Prof. Dr. Sebastian Conrad holds the Chair of Modern History at Freie Universität Berlin , specializing in Global History , (Post-)colonial History , and Intellectual History . He joined the faculty in 2010 after teaching at European University Institute in Florence and serving as visiting scholar at institutions including NYU Abu Dhabi and The New School, New York. Director of MA "Global History" (FU Berlin/Humboldt University joint program) Director of Graduate School "Global Intellectual History" Principal Investigator, Cluster of Excellence "Temporal Communities" (2024) Research Interests focus on transnational historical frameworks , globalization of historical narratives , and postcolonial historiography . His latest book Die Königin: Nofretetes globale Karriere (2024) analyzes cultural globalization through Nefertiti's iconography, while his current project explores global history of bodily aesthetics and identity formation since the 19th century. Recent Publications include: 2022: "Empire and Nationalism" (American Historical Review) 2021: "Globalizing the Beautiful Body" (Journal of World History) 2020: "Greek in Their Own Way" (American Historical Review) Scientific Honors : Elected member, Berlin-Brandenburg Academy of Sciences and Humanities (2024) Elected member, Academia Europaea (2024) Finalist, German/NDR non-fiction book awards (2024) for Nefertiti study Academic Leadership includes editorial roles at Geschichte und Gesellschaft , Past & Present , and Globalgeschichte book series (Campus Verlag). His work bridges historical analysis with contemporary debates on memory, identity, and global cultural dynamics.
Benjamin Lange is a Research Professor and Junior Research Group Leader in the Ethics of AI and Machine Learning at Ludwig Maximilians University of Munich (LMU) and the Munich Center for Machine Learning (MCML). He is also an Associate Research Fellow at the Oxford Uehiro Institute for Practical Ethics and a member of LMU's Zentrum für Ethik und Philosophie in der Praxis (ZEPP). His research bridges foundational ethical theory with practical challenges in technology, business, and organizational ethics. Lange holds a BSc from the London School of Economics, and a B.Phil and D.Phil from the University of Oxford. He previously held visiting positions at Google’s Responsible Innovation team and the University of Hamburg. His research focuses on ethical frameworks for AI, digital ethics, and corporate responsibility. Key themes include the ethics of human-AI collaboration, moral partiality in relationships, and organizational ethics. He has advised the European Commission on AI governance and developed assurance audit methodologies for algorithmic systems, including work for NYC’s AI Bias Law. His consulting practice focuses on helping organizations build ethical cultures through capabilities like moral imagination and compliance frameworks. Lange’s work has been featured in media such as CBS News, Business Insider, and ZDF heute journal. He is completing a book on the ethics of interpersonal relationships and partiality, aiming to connect philosophical insights with practical applications. His recent publications explore moral imagination in engineering teams, bias mitigation in AI systems, and the ethical implications of digital duplication technologies. He advises Fortune 500 companies, startups, and public institutions across sectors including finance, technology, and education. His approach emphasizes aligning ethical practices with organizational growth and social impact through tailored ethical frameworks and stakeholder engagement strategies.
Jun Liu is a distinguished scientist and academic, serving as a Battelle Fellow at Pacific Northwest National Laboratory (PNNL) and holding the position of Campbell Chair Professor at the University of Washington. His career spans over three decades in materials science and energy storage research, with significant leadership roles including Director of the Battery500 Consortium, a major DOE initiative focused on developing next-generation battery technologies. Dr. Liu earned his Bachelor's degree in Chemical Engineering from Hunan University, followed by a Master's degree in Ceramic Engineering and a Ph.D. in Materials Science and Engineering, both from the University of Washington. His educational background provided the foundation for his extensive career in advanced materials development. Dr. Liu's research focuses on the development, synthesis, and characterization of new materials for energy applications, with particular emphasis on battery technologies. His work spans lithium-ion batteries, lithium-sulfur systems, redox flow batteries, and magnesium-based energy storage solutions. He has pioneered approaches to improve energy density, cycle life, and safety of battery systems through innovative materials design and interface engineering. Analysis of Dr. Liu's recent publications reveals a strong focus on practical battery applications, with particular attention to lithium metal anodes, solid electrolyte interphases, and high-energy battery systems. His research increasingly addresses the challenges of translating laboratory discoveries into commercially viable battery technologies, with growing emphasis on pouch cell development and real-world performance metrics. Distinguished Inventor of Battelle (2007) PNNL's Inventor of the Year (2012, 2016) Electrochemical Society Battery Division Technology Award DOE EERE Exceptional Achievement Award PNNL Lifetime Achievement Award Fellow of the American Association for the Advancement of Science Fellow of the Materials Research Society Member of the Washington State Academy of Science Dr. Liu has secured substantial research funding through his leadership of the Battery500 Consortium and other DOE initiatives. He has mentored numerous researchers and students throughout his career, contributing to the development of the next generation of energy storage scientists. His research group at PNNL collaborates extensively with academic institutions, national laboratories, and industry partners to advance battery technology. Dr. Liu leads the Battery500 Consortium, a major collaborative effort involving multiple national laboratories, universities, and industry partners focused on developing lithium-metal batteries with significantly higher energy density than current technologies. His research group at PNNL maintains state-of-the-art facilities for materials synthesis, characterization, and battery testing, enabling comprehensive investigation of next-generation energy storage systems.
