Karl Gustafsson is a Professor at Stockholm University within the Department of Economic History and International Relations. His work bridges security studies , collective memory , and digital research methods in international relations. Current Role: Professor of International Relations, Stockholm University Previous Positions: Post-Doc at Lund University, Researcher at Swedish Institute of International Affairs Visiting Appointments: University of Tokyo, Osaka University, Keio University, Academia Sinica, Nordic Institute of Asian Studies Research Focus: Karl specializes in memory politics , ontological security , and anxiety-driven international dynamics , particularly in Sino-Japanese relations and digital platforms . His studies explore how historical narratives and online memory construction influence state identity and policy. Awards & Grants: Recipient of the Wang Gungwu Prize (2014) and Stockholm University Association’s award (2011). Holds Swedish Research Council and Marianne & Marcus Wallenberg Foundation grants for projects on apologies in international politics and digital memory in East Asia . Publications: His work appears in journals such as International Studies Quarterly , European Journal of International Relations , and Memory Studies , addressing topics like narrative power in East Asia, anxiety’s role in policy change , and digital humanities applications in IR.
Charles D. Sprenger is a Professor of Economics at the California Institute of Technology (Caltech), where he has served since 2020 and held the position of Executive Officer from 2022 to 2025. He is affiliated with Caltech's Division of the Humanities and Social Sciences (HSS) and holds key roles at the Ronald and Maxine Linde Institute of Economic and Management Sciences and the Center for Theoretical and Experimental Social Sciences (CTESS). His external appointments include Board of Editors for the American Economic Review and Associate Editor roles for the Journal of the European Economic Association and Quantitative Economics . His educational background includes a B.A. from Stanford University (2002), an M.Sc. from University College London (2005), and a Ph.D. from the University of California, San Diego (2011). These credentials established his foundation in economic theory and experimental methodology. Sprenger is a leading behavioral and experimental economist specializing in intertemporal decision making and choices under uncertainty. His research designs innovative experiments across diverse contexts—from food deserts in the United States to polio vaccination drives in Pakistan—to test the validity of standard economic models. His work consistently reveals significant deviations from rational choice theory, particularly regarding time inconsistency, risk preferences, and reference-dependent behaviors. He has pioneered methods for measuring time preferences and testing cumulative prospect theory, with implications for public policy and behavioral interventions. Analysis of his 15 most recent publications (2015-2024) shows a cohesive research program centered on behavioral anomalies in decision making. His work bridges laboratory precision with real-world field applications, demonstrating how psychological factors like procrastination and loss aversion manifest in high-stakes environments. Key trends include the development of tailored incentive structures, validation of rank-dependent utility models, and exploration of dynamic inconsistency across domains including health, finance, and public policy. His notable recognition includes: Sloan Foundation Fellowship (2016-2018) Sprenger actively contributes to the academic community through editorial leadership and collaborative research. His work has been featured in Caltech news for projects like "Reducing Procrastination with Tailored Incentives" (2023) and the graduate summer program "Experimental Economics in Theory and Practice" (2023). Though specific advisees aren't listed, his teaching of advanced courses like Experimental Economics (SS 212 abc) indicates mentorship of graduate researchers. He secures significant research funding through fellowships and institutional support, enabling large-scale field experiments. As a core member of CTESS, Sprenger leads a multidisciplinary team conducting cutting-edge experimental economics research. His lab integrates theoretical modeling with empirical validation, focusing on how behavioral insights can improve policy design in areas like tax compliance, vaccination programs, and financial decision making. Current work emphasizes scalable interventions for procrastination and preference-based incentive customization.
