Matthias Hein is a Professor at the Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Tübingen. His research focuses on Machine Learning , Adversarial Robustness , and Out-of-Distribution Detection , with applications in computer vision and medical imaging. He has received notable recognition including the Best Paper Honorable Mention Prize at ICLR 2021 and Outstanding Paper Award at CVPR 2021. His work includes developing benchmarks like RobustBench and Spurious ImageNet , and frameworks such as Sparse-RS and DIG-IN . His recent publications emphasize adversarial robustness across multiple domains (vision, text), counterfactual explanations for classifiers, and improved OOD detection methods . Collaborators include prominent researchers like Francesco Croce, Julian Bitterwolf, and Alexander Meinke. Scientific Awards : Best Paper Honorable Mention (ICLR 2021) CVPR 2021 Outstanding Paper Award Key Research Areas : Adversarial Robustness Vision-Language Models Medical Imaging AI Neural Network Calibration
Prof. Matthias Nießner is a Professor at the Technical University of Munich , where he leads the Visual Computing Lab . Prior to this, he held a Visiting Assistant Professor position at Stanford University . His work bridges computer vision , graphics , and machine learning , focusing on 3D reconstruction , semantic scene understanding , and AI-driven video synthesis . Prof. Nießner has published over 150 works in top venues like SIGGRAPH , CVPR , and ECCV , with several receiving best paper awards (SIGCHI’14, HPG’15, SPG’18, SIGGRAPH’16 Emerging Tech). His research has garnered international media attention, including features in the New York Times , Wall Street Journal , and MIT Technological Review , as well as TV demonstrations (e.g., Jimmy Kimmel Live for Face2Face technology). Awards : TUM-IAS Rudolph Moessbauer Fellowship (2017–ongoing) Google Faculty Award (2017) Nvidia Professor Partnership Award (2018) ERC Starting Grant (2018, €1.5M) Eurographics Young Researcher Award (2019) Research Trends : 3D Gaussian Splatting for real-time rendering Neural Radiance Fields (NeRF) with mesh supervision Audio-driven facial animation via diffusion models Latent space diffusion for 3D scenes Self-supervised and zero-shot methods for 3D and image analysis As a co-founder and director of Synthesia Inc. , he drives democratization of synthetic media. His YouTube channel has over 5 million views, reflecting his impact beyond academia.
Jonathan T. Barron is a Researcher at Google DeepMind in San Francisco, specializing in Computer Vision , Neural Rendering , and 3D Scene Reconstruction . He earned his PhD at UC Berkeley under Jitendra Malik and has pioneered advancements in NeRF (Neural Radiance Fields) and diffusion-based 3D generation. Research Interests : Computer Vision, Deep Learning, Generative AI, Image Processing, and 3D Reconstruction via Radiance Fields. His work includes Bolt3D for rapid 3D scene generation, CAT3D/CAT4D for text-to-3D/4D, and Zip-NeRF for anti-aliased radiance fields. He has also developed real-time rendering frameworks like SMERF and NeRF-Casting for reflections. Scientific awards: PAMI Young Researcher Award He has served as Area Chair for CVPR, ICCV, and NeurIPS, and his research is widely adopted in applications like Google's Lens Blur , Portrait Mode , and Jump VR .
Xiaoxiao Long is a Tenure-Track Associate Professor at the School of Intelligence Science and Technology, Nanjing University. He joined NJU as an associate professor in February 2024. Previously, he earned his Ph.D. from the University of Hong Kong (HKU) under the supervision of Prof. Wenping Wang (IEEE & ACM Fellow) and Prof. Taku Komura. His educational background includes: Ph.D. in Computer Science from University of Hong Kong Bachelor's degree in Control Science & Engineering from Zhejiang University Dr. Long's research focuses on computer graphics and 3D computer vision, with particular emphasis on 3D Vision, Physical AI, and World Models. His long-term goal is to develop General-Purpose AI with spatial capabilities. His work bridges theoretical understanding of 3D spaces with practical implementations of spatial AI systems, with applications spanning robotics, virtual reality, and augmented environments. He employs innovative neural network approaches and geometric constraints to advance 3D scene understanding and reconstruction. His publication record shows strong momentum with multiple papers accepted to top-tier conferences including CVPR (5 papers in 2025 alone), ICML, ICLR, ECCV, and TPAMI. His research demonstrates a clear progression from foundational geometric estimation techniques (ASN++) toward more comprehensive spatial AI systems. His scientific recognition includes: Excellent Young Scholars Fund (Overseas) from NSFC Dr. Long has successfully mentored numerous students who have published at major venues and gone on to pursue advanced degrees at prestigious institutions including USTC, Beihang University, HKU, UCAS, Virginia Tech, and HKUST. He is currently recruiting Ph.D. and master's students for Fall 2026, seeking candidates interested in pushing the boundaries of 3D computer vision and spatial AI. His laboratory focuses on developing advanced techniques for 3D scene understanding, neural rendering, and physical AI. Current projects span Gaussian-based representations, neural radiance fields, and geometric estimation, with applications in robotics, virtual environments, and spatial reasoning systems.