Prof. Dr. Oliver Reiser is a full Professor at the Institute of Organic Chemistry within the Faculty of Chemistry and Pharmacy at the University of Regensburg. His research group focuses on cutting-edge developments in organic synthesis, particularly in the areas of photocatalysis and visible light chemistry. He leads the Collaborative Research Centre CRC 325 on "Assembly Controlled Chemical Photocatalysis," which aims to develop new frontiers in photocatalysis for organic synthesis through designed control of catalyst-substrate interactions. University of Hamburg (PhD, 1989) IBM Research Center (Postdoc) Harvard University (Postdoc) University of Göttingen (Habilitation, 1995) Prof. Reiser's research spans multiple interconnected fields with a strong emphasis on sustainable chemistry. His group extensively utilizes modern techniques for organic synthesis including flow reactors, microwaves, and high-pressure systems. The primary research thrusts include catalysis (both metal and organocatalysts), unnatural amino acids and peptide foldamers, and natural product synthesis. His work on visible light photocatalysis has been particularly influential, with numerous publications in high-impact journals like Angewandte Chemie and Nature Catalysis. The group's research integrates experimental, spectroscopic, and computational techniques to analyze catalyst-substrate interactions for more rational design of photochemical reactions. Analysis of Prof. Reiser's recent publications (2023-2025) reveals a strong focus on copper-based photocatalysis, sustainable chemistry using earth-abundant metals, and innovative approaches to heterocycle synthesis. His work demonstrates a clear trend toward developing more efficient and environmentally friendly catalytic processes, with particular emphasis on visible light activation, catalyst immobilization for recyclability, and applications in medicinal chemistry. The research spans from fundamental mechanistic studies to practical applications in synthesis. German Academic Scholarship Foundation Minerva Foundation NATO Fellowship German Research Foundation Support Karl Winnacker Foundation Prof. Reiser has supervised numerous doctoral students, with recent PhD theses focusing on copper photoredox catalysis, magnetic nanoparticle-supported catalysts, and the synthesis of bioactive compounds. His research is supported by multiple collaborative projects, including the Collaborative Research Centre CRC 325, and involves extensive national and international collaborations with institutions such as the University of Kansas, the National Institute of Chemistry in Pune, the Institut Chimie de Coordination du CNRS in Toulouse, and the University of Zaragoza. The group maintains strong ties with pharmaceutical research through collaborations with Prof. A. Beck-Sickinger in Leipzig on neuropeptide ligands. The research group operates well-equipped laboratories with capabilities for advanced organic synthesis and characterization. They have developed specialized expertise in flow chemistry, high-pressure techniques, and magnetic nanoparticle-based catalyst systems. The CRC 325 initiative has provided significant infrastructure for collaborative research in photocatalysis, bringing together multiple research groups with complementary expertise in organic synthesis, spectroscopy, and computational chemistry.
Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington, where he conducts research at the intersection of causal inference, statistical methodology, machine learning, and artificial intelligence. He is also a data science fellow at the eScience Institute and affiliate faculty of the Center for Statistics and the Social Sciences, demonstrating his interdisciplinary approach to causal methodology. Dr. Cinelli received his Ph.D. in Statistics from the University of California, Los Angeles, advised by Chad Hazlett and Judea Pearl, two prominent figures in causal inference. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences, with particular attention to challenges faced by social and health scientists. His work spans theoretical developments in causal identification, sensitivity analysis frameworks, and practical software implementations that enable researchers to assess the robustness of their causal conclusions. Cinelli's research program addresses fundamental questions about how unobserved confounding affects causal estimates and develops tools to quantify how sensitive findings are to potential violations of causal assumptions. His work on omitted variable bias frameworks has been particularly influential across multiple disciplines. Through his publications, Cinelli has established himself as a leading researcher in causal inference methodology, with papers appearing in top journals across statistics, machine learning, epidemiology, and social sciences. His work demonstrates both theoretical rigor and practical relevance, often accompanied by open-source software implementations that make his methods accessible to applied researchers. Best paper award at SBE 2024 in Econometrics Royalty Research Fund (RRF) Award recipient NSF/MMS research support As an advisor, Cinelli has successfully guided PhD students like Nick Irons to dissertation completion. He actively seeks new students with strong interests in causal inference. His research is supported by multiple funding sources including the National Science Foundation and the University of Washington's Royalty Research Fund. Cinelli contributes to the academic community through editorial work for the Journal of Causal Inference and by developing widely used software packages like sensemakr for sensitivity analysis.
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.
Leo Schwinn is a Lecturer at the Technical University of Munich (TUM) within the Department of Computer Science (I26), working in the Data Analytics and Machine Learning group supervised by Prof. Stephan Günnemann at the TUM School of Computation, Information and Technology. His research focuses on robust machine learning with particular emphasis on data-efficient learning and robustness vulnerabilities of Large Language Models (LLMs). Dr. Schwinn's research interests span multiple critical areas in contemporary machine learning including: Robustness against adversarial attacks in LLMs Embedding space vulnerabilities and defenses Model unlearning and privacy preservation Efficient training methodologies for large models Time-series forecasting with probabilistic frameworks Graph-based machine learning approaches His work bridges theoretical understanding with practical security implications of modern AI systems. Analysis of his recent publications (2023-2025) reveals a strong focus on LLM security, with multiple papers accepted at premier conferences including ICML, CVPR, ICLR, and NeurIPS. His research demonstrates consistent innovation in identifying novel attack vectors while developing practical defense mechanisms, particularly through embedding space manipulation techniques. The work shows increasing sophistication in handling both theoretical aspects of model robustness and practical deployment concerns. His notable scientific achievements include: Receiving the ATE dissertation price for his PhD work at FAU Securing an oral presentation at ICLR 2025 Organizing the ICLR BlogPost Track Becoming a member of ELLIS (European Laboratory for Learning and Intelligent Systems) Dr. Schwinn has served as review process chair for the 2024 Conference on Lifelong Learning Agents (CoLLAs) and actively collaborates with researchers at Mila Quebec AI Institute. His research group at TUM focuses on addressing fundamental challenges in machine learning robustness, particularly as they apply to real-world deployment scenarios where security and reliability are paramount. He maintains active GitHub repositories related to LLM security research, including circuit-breakers-eval and LLM_Embedding_Attack, demonstrating his commitment to open science and reproducible research in the field of AI security.