Pranav Anand is a Professor in the Department of Linguistics at the University of California, Santa Cruz (UCSC). He currently serves as the Faculty Director of the Humanities Institute at UCSC since July 2023. His research focuses on the interplay between context, interpretation, and grammatical perspective, particularly in areas like de re/de se contrasts, evaluative predication, and indexical shift. He has contributed to studies on narrative structures, evidential restrictions, and the syntax-semantics interface in sluicing. Dr. Anand has taught a variety of courses including Ling 119: Narratives , Ling 231: Semantics A , and special topics like Invented Languages: From Elvish to Esperanto . His work bridges theoretical linguistics with computational methods, evidenced by collaborations in projects such as the Santa Cruz sluicing dataset and analyses of political discourse in online commentary. His research has been published in journals like Linguistics and Philosophy , Language , and Discourse and Society , with a focus on semantics, pragmatics, and narrative linguistics. He has also contributed to computational linguistics initiatives, including the development of annotated corpora for sentiment analysis and argumentation studies. Dr. Anand's academic contributions span both theoretical exploration and applied computational linguistics, reflecting his interdisciplinary approach to understanding language structure and usage.
Freda Shi is an Assistant Professor at the David R. Cheriton School of Computer Science at the University of Waterloo and a Faculty Member at the Vector Institute, where she holds a Canada CIFAR AI Chair. She joined the University of Waterloo in July 2024 after completing her Ph.D. at the Toyota Technological Institute at Chicago. Educational Background: Ph.D. in Computer Science, Toyota Technological Institute at Chicago (2024), advised by Professors Karen Livescu and Kevin Gimpel Bachelor's degree in Intelligence Science and Technology (Computer Science Track) with a minor in Sociology, Peking University (2018) Dr. Shi's research focuses on computational linguistics and natural language processing, particularly on deeper understandings of natural language and the human language processing mechanism. She is especially interested in learning language through grounding, computational multilingualism, and related machine learning aspects. Her work aims to inform the design of more efficient, effective, safe, and trustworthy NLP systems. She leads the CompLING Lab at the University of Waterloo, which investigates how language models process spatial relationships and acquire linguistic structures through grounded experiences. Her publication record shows a consistent trajectory of high-impact research, with recent work focusing on spatial reasoning in vision-language models, multilingual chain-of-thought capabilities, and grounded language acquisition. She has published in top-tier conferences including ACL, EMNLP, ICLR, and NAACL, with several papers receiving notable recognition including Best Paper Nominee status at multiple venues. Her research bridges theoretical linguistics with practical NLP applications, demonstrating how linguistic insights can improve AI systems. Scientific Recognition: Canada CIFAR AI Chair (2024) Google Ph.D. Fellowship Thesis of Distinction for her doctoral work Multiple Best Paper Nominee awards at major NLP conferences Dr. Shi teaches CS 784: Computational Linguistics and CS 486/686: Introduction to Artificial Intelligence at the University of Waterloo. She actively contributes to the NLP research community through conference participation, program committee service, and collaborative projects. Her research has significant implications for creating more robust, human-like language understanding systems and advancing the field of grounded language learning in artificial intelligence.
Professor Shoon Murray is a distinguished faculty member at American University's School of International Service, specializing in American foreign policy and civil-military relations. Holding a PhD in political science from Yale University, she teaches foundational courses including Analysis of US Foreign Policy (SISU-230) and Foreign Policy: Theory and Decision Making (SIS-689). Education: PhD in Political Science, Yale University Research Focus: Dr. Murray's scholarship critically examines war powers, decision-making theory, and the interplay between public opinion, media, and foreign policy formation. Her work analyzes how political leaders navigate security crises, congressional-authority tensions in military actions, and institutional shifts in diplomatic practices amid global power diffusion. She investigates enduring phenomena like the “rally ‘round the flag’” effect while documenting the post-9/11 militarization of U.S. strategic approaches. Publication Trends: Recent research centers on the evolution of the 2001 AUMF, combatant commanders' diplomatic roles, and State Department-DoD institutional competition. Her scholarship reveals how military actors increasingly shape foreign policy narratives while traditional diplomatic channels erode, with implications for democratic accountability and strategic coherence in counterterrorism operations.
