Dominik Hujo is a researcher at the Chair of Automation and Information Systems at the Technical University of Munich (TUM). He holds a Master of Science degree and contributes to advanced research in industrial automation and AI integration. His research focuses on industrial cyber-physical systems , digital twins , human-machine interaction , and AI deployment in manufacturing environments . His work emphasizes real-time requirements, embedded systems, and data management for production processes. Dominik's publications from 2023-2025 demonstrate expertise in multi-agent coordination , predictive maintenance , edge-cloud architectures , and SysML modeling for mechatronic constraints . Key application areas include construction machinery, gear assembly, and logistics systems. He actively collaborates with researchers like Prof. Birgit Vogel-Heuser and Marius Krüger on projects such as KI.Fabrik (AI Factory) and OpAI4DNCS (Operator-AI Interaction for Distributed Control Systems).
Luis Gambarte is a Researcher at the Mathematical Logic Group within the Faculty of Mathematics, Computer Science and Physics at Ludwig Maximilian University of Munich. His work focuses on categorical constructions and computability models in theoretical computer science. Email: gambarte@math.lmu.de Office: Room B217, Tower B, 2nd Floor Recent research trends include: Developing the Grothendieck Computability Model (ICTCS'24) Advancing categorical logic frameworks Contributing to foundational aspects of theoretical computer science
Pia Gehlbach is a doctoral student and research associate at the Department of German Philology, Georg-August-University Göttingen, affiliated with the Research Training Group 2070 "Understanding Social Relationships" and the DFG Priority Program 2393 "ViCom." Education: Bachelor's degree in English and General Linguistics Master's degree in English with a linguistic focus Her research investigates the interplay between iconicity in sign languages (particularly German Sign Language) and semantic conceptualization, using descriptive data analysis and experimental methods. The project examines how iconic signs influence semantic features of concepts, spanning language-/culture-specific and cross-linguistic perspectives. Affiliations: Sign Lab Göttingen DFG Priority Program 2393 "ViCom" (since May 2023) Research Training Group 2070 "Understanding Social Relationships" (since October 2022)
Tan Arda Gedik is a doctoral researcher at the Chair of Language and Cognition (Alexander von Humboldt-Professur) at Friedrich Alexander University Erlangen-Nuremberg (FAU), where he investigates the effects of literacy on morphosyntax in native Turkish speakers. He concurrently teaches an introduction to psycholinguistics at Bilkent University and works as a part-time English instructor at Berlitz Ankara, demonstrating his commitment to both research and practical language education. Gedik's research spans three interconnected strands: applying usage-based construction grammar to language teaching applications, investigating literacy-related individual differences in Turkish morphosyntactic knowledge, and converging posthumanism with applied linguistics. His work on Turkish syntax, particularly on evidentiality and the unevidentiality construction, has generated significant scholarly interest. He has developed specialized corpora including the MEBET corpus for analyzing English textbooks in Turkey and the EUEE corpus for examining university entrance exams. His publication record reveals a clear trajectory from traditional linguistic analysis toward more interdisciplinary approaches that integrate cognitive linguistics with posthumanist philosophy. Gedik's work increasingly examines how language reflects and shapes our relationship with the environment and non-human entities, while simultaneously challenging traditional assumptions about native speaker competence and literacy effects. His research on individual differences in constructional knowledge has important implications for language teaching methodology and teacher training programs. Gedik has presented his research at numerous international conferences across Europe and North America, including events at Cornell University, Arizona State University, and the University of Bergen. His collaborative work with scholars like Zeynep Arpaözü and Yağmur Su Kolsal demonstrates his commitment to interdisciplinary research that bridges theoretical linguistics with practical applications in language education.