Krishna Gummadi is a Scientific Director and Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Germany, where he leads the Networked Systems Research Group. He also holds a professorship at the University of Saarland, demonstrating his dual commitment to research and academic instruction in computer science. His educational background includes: Ph.D. in Computer Science and Engineering from the University of Washington (2005) B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Madras (2000) Gummadi's research spans networked and distributed computer systems with a current focus on social computing systems. His work addresses critical challenges in algorithmic fairness, privacy in social media, trustworthiness of online identities, and information dissemination in social networks. He approaches these problems through interdisciplinary methods combining user-centric studies, data-centric analysis, and systems-centric design to create practical solutions that enhance fairness, transparency, and user control in online platforms. His methodology integrates large-scale observational studies, computational modeling, and system implementation to tackle complex human-computer interaction challenges at societal scale. His recent publications reveal a strong emphasis on fairness in algorithmic decision making, with significant contributions to quantifying and addressing discrimination in machine learning systems. His work bridges computer science, social science, and ethics, creating frameworks for fair classification, understanding media bias, and developing privacy-preserving techniques that maintain functionality while protecting user data. The research demonstrates a progression from technical system design to addressing societal implications of computing systems. Among his notable scientific achievements: ERC Advanced Grant in 2017 for 'Foundations for Fair Social Computing' Test of Time Awards at ACM SIGCOMM and AAAI ICWSM Casper Bowden Privacy Enhancing Technologies (PET) and CNIL-INRIA Privacy Runners-Up Awards IW3C2 WWW Best Paper Honorable Mention Multiple Best Paper awards across prestigious conferences Gummadi has advised numerous PhD students and postdoctoral researchers who have gone on to prominent positions in academia and industry. His ERC Advanced Grant has supported extensive research into fair social computing, while his leadership in major conferences (including serving as General Chair for ICWSM 2016 and Program Chair for WWW 2015) has shaped research directions in the field. His teaching portfolio includes courses on Distributed Systems, Human-Centered Machine Learning, and Social Media Analysis. He leads the Networked Systems Research Group at MPI-SWS, which has developed several publicly available systems including tools for fair classification, privacy risk assessment, trust evaluation in social media, and information diet management. The group's work bridges theoretical advances with practical implementations that address real-world challenges in social computing, with numerous software releases and datasets made available to the research community.
Professor Maria Löblich holds a position in the Department of Communication History and Media Cultures at the Institute of Journalism and Communication Studies, Faculty of Political and Social Sciences at Freie Universität Berlin. She specializes in historical and comparative approaches to communication studies with particular expertise in German division and reunification contexts, media policy history, and qualitative research methodologies. Her research focuses on communication history, media biographies, and the relationship between East and West German media landscapes. She has developed innovative approaches to studying media use across life courses and historical periods, with particular attention to how media shapes and reflects collective identities during periods of political transformation. Her methodological expertise lies in qualitative approaches including biographical interviews, discourse analysis, and historical media research. Her recent publications demonstrate a strong focus on historical media analysis, particularly examining the Berliner Zeitung as an anchor of East German identity, media usage patterns in divided Berlin, and the development of journalism studies after the student movement. She has also contributed significantly to theoretical discussions on mediatization and media policy frameworks. Fellow at the Berkman Center for Internet & Society, Harvard University (2012/13) Speaker of the Communication History Group of the German Society for Publizistik- and Communication Science (2012-2016) Professor Löblich has supervised numerous research projects examining media history in divided Germany and currently leads investigations into the media legacy of the GDR. Her methodological contributions to qualitative communication research have been widely recognized, including her co-authored book 'Qualitative Forschung in der Kommunikationswissenschaft.'
Dr. Philipp Porada is a Junior Professor of Ecological Modeling at the University of Hamburg, affiliated with the Department of Biology within the Faculty of Mathematics, Computer Science and Natural Sciences. He works at the Institute of Plant Sciences and Microbiology, specifically in the Applied Plant Ecology group, based at the Otto Warburg House. His research integrates process-based modeling with ecological field studies to investigate non-vascular vegetation, biogeochemical cycles, and climate-vegetation interactions across multiple temporal and spatial scales. Porada's research focuses primarily on non-vascular vegetation (bryophytes, lichens, and biocrusts), examining their role in global biogeochemical cycles, biodiversity-ecosystem functioning relationships, and paleoclimate dynamics. His work spans from contemporary ecosystem processes to geological time scales, with particular emphasis on the impacts of climate change on non-vascular communities. He has developed several process-based models including LiBry for lichen and bryophyte communities, LiDELS for soil-vegetation interactions, and LYCOm for early vascular plants. His research demonstrates how non-vascular vegetation influences carbon sequestration, water cycling, and soil processes across diverse ecosystems from urban forests to polar regions. Analysis of Porada's publication record reveals a strong interdisciplinary approach combining ecological theory, biogeochemistry, and computational modeling. His work spans multiple ecosystems including peatlands, drylands, urban forests, and coastal blue carbon systems. A consistent theme across his research is understanding how non-vascular vegetation mediates the relationship between environmental conditions and ecosystem functions. His most recent work increasingly focuses on climate change impacts and potential mitigation strategies through vegetation management. Porada leads two major research projects funded by the German Research Foundation (DFG): 'Effects of nutrient limitation on non-vascular vegetation under climate change' and 'The role of early plants for palaeoclimate dynamics'. These projects reflect his dual interest in contemporary environmental challenges and deep-time ecological processes. His collaborative work, evident in his extensive publication record with international researchers, demonstrates strong interdisciplinary connections across ecology, biogeochemistry, and climate science. Dr. Porada maintains an active research laboratory focused on ecological modeling, with particular expertise in non-vascular vegetation dynamics. His team develops and applies process-based models to address questions ranging from micro-scale lichen water relations to global biogeochemical cycles. The research group collaborates extensively with field ecologists, climate scientists, and biogeochemists to ground-truth model predictions and explore new ecological phenomena.