Professor Gabriel Brostow is a faculty member in the Department of Computer Science at University College London (UCL), where he leads research in Computer Vision and Human-Computer Interaction. He also serves as Chief Research Scientist and Senior Director of the R&D Team at Niantic, the company behind Pokémon GO. His work bridges academic research and industry applications, focusing on developing AI systems that enhance human capabilities through what he terms 'Human in the Loop AI'—now commonly referred to as Human-Centered AI. Brostow completed his BS in Electrical Engineering at UT Austin, followed by a PhD with Irfan Essa at Georgia Tech. He then pursued postdoctoral research with Roberto Cipolla's Computer Vision & Robotics Group at Cambridge University as a Marshall Sherfield Fellow, and with Marc Pollefeys in ETH Zurich's CVG Group. His research explores how AI, particularly Computer Vision, can serve as 'super-tools' for professionals across various domains including filmmaking, architecture, robotics, and scientific research. Specific interests include assistive technology for everyday life, authoring systems that maximize user effort, 3D reconstruction, depth estimation, and vision-language models. His work often involves creating systems that are validated through real-world human interaction to ensure practical utility. Analysis of his recent publications reveals a strong focus on practical applications of Computer Vision that directly interact with humans. His research spans 3D scene understanding, depth estimation, sketch-based interfaces, and multimodal AI systems. There's a clear emphasis on creating benchmarks and tools that facilitate human-AI collaboration, with applications in assistive technology, urban planning, filmmaking, and biodiversity monitoring. His work frequently appears at top conferences including CVPR, NeurIPS, ECCV, and CHI. Marshall Sherfield Fellowship Brostow actively mentors PhD students, with current advisees including Ross Murphy, Skanda Koppula, Gizem Unlu, Omiros Pantazis, and Jamie Watson. His alumni include numerous PhD graduates and MSc students who have gone on to successful careers in academia and industry. He emphasizes selecting students based on passion and potential rather than just academic credentials, valuing traits like helpfulness, drive, and hunger to learn. His research is supported through collaborations with major institutions and companies including DeepMind, MIT, and the University of Edinburgh. He leads a research group at UCL that collaborates closely with Niantic's R&D team, creating a unique bridge between academic research and industry application. His team's work frequently involves developing novel Computer Vision techniques that are validated through real-world human interaction, ensuring practical utility alongside technical innovation. The group explores blue-sky research problems with applications ranging from assistive technology to professional tools for filmmakers, architects, and scientists studying diverse environments.
Prof. Albert Postma is a full professor in Experimental Psychology (Clinical Neuropsychology) at Utrecht University. He holds the chair in Clinical Neuropsychology and has served as department head of Experimental Psychology (2010–2017) and later as head of the Psychology Department (FSW, UU). His research focuses on spatial cognition, memory disorders, and sensory impairments, with notable contributions to understanding spatial navigation in neurological patients and the neurocognitive basis of spatial memory. Education: PhD in Psychology (1991, University of Nijmegen). Affiliations: Director of the Memory & Space Lab; Head of Research Committee at Korsakov Centrum Slingedael; Editorial roles in Cognitive Processing and Acta Psychologica . Research Interests: Spatial cognition, neuropsychology of memory (episodic, prospective), sensory deprivation effects (blindness/deafness), and neuroimaging studies of spatial processing. Key projects include EU-funded studies on spatial memory and the NWO Pioneer grant on spatial cognition development. Grants & Awards: Multiple EU Marie Curie grants, NWO Pioneer Grant (2000–2007), and coordination of the EU NEST Fp6 program on spatial orientation. His work emphasizes translational research in clinical settings, such as Korsakoff syndrome interventions. Labs & Teams: Leads the Memory & Space Lab, collaborating internationally on spatial cognition and neurological rehabilitation. Active in multidisciplinary initiatives like the Dynamics of Youth (DoY) research theme.
