ethical AI through principles like symmetry-enhanced training.
Gioele Zardini is the Rudge (1948) and Nancy Allen Assistant Professor at MIT's Department of Civil and Environmental Engineering (CEE), with affiliations to the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS). He holds a PhD from ETH Zurich and previously worked as a postdoctoral scholar at Stanford University. His research focuses on co-design of complex systems, autonomous systems, and game-theoretic modeling of transportation networks. Education: BSc and MSc in Mechanical Engineering and Robotics from ETH Zurich (2017–2019), PhD in 2023. He has held visiting roles at nuTonomy Singapore, Stanford, and MIT. Research interests include co-design methodologies, autonomous vehicle systems, compositionality in engineering, and strategic interactions in mobility networks. Recent work emphasizes scalable fleet coordination, safety-critical robotics, and user-centric transportation solutions. Notable awards include the 2024 ETH Doctoral Dissertation Award (Silver Medal), Best Paper at ITSC 2021, and federal grants for enhancing urban transit equity. He leads the Zardini Lab, fostering interdisciplinary collaboration in systems engineering and autonomy. Grants and advising: Received federal grants for transit accessibility projects. His work on Autonomy Talks has produced over 180 recorded lectures, promoting knowledge exchange in autonomous systems. Labs/Teams: Principal Investigator at LIDS, affiliate at IDSS, and founder of the Zardini Lab, focusing on systems co-design, mobility innovation, and game-theoretic frameworks.
Pedro Neves is a Full Professor in the Department of Management and Organizations at Nova School of Business and Economics (Nova SBE), Universidade Nova de Lisboa. His academic background includes a PhD in Industrial and Organizational Psychology/Organizational Behavior from ISCTE-IUL and post-doctoral research at the University of Delaware. He teaches courses in Organizational Behavior, Persuasion and Negotiation, and Entrepreneurship, and actively contributes to executive education programs. His research focuses on leadership processes , particularly the dark side of leadership , interpersonal relationships , organizational change management (especially resistance anticipation), occupational health (including stress perception), and entrepreneurial behavior . His work appears in leading journals such as the Journal of Applied Psychology , The Leadership Quarterly , and Human Resource Management . His recent publications show a consistent focus on leadership dynamics, organizational change, employee well-being, and ethical behavior, often using multi-level and cross-cultural frameworks. He has also edited key volumes such as the Handbook of Methods in Leadership Research and the Research Handbook on Destructive Leadership . He currently serves as Associate Editor of the Journal of Applied Behavioral Science and is on the Editorial Board of The Leadership Quarterly and other journals, reflecting his scholarly impact. Pedro Neves has collaborated with doctoral students and researchers globally and has contributed to both theoretical and applied aspects of organizational behavior. He has not received explicitly mentioned scientific awards in the text, but his editorial roles and publication record indicate significant recognition in the field. He is involved in the Leadership for Impact Knowledge Center and contributes to open and customized executive education programs at Nova SBE.
Professor Anil Seth is a leading cognitive and computational neuroscientist at the University of Sussex, where he holds a professorship in the School of Engineering and Informatics. He is Director of the Sussex Centre for Consciousness Science and Co-Director of the CIFAR Program on Brain, Mind, and Consciousness and the Leverhulme Doctoral Scholarship Programme. His research bridges neuroscience, psychology, philosophy, and AI to investigate the biological basis of consciousness and selfhood. His research interests include: Predictive processing approaches to perception Virtual and augmented reality in self-experience studies Mathematical modeling of perception and emergence Machine learning applications in subjective perception modeling Interoception and selfhood Neural mechanisms of conscious experience The recent publications reflect a strong focus on consciousness, neural dynamics, predictive models, and interdisciplinary approaches. Trends include the use of computational modeling, neurophenomenology, causal analysis, and the ethical implications of emerging neurotechnologies. His work increasingly integrates large language models and data-driven methods for analyzing subjective experience. His scientific awards include: Segerfalk Award Perspectives Award Highly Cited Researcher (Web of Science, 2019–2022) Royal Society Michael Faraday Prize (2023) Seth has secured substantial research funding from the European Research Council (ERC), EPSRC, Wellcome Trust, CIFAR, and the Sackler Foundation. He has supervised numerous research projects and doctoral students through interdisciplinary programs. He leads the Dreamachine project and has been an Engagement Fellow with the Wellcome Trust. He serves on editorial boards including Philosophical Transactions of the Royal Society B and is Editor-in-Chief of Neuroscience of Consciousness . He leads a multidisciplinary research group at the Sackler Centre, bringing together psychologists, mathematicians, neuroscientists, computer scientists, and philosophers. His team conducts innovative research using virtual reality, neuroimaging, and computational modeling to explore the nature of consciousness and self.