Prof. Oliver Seitz leads the Bioorganic Synthesis research group at the Department of Chemistry, Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin. His lab focuses on cutting-edge chemical biology approaches for protein/nucleic acid interrogation, with recent work advancing DNA/RNA-programmed assemblies for cellular imaging and therapeutic applications. Research spans chemical protein synthesis, glycoprotein/phosphoprotein engineering, and nucleic acid-templated reactions. Key innovations include Forced Intercalation (FIT) probes for wash-free RNA imaging, loss-of-affinity principles for catalytic efficiency, and peptide-PNA conjugates for targeted cellular delivery. The group actively develops tools for live-cell protein labeling and biomolecular spatial screening. Recent publications (2021-2024) emphasize fluorescence-based detection systems, catalytic templated reactions, and therapeutic peptide synthesis. Trends show increasing sophistication in multi-dye probes, glycan engineering, and RNA-triggered pro-drug activation. Scientific awards include: Max Bergmann Award (2019) Prof. Seitz actively advises doctoral students, with recent graduates Marvin Björn Stutz (2023, magna cum laude ), Dino Gluhacevic von Krüchten (2023, summa cum laude ), and Sophie Schöllkopf (2023, magna cum laude ). Current PhD candidates include Ekaterina Kazakova (glycoprotein synthesis), Alina Herfort (phosphoproteins), and Lina-Marie Beck (peptide-nucleic acid conjugates), with postdocs like Dr. Mandana Oloub (viscosity sensors). The Bioorganic Synthesis lab operates within Berlin's vibrant chemical research ecosystem, utilizing specialized techniques for chemical protein synthesis and nucleic acid detection. Recent team growth reflects ongoing projects in RNA imaging, catalytic templated reactions, and therapeutic conjugate development, supported by open positions for new researchers.
Dr. Maryegli Fuss is a Scientific Associate at the Institute for Technology Assessment and Systems Analysis (ITAS), Karlsruhe Institute of Technology (KIT) in Germany. She heads the 'Energy-X Nexus' working group within the 'Sociotechnical Energy Futures' research group and serves as academic mentor for the doctoral program Climate, Resources and Circular Economy (KLIREC) . She also contributes to ITAS's 'New Work' initiative and provides scientific advice for German-Indian university collaborations (TU9 network). Education: PhD in Philosophy (Sustainability), KIT (2022) MSc in Material Flow Management, Trier University of Applied Sciences (2012) BSc in Agro-industrial Chemistry, Federal Institution of Goiás, Brazil (2010) Research Focus: Her transdisciplinary work integrates sustainability science, systems analysis, and industrial ecology. Primary domains include: (1) Circular economy implementation in waste management systems, (2) Energy-resource nexus (Water-Waste-Energy-Food), (3) Supply chain risks for critical materials in renewable energy, and (4) Life cycle assessment of low-carbon transitions. Methodologies feature material flow analysis, stakeholder interviews, and socio-technical system modeling. Publications: Her research output emphasizes sustainable waste management in Global South contexts (especially Brazil), critical material supply chains for energy transitions, and environmental assessment frameworks. Recurring themes include circular economy implementation challenges, transdisciplinary approaches, and policy design for resource security. Leadership: Coordinates international research partnerships (e.g., Chile/Indonesia water-energy nexus projects) and leads doctoral mentoring initiatives focused on climate-resource systems.