Carlo Rovelli is a Professeur de classe exceptionnelle in the Department of Physics at Aix-Marseille University, holding adjunct roles at Western University's Department of Philosophy and a Distinguished Visiting Research Chair at the Perimeter Institute. He founded the quantum gravity group at the Centre de Physique Théorique (CPT) and is an associate member of the Rotman Institute of Philosophy. His research focuses on loop quantum gravity, relational quantum mechanics, and the history/philosophy of science. He authored influential popular science books including Seven Brief Lessons on Physics (41 languages, 1M+ copies sold) and Helgoland: Making Sense of the Quantum Revolution . Rovelli's work bridges theoretical physics with philosophical inquiry, exploring foundational questions in quantum mechanics, spacetime structure, and the interpretation of physical theories. His recent publications address topics like quantum information theory, gauge symmetries, black hole evaporation, and cosmological implications of quantum gravity.
Ralf Haefner is an Assistant Professor in the Departments of Brain & Cognitive Sciences and Physics & Astronomy at the University of Rochester, holding this joint appointment since 2014. His interdisciplinary research bridges neuroscience and physics to investigate computational principles of perception and decision-making. Education and professional background: PhD, Oxford University, 1999 Visiting Research Fellow, Department of Neurobiology, Harvard Medical School Swartz Fellow, Sloan-Swartz Center for Theoretical Neurobiology, Brandeis University Haefner's research program centers on computational neuroscience , with primary focus on how the brain forms perceptual beliefs and uses them for decisions through Bayesian modeling . He employs machine learning tools to construct mathematical models explaining neural responses and behavior, particularly in the visual domain. His work addresses neural representation of uncertainty, causal inference mechanisms, and probabilistic computation in cortical circuits. Analysis of recent publications (2023-2025) reveals three dominant trends: (1) causal inference frameworks applied to motion perception and segmentation, (2) Bayesian modeling of perceptual biases and confidence computations, and (3) integration of generative and discriminative neural computations. His work extends beyond traditional neuroscience into scientific methodology through 'Generative Adversarial Collaborations' for improving research discourse. Honors and Awards: Swartz Fellowship, Sloan-Swartz Center for Theoretical Neurobiology NSF CAREER Award (2022) for 'Approximate inference at the intersection of neuroscience and machine learning' Haefner secured significant research funding through his NSF CAREER award, which supports foundational work on probabilistic inference at the neuroscience-ML interface. While specific students aren't listed, his active publication record and lab infrastructure suggest ongoing mentorship of graduate students and postdocs. His research has clinical relevance as shown by studies on perceptual abnormalities in autism spectrum disorder, indicating translational potential for understanding neurological conditions.
Matej Varga is a Scientific Assistant and Postdoctoral Researcher at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, working in the Geosensors and Engineering Geodesy group under Prof. Andreas Wieser since 2021. His research spans geometrical geodesy, physical geodesy, and satellite geodesy, with applications in both theoretical and practical domains. Dr. Varga's research interests focus on spatial, temporal and spectral analysis of geodetic data, with particular expertise in geodetic reference systems and frames, gravity and geomagnetic field modeling at all temporal and spatial scales, and multi-GNSS multi-frequency positioning and monitoring. His work integrates geometrical and physical aspects of geodesy to address complex Earth observation challenges, particularly in infrastructure monitoring and geophysical applications. His recent publications demonstrate a strong trend toward high-precision geodetic applications for major scientific infrastructure, most notably the Future Circular Collider project, alongside important contributions to earthquake impact analysis, geomagnetic network development, and gravity field modeling. His research bridges traditional geodetic methods with modern computational approaches, including machine learning applications for point cloud registration. Dr. Varga is actively involved in the GSEG research group at ETH Zurich, contributing to the development of geodetic infrastructure and reference systems. His work has practical applications in infrastructure monitoring, earthquake analysis, and scientific projects requiring extreme geodetic precision.