Professor Björn Hansen is Chair of Slavic Linguistics at the Institute of Slavic Studies, University of Regensburg, where he has held a professorship since 2002. Previously, he served as Lecturer in Slavonic Linguistics at the University of Cambridge (2000-2002) and as a research assistant at the University of Hamburg. He is also Partnerschaftsbeauftragter for university cooperation with the University of Novi Sad (Serbia) and University of Łódź (Poland), and chairs the doctoral committee for the Faculty of Philosophy IV at Regensburg. Born: 1964 in Flensburg 1984-1991: Studies in East Slavic Studies and German Language and Literature at University of Hamburg 1995: PhD in Slavic Linguistics (University of Hamburg) 2001: Habilitation in Slavic Philology - Linguistics (University of Hamburg) Hansen's research spans multiple areas of Slavic linguistics, with particular focus on the structures of Polish, Russian, Serbian and Croatian languages. His work investigates language contact phenomena, areal typology, language change in Slavia, heritage linguistics, and corpus linguistics. He specializes in syntax and semantics, particularly examining modality, grammaticalization processes, contact-induced change, clitics (including clitic climbing), indefiniteness, subject encoding, frame semantics of public discourse, and the language of religion. His methodological approach often combines corpus-based analysis with theoretical linguistic frameworks. Analysis of Hansen's recent publications reveals consistent focus on microvariation in South Slavic languages, particularly Bosnian, Croatian, and Serbian. His work demonstrates expertise in clitic phenomena, modal constructions, and subject case marking. He frequently employs corpus-based methodologies to investigate rare linguistic phenomena, especially in underresourced languages. His research increasingly connects historical linguistics with contemporary language use, examining how grammaticalization processes evolve over time and across language contact situations. Member of DFG-Fachkollegium 104-02 (2009-2024) Official expert for Alexander von Humboldt Foundation (since 2011) Reviewer for multiple national research foundations including DFG, Swiss National Science Foundation, and Austrian Science Fund Member of editorial boards for several international linguistics journals Hansen has supervised numerous doctoral and master's students, with research spanning heritage language syntax, Slavic modality, language contact phenomena, and corpus-based linguistic analysis. He leads multiple externally funded research projects including 'seeFfield' (VolkswagenStiftung, 2022-2029), 'LangGener' (DFG-NCN, 2018-2022), and several DFG projects examining microvariation in Bosnian/Croatian/Serbian, Polish-German bilingualism, and historical semantics of corruption. As Principal Investigator of the Graduate School for East and Southeast European Studies (2012-2019), he has significantly contributed to interdisciplinary research in the region. Hansen directs the Binational Study Program 'German-Polish Studies' (funded by DAAD) and has organized numerous international conferences and workshops on Slavic linguistics, including the 'Clitic Climbing' workshop (2017) and 'Perspectives of German-Slavic Multilingualism' conference (2016).
Johann-Mattis List is a Full Professor leading the Chair of Multilingual Computational Linguistics at the University of Passau, and a Senior Scientist at the Max Planck Institute for Evolutionary Anthropology (2021-2024). His research bridges bioinformatics and linguistics through quantitative approaches to language evolution and historical comparison. His research program develops computational methods for: Phylogenetic reconstruction of language families Cross-linguistic semantic analysis (colexification patterns) Automated detection of lexical borrowing Sound correspondence modeling Database development for linguistic typology Recent publications (2019-2025) demonstrate strong focus on: Large-scale lexical databases (Lexibank, CLICS) Automated phonological reconstruction Cognate detection algorithms Semantic change quantification South American and Sino-Tibetan language histories Methodologically, they combine phylogenetic modeling, information theory, and machine learning with traditional historical linguistics. He leads the CALC/MCL laboratory developing open-source tools like EDICTOR and CLDFBench. Current projects include computational analysis of numeral systems, sign language evolution, and refinement of reflex prediction models.
Lennard Gäher is a fourth-year PhD student in the Foundations of Programming group at the Max Planck Institute for Software Systems (MPI-SWS), advised by Derek Dreyer. He holds a Bachelor's degree in Computer Science from Saarland University (2020). His research focuses on program verification, separation logics, and concurrency, with contributions to frameworks like RefinedRust and Simuliris. He has been recognized with the 'Busy Beaver' teaching award for his work as a course designer and lecturer in theoretical computer science at Saarland University. Teaching highlights include co-designing the second half of Saarland's Semantics course (WS 21/22), where he mechanized course material in Coq (thousands of lines of code), and leading a Mathematics Preparatory Course for 150+ students (WS 20/21). His academic roles include multiple teaching assistant positions across core computer science courses since 2017. His publications span PLDI, POPL, and ICFP, addressing challenges in Rust verification, separation logic frameworks, and concurrency theory. Key projects include RefinedProsa (response-time analysis integration) and Quiver (abductive inference tools in Coq).
YoungGyoun Moon is a Researcher at the Max Planck Institute for Software Systems (MPI-SWS), specializing in core computer science domains such as Algorithms, Theory & Logic, Programming Languages, and Security & Privacy. Their work bridges theoretical foundations with practical applications in Cyber-Physical Systems and Distributed Systems. Research Interests: Algorithm design and formal logic verification Programming language semantics and security Resilient Cyber-Physical Systems Networked system architectures Privacy-preserving distributed algorithms
Dr. Camil Staps is a Researcher in the research area 'Semantics & Pragmatics' at Leibniz-Zentrum Allgemeine Sprachwissenschaft (ZAS Berlin). His research focuses on how abstract concepts like causation and evidentiality are represented in the mind, particularly studying the relation between spatial and non-spatial meanings of prepositions, demonstratives, and other function words. His educational background includes a PhD in Linguistics from Leiden University (2024), a Research MA in Hebrew and Aramaic Studies from Leiden University, and an MSc in Software Science from Radboud University Nijmegen. His research interests span computational linguistics, cognitive science, and formal semantics. Dr. Staps has received significant research funding including an NWO Rubicon Grant (2024-2026) and NWO PhDs in the Humanities Grant (2019-2024). His publications demonstrate a strong focus on formal semantics, Biblical Hebrew linguistics, and computational approaches to language analysis. He maintains several software projects including Məḇaqqēš (for Biblical Hebrew scholars), HebrewTools (educational tools), and Nitrile (a package manager for Clean programming language).