Dr. Fabian Schulz is a Senior Researcher in Ancient History at the University of Tübingen, where he works in the Department of History within the Faculty of Humanities. He leads a prestigious Junior Research Group under the Emmy Noether Programme of the German Research Foundation (DFG), titled 'Power and Influence: Influencing Emperors between Antiquity and the Middle Ages.' His academic profile also includes association with the Collaborative Research Center (SFB) 923 'Threatened Order - Societies under Stress' and active participation in the 'Tübingen Theosophy' research group. Previously, he contributed to the Heidelberg Academy of Sciences and Humanities project on John Malalas' Chronicle and the University of Tübingen's DFG project 'East and West 400-600 AD.' Dr. Schulz earned his PhD in Classics from Freie Universität Berlin (2006-2010), where he also served as a teaching assistant. During his doctoral studies, he was a Pensionnaire étranger at the École Normale Supérieure Paris. Prior to that, he completed an MA equivalent degree in Classics at Freie Universität Berlin, with time spent as a visiting student at the University of Oxford (1999-2005). His early career included teaching Latin and Greek at a grammar school in Lübeck (2006). His research specializes in power structures and influence mechanisms in ancient societies, with particular focus on how advisors shaped imperial decisions in Late Antiquity. He has made significant contributions to understanding councils of elders in Archaic Greece (especially the Spartan Gerousie), Byzantine chronography (particularly John Malalas' works), and the complex dynamics between Eastern and Western Roman empires during the 5th and 6th centuries. His methodological innovation lies in applying modern sociological models of interpersonal influence to historical analysis of ancient political culture, bridging literary representation with political reality in pre-modern monarchical states. Young researchers award for 'Ratgeber, Experten, Manipulatoren: Einfluss als Machtfaktor und Diskursobjekt in der Antike' As principal investigator of his Emmy Noether project, Dr. Schulz has secured substantial research funding from the German Research Foundation. His collaborative work includes multiple edited volumes on Late Antique history and Byzantine chronography, reflecting his strong international scholarly network. He currently serves as a deputizing member (non-professorial academic staff) on the faculty board of Humanities at the University of Tübingen. His research often intersects with contemporary concerns about power dynamics, political influence, and historical memory, as evidenced by his work on video game representations of ancient history. Dr. Schulz is an active contributor to the online commentary project on John Malalas' chronicle, specifically editing sections of Book XVIII. His work frequently involves detailed textual criticism and analysis of historical sources, with particular attention to how narratives are transmitted and reinterpreted across different cultural contexts. His scholarly approach combines philological precision with broader historical and sociological perspectives.
Max Planck Institute of Colloids and InterfacesGermany
Shrikanth (Shri) Narayanan is University Professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California (USC), with appointments spanning Electrical & Computer Engineering, Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head & Neck Surgery. He serves as Research Director of the Information Sciences Institute and Director of the Ming Hsieh Institute. PhD in Electrical Engineering (UCLA, 1995) Engineer and MS in Electrical Engineering (UCLA, 1992 and 1990) BE in Electrical Engineering (Anna University, India, 1988) His interdisciplinary research focuses on human-centered signal processing and machine intelligence , addressing societal challenges in health, education, defense, and media arts. Key areas include: Behavioral signal processing Affective computing Multimodal signal processing Computational speech science Biomedical applications Scientific Awards : IEEE James L. Flanagan Speech and Audio Processing Award (2025) Edward J. McCluskey Technical Achievement Award (2024) ISCA Medal for Scientific Achievement (2023) Claude Shannon-Harry Nyquist Technical Achievement Award (2023) ACM ICMI Sustained Accomplishment Award (2020) USC Distinguished Faculty Service Award With over 1,000 publications and 19 patents , his work has been commercialized through startups like Behavioral Signals Technologies and Lyssn . He leads transformative university initiatives and has served in editorial roles for top journals including Computer Speech and Language and IEEE Transactions on Affective Computing .