Sandro Zanetti, Full Professor of General and Comparative Literature at the University of Zurich's Faculty of Arts and Humanities, specializes in avant-garde research, artistic production processes, and poetics of (im)probability. He studied German, History, and Philosophy at Basel, Freiburg, and Tübingen (1993-1999), earned his PhD in 2005 at Basel's SNSF project, and held positions including Junior Professor at Hildesheim (2008-2011) and DFG network head (2010-2014). Since 2011 at UZH, he progressed from Assistant to Full Professor (2022) and serves as Co-Dean of Studies since 2021. Current affiliations: Department of General and Comparative Literature (UZH), Center for Arts and Cultural Theory (ZKK), Literary Radio Zurich Former roles: ZGW Center (2010-2022), SNF project leader (2015-2019), Co-editor of History of the Present (2016-present) His research explores: Avant-garde genealogies and postwar literary modernity Time perception and aesthetic temporality Writing as cultural technique Interdisciplinary intersections with philosophy, media studies, and art Publications reveal trends in: 2022-2024: Dada historiography, AI-generated literature, Paul Celan's late works 2019-2021: Aesthetic theory, transactuality, Barthesian concepts 2014-2017: Improvisation networks, multilingualism, collaborative writing He co-founded interdisciplinary platforms and maintains editorial roles across 15 anthologies since 2004.
Professor Michael Burke holds a full professorship in Rhetoric at Utrecht University (Faculty of Humanities) and teaches at University College Roosevelt (UCR) in Middelburg. Previously, he served as UU Honours Dean (2016–2021). His research focuses on rhetoric, stylistics, cognitive aspects of language, and education. He earned his Ph.D. in English Language and Literature from the University of Amsterdam, where he also completed his undergraduate studies. Research interests span written, oral, and digital rhetoric, emphasizing pedagogical, cognitive, stylistic, and neuroscientific dimensions. He has held visiting roles at institutions like the University of Cambridge and University of Bologna. Editorial roles include series editor for Routledge’s 'Research Monographs in Linguistics' and board memberships with journals like Language and Literature . Teaching includes undergraduate courses on classical rhetoric, argumentation, and public speaking at UCR, plus leadership and interdisciplinary research modules for Utrecht’s Honours College. He supervises PhD students in the UiL-OTS linguistics research school. Notable publications include The Routledge Handbook of Stylistics (2023) and studies on critical thinking, digital literacy, and cognitive literary science. Professional activities include keynote speaking at conferences like PCST and EARS, and contributions to projects such as ‘Undergraduate Research in the Netherlands’. His work bridges rhetoric, cognitive science, and education, emphasizing practical applications for students and educators.
Emily S. Cross is Professor at the University of Glasgow's School of Psychology & Neuroscience, where she co-directs the Social Brain in Action Lab (SoBA) and leads the ERC Starting Grant 'Social Robots' project. She completed her PhD at Dartmouth College and held positions at Bangor University, Radboud University, and the University of Western Australia before joining Glasgow. Her research explores: Experience-dependent plasticity through dance and motor learning Social cognition in human-robot interaction Emotional processing during action observation Aesthetic perception of movement Cross-cultural differences in technology acceptance Professor Cross uses neuroimaging (fMRI), neurostimulation (TMS, tDCS), and behavioral methods to study how we perceive and learn from human and artificial agents. Her work has been published in Trends in Cognitive Sciences, Journal of Neuroscience, and Philosophical Transactions of the Royal Society. She currently supervises projects on robot-assisted health interventions and develops VR paradigms to study social cognition.