Jo-Anne Baird is a Professor of Education and Director of the Oxford University Centre for Educational Assessment (OUCEA) at the University of Oxford, affiliated with St Anne's College. She previously served as Head of the Department of Education and held academic roles at the Institute of Education (University of London) and the University of Bristol. Her research focuses on educational assessment, including systemic structures, examination standards, and collaboration with government and industry partners. Education: She holds a psychology doctorate, an MBA, and an Honorary Doctorate from the University of Bergen (2019). She is Co-Lead Editor of the Oxford Review of Education and has served on advisory boards for Ofqual, the Welsh Government, and the National Reference Test Expert Group. Research Interests: Educational assessment systems, standards maintenance, marking practices, and curriculum-aligned qualifications. Key projects include the 'Perception of Standards in Scotland (PASS)' and studies on GCSE reforms. She has advised parliamentary committees and contributed to policy frameworks like the Teaching Excellence Framework. Awards: Honorary Doctorate from the University of Bergen (2019). Advising and Grants: Supervises doctoral students in areas like assessment standards and policy. Funded projects include 'Examination Reform: Impact of linear/modular GCSEs' (Ofqual) and 'Predictability of the Irish Leaving Certificate' (State Examinations Commission). Labs/Teams: Leads OUCEA, collaborating with international institutions and policy bodies on assessment research and policy.
Dr. Sander Los is an Associate Professor at the Faculty of Behavioural and Movement Sciences (Department of Cognitive Psychology), Vrije Universiteit Amsterdam. He earned his PhD in 1994 with a thesis on 'On the origin of mixing costs: Exploring information processing in pure and mixed blocks of trials' under Prof. Andries Sanders. His research focuses on temporal dynamics of preparatory processes, co-developing the formalized Multiple Trace Theory (fMTP) to explain temporal preparation across time scales (seconds to days). His work integrates cognitive psychology, neuroscience, and computational modeling to explore attentional mechanisms, statistical learning, and spatiotemporal dynamics. Education: PhD in Cognitive Psychology (VU Amsterdam, 1994), postdoctoral research at VU Amsterdam, progressing to Assistant Professor before his current role. Key research areas include visual attention, response inhibition, and long-term memory. He has published over 40 peer-reviewed articles and serves on editorial boards for journals like Attention, Perception, and Psychophysics and Acta Psychologica . Research Interests: His studies investigate how humans prepare for upcoming events temporally and spatially, with recent work on statistical learning guiding visual attention and computational frameworks for temporal preparation. Collaborations emphasize interdisciplinary approaches to understanding attention allocation and neural underpinnings of timing. Grants & Advising: No explicit grants listed, but active in training students (1 supervised PhD thesis). His courses include Methodology, Research Methods, and Practical Skills for Researchers at VU Amsterdam. Labs/Teams: Works closely with colleagues on the fMTP model and statistical learning projects, emphasizing team-based computational and experimental psychology.
Prof. Torsten Wolfgang Kuhlen serves as a Universitätsprofessor at RWTH Aachen University, leading the Teaching and Research Area for Virtual Reality and Immersive Visualization within the Department of Computer Science. He is affiliated with Chair of Computer Science 12 (High Performance Computing), the Visual Computing Institute, and remains an integral part of the RWTH IT Center where his research group operates one of the world's largest Virtual Reality laboratories including the 30 sqm aixCAVE visualization chamber. The group maintains strong connections with Computational Science & Engineering Division, National High Performance Computing Center for Computational Engineering Science (NHR4CES), and VR in Science and Industry Network NRW e.V. Prof. Kuhlen's research spans virtual reality, immersive visualization, and multimodal 3D user interfaces with applications across simulation science, production technology, neuroscience, and medicine. His work combines basic research on advanced methods and algorithms with interdisciplinary collaborations involving RWTH Aachen institutes, Forschungszentrum Jülich, and industry partners. Recent publications demonstrate strong focus on audiovisual perception, immersive analytics, collaborative virtual environments, and practical VR applications in education and manufacturing. His research group has produced significant work on listening effort in virtual environments, immersive authoring techniques, and VR applications for scientific visualization. Notable projects include VRScenarioBuilder for automated vehicle testing and applications in monitoring additive manufacturing processes. The group actively participates in major conferences including IEEE VIS and EuroVis, with several award-winning contributions. Prof. Kuhlen has advised PhD students including Martin Bellgardt who recently completed his doctoral degree on "Increasing Immersion in Machine Learning Pipelines for Mechanical Engineering". The research group maintains state-of-the-art VR infrastructure including the aixCAVE facility which is open to all RWTH research groups.