Helmholtz Centre for Environmental ResearchGermany
Joakim Nivre is a Professor at Uppsala University's Department of Linguistics and Philology. He is a leading researcher in computational linguistics, with a focus on dependency parsing, Universal Dependencies (UD) framework development, and multilingual NLP applications. His recent work explores LLMs in climate change discourse analysis, pharmacovigilance explainability, and historical text processing. Key research areas: Dependency parsing theory, Universal Dependencies standardization, LLM evaluation Collaborations: SweSAT-1.0 benchmark development, ClimateEval project, PARSEME integration His 2025-2023 publications demonstrate expertise in explainable AI for healthcare, synthetic data generation for idioms, and multilingual benchmark design. Notably, he co-developed SweSAT-1.0 to evaluate Swedish LLMs and contributed to typology-informed UD revisions. Despite extensive work in NLP, no scientific awards are mentioned in available texts.
Prof. Dr. Henning Klöter is Professor of Modern Chinese Languages and Literatures at Humboldt University of Berlin (HU Berlin) and serves as Dean of the Faculty of Culture, Language and Education. He is affiliated with the Institute of Asian and African Studies and the Department of East Asian Studies. He has been a faculty member since 2015 and leads significant research in sociolinguistics, language planning, and Sinophone studies. His educational background includes studies in Sinology, German Studies, and Linguistics at the University of Trier, Capital Normal University (Beijing), Leiden University, and National Taiwan University. He earned his doctorate from Leiden University in 2003 and completed his habilitation at Ruhr University Bochum in 2010. Henning Klöter's research focuses on multilingualism, language planning, the history of Chinese linguistics in Europe—especially missionary and colonial linguistics—historical sociolinguistics, Overseas Chinese communities, and Taiwan Studies, with regional interests spanning the PRC, Hong Kong, Macao, Singapore, the Philippines, and Taiwan. His work often explores the ideological and political dimensions of language standardization and visibility in public space. Since 2023, he has been Chief Editor of the Journal of Asian Pacific Communication. His recent scholarly output reveals a strong trend in historical and sociopolitical aspects of Chinese language use, particularly across Sinophone regions. Themes include language policy divergence between Taiwan and the PRC, linguistic landscape analysis in Taipei, missionary lexicography, and the evolution of Chinese linguistic standards. His publications bridge historical documentation with contemporary sociolinguistic theory, emphasizing pluricentricity and ideological influences on language. Chief Editor, Journal of Asian Pacific Communication (since 2023) Principal Investigator, ChinGram (Erasmus+ project) Principal Investigator, EMHo (DFG Weave project) He has not received any explicitly mentioned scientific awards in the provided texts. Prof. Klöter is actively engaged in major research initiatives and editorial leadership, contributing significantly to the academic understanding of language in Chinese-speaking societies. He is involved in the Mori-Ōgai Memorial and contributes to the academic community through research projects, publications, and institutional leadership. His work often intersects with cultural memory, colonial history, and linguistic identity in East Asia.