Jonas Kuhn is a professor at the Institute for Natural Language Processing (IMS) at University of Stuttgart. He is working at the interface between language and computers, combining linguistics and computer science. Kuhn's research interests span a wide range of computational linguistics topics including: Language models and spatial reasoning Analysis of large language models (LLMs) through linguistic theories Political text analysis and discourse networks Computational approaches to literature and cultural studies Retrieval-augmented language modeling Semantic change detection Dependency parsing and syntactic analysis His recent publications (2023-2025) focus on the intersection of neural language processing with fields as diverse as spatial reasoning, literary analysis, and political discourse. This reflects his interdisciplinary approach that bridges fundamental language research with practical technology development. As a faculty member at one of Germany's largest computational linguistics centers, Kuhn contributes to both fundamental research and technological development in language processing systems.
Miriam Stock is Professor of Cultural Studies at the Institute of Humanities at Pädagogische Hochschule Schwäbisch Gmünd, where she also serves as Director of the Cultural Studies Department and Spokesperson for the Master's program 'Interkultur und Integration.' She is a founding member of the Center for Migration and Integration Studies 'Migration – Society – School.' Dr. Stock holds a PhD in Geography from Europa-Universität Viadrina Frankfurt (Oder) and completed her diploma in Geography at Ludwig-Maximilians-Universität München. Her academic journey includes research fellowships, organizational roles in international projects, and advisory work with refugee communities. Her research focuses on migration geographies, cultural theories, postcolonial approaches, urban development from post-migrant perspectives, and qualitative research methodologies. She examines migration and flight between the Arab region and Europe, urban development and consumption, rural perspectives on migration society, anti-discrimination in schools, critical masculinity and family studies, and theoretical-practical approaches. Dr. Stock's recent publications reveal a strong emphasis on transnational migration experiences, particularly Syrian refugee families, with attention to emotional dimensions, gender dynamics, and educational contexts. Her work frequently bridges theoretical frameworks with practical applications through projects like Erasmus Mundus Joint Master Program 'Education, Migration, and Diversity' (2024-2030). Success Story 2020 award for the Enable project As an advisor, Dr. Stock has supervised PhD candidates like Sara Mazzei (University of Calabria/PH Schwäbisch Gmünd) working on Arab-Islamic education systems in migrant experiences. Her grant portfolio includes significant Erasmus+ projects such as Enable (Self-learning for Arab refugee children) and PARENTable (Communicating with parents of newly migrated children), alongside research funded by the Werner-Zeller Foundation. Dr. Stock leads the Institute for Humanities and directs Cultural Studies department activities, including organizing lecture series on topics like 'Right Established - How the New Right Changes Education and Society,' 'Migration and Body,' and 'Emotions in Migration Society.'
Dr. Jonathan Gair is a Group Leader in the Astrophysical and Cosmological Relativity Division at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Potsdam, Germany. Previously, he served as Professor of Astrostatistics at the University of Edinburgh (2018-2019) and as Reader (Associate Professor) in Statistics at the same institution (2015-2018). Dr. Gair's research focuses on gravitational wave data analysis and its applications to cosmology and fundamental physics. His work spans multiple areas of gravitational wave astronomy, with particular emphasis on: Developing and applying new methodologies for gravitational wave data analysis Using gravitational wave observations to derive cosmological parameters, particularly the Hubble constant Developing data analysis tools for the LISA space-based gravitational wave detector Exploring the scientific potential of gravitational wave observations for testing general relativity Creating computationally efficient techniques for parameter inference in gravitational wave astronomy Dr. Gair plays a leading role within the LIGO/Virgo collaboration in deriving cosmological constraints from gravitational wave observations. He currently chairs the LISA Science Group, overseeing the development of data analysis tools for the planned ESA-led LISA mission. His research has significantly contributed to our understanding of how gravitational wave observations can serve as "standard sirens" for measuring cosmic distances and probing the expansion history of the universe. Dr. Gair's work involves both theoretical development and practical application of data analysis techniques. He has developed methods for handling selection effects in rate estimation of gravitational wave events, techniques for mapping gravitational wave backgrounds using methods adapted from cosmic microwave background analysis, and approaches for incorporating model uncertainties into gravitational wave parameter estimation.