Barbara Plank is a full professor and chair for AI and Computational Linguistics at Ludwig Maximilian University of Munich (LMU), where she heads the Munich AI and NLP (MaiNLP) lab and co-directs the Center for Information and Language Processing (CIS). She additionally serves as a visiting full professor at the IT University of Copenhagen, maintaining active dual institutional affiliations in computational linguistics and NLP research. Her research focuses on human-centric natural language processing challenges, particularly learning under sample selection bias (domain adaptation, transfer learning) and annotation bias, learning with limited data through continual/semi-supervised/weakly-supervised methods, multimodal learning at language-vision-speech interfaces, and fortuitous supervision for variety-space aware language understanding. She pioneers methodologies addressing human label variation as a critical factor in model robustness rather than mere noise. Recent publications (2024-2025) reveal dominant trends in modeling human label variation across NLP tasks, especially natural language inference and entity recognition, alongside dialectal language processing and LLM evaluation frameworks. Her work systematically investigates how human disagreement in annotations can be leveraged to build more robust, adaptable systems rather than treated as errors. Scientific recognition includes: ERC Consolidator Grant for the DIALECT project advancing natural language understanding for non-standard languages and dialects ACL 2024 Area Chair Award for the paper 'VariErr NLI: Separating Annotation Error from Human Label Variation' Leading the MaiNLP lab at CIS (LMU), she directs research integrated with MCML (Munich Center for Machine Learning), Munich Intelligent Robotics, ELLIS Unit Munich, UniDive, and COST action. Current projects include ERC-funded DIALECT and KLIMA-MEMES, focusing on human-facing NLP solutions for real-world language diversity challenges. She actively shapes the field through ACL leadership as VP-Elect and numerous keynotes emphasizing human-centric approaches. The MaiNLP lab at Akademiestr. 7, 80799 Munich, drives innovation in computational linguistics through interdisciplinary collaboration, maintaining strong ties with European research networks while developing practical applications for language variation and robust NLP systems. The lab's work directly informs her teaching in LMU's Computational Linguistics programs, bridging research and education in cutting-edge NLP methodologies.
Reza Ghabcheloo is a Professor at Tampere University, affiliated with the Faculty of Engineering and Natural Sciences and the Department of Automation Technology and Mechanical Engineering. He leads the Robotics major and the international Automation Engineering program. His research focuses on autonomous mobile machines, robotics, control systems, and safety engineering, with specific interests in construction robotics, sensor fusion, and hydraulic systems. He co-leads the Autonomous Mobile Machines Group and is associated with the Robotics and Intelligent Machines Lab and the Innovative Hydraulics and Automation Lab. His research emphasizes developing autonomous systems for off-road machinery, safe control strategies, and energy-efficient automation. He has published extensively on topics such as reinforcement learning for crane control, radar-based perception, and safety architectures for autonomous systems. His work bridges robotics, control theory, and industrial automation, addressing challenges in heavy-duty machinery and real-world robotic applications. Research Group: Autonomous Mobile Machines Group Labs: Robotics and Intelligent Machines Lab, Innovative Hydraulics and Automation Lab Key Projects: Safety of automated off-road machinery, machine learning for autonomous loading, and trajectory optimization