Ohad Fried is an Associate Professor of Computer Science at Reichman University. He was previously a postdoctoral research scholar at Stanford University under Prof. Maneesh Agrawala and completed his PhD with Prof. Adam Finkelstein as part of the Princeton Graphics group. He holds an M.Sc. in Computer Science and a B.Sc. in Computational Biology from The Hebrew University. His research lies at the intersection of computer graphics, computer vision, and Generative AI , focusing on tools, algorithms, and paradigms for photo and video editing and synthesis . His work has been widely recognized in top conferences including CVPR, SIGGRAPH, and ECCV, with recent contributions to tiled diffusion models, expressive 4D facial motion generation, and synthetic image detection. Ohad has received numerous awards, including the Israel Science Foundation personal research grant (2021) , the Outstanding faculty researcher at Reichman University (2022) , and the Siebel Scholar award (2017) . He has advised multiple students in research projects, and his work is covered by media outlets like Wired , The Washington Post , and CNN . Teaching roles include courses at Reichman University such as "GenAI for Games & Entertainment" and "Synthetic Media Detection", and at Stanford University "Computational Video Manipulation". Key Research Themes: Neural Rendering Diffusion Models 3D Facial Animation Image/Video Editing Media Forensics Scientific Awards: ISF Personal Grant (2021) Siebel Scholar (2017) Google PhD Fellowship (2014-2016) Gordon Y.S. Wu Fellowship (2012-2013) Excellence Scholarships
James Urquhart Allingham is a Research Scientist at Google DeepMind , working on the Gemini project. He completed his PhD in the Machine Learning Group at the University of Cambridge under the supervision of José Miguel Hernández-Lobato, with funding from EPSRC, the Michael E. Fisher Studentship in Machine Learning, and the Qualcomm Innovation Fellowship. He was also part of the ELLIS PhD program, advised by Eric Nalisnick at AMLab UvA. Current affiliation: Google DeepMind (Research Scientist) PhD: University of Cambridge (Machine Learning Group) Academic networks: ELLIS PhD program, Darwin College His research focuses on the intersection of Bayesian deep learning and probabilistic methods in deep learning. Key areas include deep generative models , zero-shot classification , prompt engineering , Monte Carlo gradient estimation , and applications to sustainability and climate change . His work has explored energy-based models , neural architecture search , and equivariance in convolutional networks . Selected scientific awards and grants include the Michael E. Fisher Studentship , Qualcomm Innovation Fellowship , and MPhil in Advanced Computer Science with Distinction . He has collaborated with institutions such as the Amsterdam Machine Learning Group (AMLAB) and University of the Witwatersrand .
Anton Ehrmanntraut is a researcher at the University of Würzburg, affiliated with the Chair of Computational Philology and Modern German Literary History. His work bridges computational methods with literary and linguistic analysis. Institution: University of Würzburg Role: Researcher Location: Emil-Hilb-Weg 23, Campus Hubland Nord Contact: anton.ehrmanntraut@uni-wuerzburg.de Research Focus: Computational Linguistics Digital Humanities German Literary History Natural Language Processing Computer Science Publishing Trends: Recent publications demonstrate a dual focus: (1) advancing NLP techniques for German texts (e.g., ModernGBERT, text normalization, literary pipelines) and (2) theoretical computer science contributions to complexity classes like UP, DisjNP, and DisjCoNP.
Lisa Zehnter is a Researcher at the Center for Civil Society Research within the WZB Berlin Social Science Center, where she contributes to the Manifesto Project—an international initiative analyzing election manifestos from over 60 countries to map party positions and preferences. Her role centers on political communication research using computational text analysis methodologies. Her academic background includes: Doctorate in Populist Political Communication, Humboldt University Berlin Master's Degree in Social Sciences, Humboldt University Berlin Bachelor's Degree in German Literature and Social Sciences, Humboldt University Berlin (with a semester abroad at the University of Gothenburg) Zehnter specializes in the intersection of populism, political discourse, and computational social science. Her work employs advanced text analysis to dissect election manifestos and political communication strategies, particularly examining populist rhetoric, gender-fair language usage, and crisis responses like the pandemic. She develops hybrid methodologies combining manual and automated coding to enhance analytical precision in large-scale textual datasets. Her 2021-2025 publications reveal a concentrated focus on German politics—especially the Alternative for Germany (AfD) party—and cross-national manifesto analysis through the Manifesto Project. Key trends include methodological innovations in text analysis, scrutiny of populist communication during crises, and longitudinal studies of party positioning in European democracies. No scientific awards were documented in the source material. No information regarding student advising or research grants was provided in the available text. She actively participates in the Manifesto Project (MARPOR), the Observatory of Political Texts in European Democracies (OPTED), and a collaborative initiative on hybrid text analysis coding techniques, working within international research teams to advance political text analysis frameworks.
Prof. Dr. Erik Rodner is a faculty member at the University of Applied Sciences Berlin (HTW Berlin), where he serves as a Professor for Machine Learning and Data Science. He also contributes to the School of Engineering Sciences - Technology and Life. His research spans computer vision, machine learning, and biomedical applications, with a focus on learning with limited data, robust visual recognition models, and medical image analysis. He has developed innovative methods for medical diagnostics, industrial classification, and anomaly detection. Recent publications (2025-2016) highlight his expertise in visual in-context learning, semi-weakly segmentation, and active learning frameworks. He has collaborated with institutions such as ZEISS Group, Friedrich Schiller University Jena, and UC Berkeley. Scientific Awards: Award for Excellent Teaching (2023)
Daniel Kasper is a researcher at the University of Hamburg's Faculty of Education, specializing in Educational Science with a focus on International Educational Monitoring and Reporting. He currently serves as the National Project Manager for TIMSS 2027, continuing his leadership role from previous TIMSS cycles (2023, 2019). His work centers on large-scale educational assessments, statistical methodology, and primary education research. Dr. Kasper completed his Habilitation in Educational Science with special consideration of empirical educational research at TU Dortmund (2012-2020), followed by his PhD in Educational Science at the same institution (2010-2012). He earned his Diploma in Educational Science from the University of Münster (2001-2007). His research interests span evaluation of education systems, primary school research, educational disparities, and advanced statistical methods in education. He has developed significant expertise in TIMSS methodology, multilevel modeling, and analysis of large-scale assessment data, with numerous publications addressing methodological challenges in international comparative studies. Analysis of his 15 most recent publications reveals a strong focus on TIMSS methodology and results, particularly regarding mathematics and science competencies in primary education. His work demonstrates expertise in statistical methodology for educational assessment, with several publications developing and refining analytical techniques for large-scale data. A recurring theme is examining educational disparities related to student composition, socioeconomic factors, and gender differences. Dr. Kasper has served as National Project Manager for multiple TIMSS cycles (2019, 2023, 2027) and has been involved in PIRLS studies, demonstrating sustained leadership in major international educational assessments. His work bridges methodological innovation with practical application in educational monitoring. He teaches courses across all academic levels, including 'Introduction to Empirical Research Methods' for Bachelor students, 'Methods of Empirical Educational Research' for Master students, and advanced workshops on longitudinal scaling and the Rasch model for doctoral candidates. His teaching reflects his dual expertise in educational research methodology and statistical analysis.
Leibniz Institute for Media Research | Hans-Bredow-InstitutGermany
Laura State serves as a Research Fellow at the Humboldt Institute for Internet and Society (HIIG) in Berlin, Germany, where she contributes to the AI & Society Lab's Impact AI project. This initiative develops transdisciplinary auditing methodologies to evaluate artificial intelligence systems' contributions to societal transformation and ecological sustainability through rigorous impact assessment frameworks. Her academic credentials include: PhD in Data Science from Scuola Normale Superiore, Pisa, Italy (Advised by Salvatore Ruggieri and Franco Turini) MSc in Neural Information Processing from the University of Tübingen BSc in Physics from the University of Rostock State's research synthesizes hard sciences with social perspectives to investigate AI's societal and planetary implications. She specializes in transparency and accountability mechanisms for non-interpretable machine learning models, developing assessment methodologies to determine how AI can foster sustainable futures. Her interdisciplinary approach integrates technical AI development with regulatory frameworks and ecological impact analysis, emphasizing real-world applicability through industry-academia collaboration. Her 2023-2025 publications reveal a cohesive research trajectory centered on explainable AI and regulatory compliance, particularly regarding GDPR requirements. Key themes include legal-technical alignment for explanation systems, bias/fairness policy frameworks, and innovative evaluation tools like REASONX. The work consistently bridges machine learning theory with societal accountability, demonstrating methodological rigor in translating technical capabilities into public-interest applications. As a core member of HIIG's AI & Society Lab, State collaborates on transdisciplinary teams examining AI's role in sustainability transitions. The Impact AI project coordinates researchers from computer science, law, and social sciences to develop evaluation frameworks that measure AI's contribution to UN Sustainable Development Goals, with active engagement in policy dialogues and public science initiatives like Lange Nacht der Wissenschaften.