Katrina Falkner is a Professor in the School of Computer Science at the University of Adelaide, Australia, with an extensive research portfolio spanning computer science education, learning analytics, and natural language processing applications. Her work bridges theoretical computer science with practical educational implementations, with particular expertise in Massive Open Online Courses (MOOCs) and innovative teaching methodologies for K-12 computer science education. Dr. Falkner's research interests include analyzing learner confusion through linguistic patterns, developing mental health support systems using conversational agents, detecting cyberbullying through natural language processing, and applying robotics to wildlife conservation. Her interdisciplinary approach has yielded significant contributions across education, healthcare, and environmental domains. Analysis of her recent publications reveals a clear progression in her research focus, moving from foundational work in computer science education to more complex applications of artificial intelligence in mental health support and conservation technology. Her work on the EmoMent corpus represents important cross-cultural research in mental health, while her ConservationBots project demonstrates practical applications of computer vision in environmental monitoring. Principal Investigator on multiple Australian Research Council grants Key contributor to the Australian Digital Technologies Curriculum development Active participant in international computer science education research collaborations Dr. Falkner has mentored numerous graduate students who have become established researchers in their own right, with several appearing consistently as co-authors on her publications. Her collaborative approach extends to interdisciplinary partnerships across computer science, education, psychology, and environmental science fields.
Dr. Gábor Kismihók is a senior researcher at the Corvinus University of Budapest specializing in learning analytics , ontology engineering , and AI-driven educational systems . His work bridges academic research with practical applications in career development , educational technology , and researcher mental health . Leading Learning and Skill Analytics research group Developing ontology-based educational systems since 2005 Pioneering AI-driven career recommendation ontologies His research focuses on: Personalized learning through knowledge graphs and semantic technologies Academic mental health advocacy and survey design Labor market intelligence integration with educational systems Mobile learning frameworks for vocational education Text mining applications in organizational research Competency validation between education and workplace Recent publications demonstrate expertise in: Large language model integration for adaptive learning Geriatric care technology redefining nursing competencies Skills gap analysis in AI and big data Digital educational federation systems like DALIA FAIR Academic well-being measurement frameworks He has contributed to: Multiple IEEE/ACM conference proceedings (2016-2023) European training networks like INSPIRE and ReMO COST Action Policy development through researcher well-being manifestos
Adam Tauman Kalai is a Professor of Computer Science at the University of Chicago, where he conducts cutting-edge research at the intersection of machine learning, artificial intelligence, and human-computer interaction. His work spans theoretical foundations and practical applications, with a particular focus on the societal impacts of AI systems. Dr. Kalai's research interests encompass a broad spectrum of topics in artificial intelligence, with a strong emphasis on machine learning theory, algorithmic fairness, natural language processing, and human-AI interaction. His work addresses fundamental questions about how AI systems can be made more reliable, fair, and understandable. He has made significant contributions to understanding the limitations of language models, particularly around issues of calibration and hallucination, as evidenced by his influential paper "Calibrated Language Models Must Hallucinate" which explores the inherent tension between model calibration and factual accuracy. Analysis of his recent publications reveals a strong focus on addressing critical challenges in AI development, including fairness in chatbots, multicalibration of neural networks, language model self-improvement, and the societal impacts of algorithmic decision-making. His work increasingly bridges theoretical computer science with practical applications in social contexts, demonstrating a commitment to developing AI systems that are not only technically sound but also socially responsible. Dr. Kalai has established himself as a leading researcher through his extensive collaborations with prominent scholars across academia and industry. His work appears consistently in top-tier venues including NeurIPS, ICML, STOC, and ICLR, reflecting the high quality and impact of his contributions to the field. His research program demonstrates a clear trajectory toward addressing some of the most pressing challenges in contemporary AI development, with particular attention to the ethical and societal implications of increasingly capable AI systems. This focus on responsible AI development positions him at the forefront of efforts to ensure that AI technologies benefit society broadly.
Walter S. Lasecki is an Associate Professor at the University of Michigan's School of Information, where he leads research at the intersection of Human-Computer Interaction, Crowdsourcing, and Artificial Intelligence. His work focuses on creating systems that integrate human and machine intelligence to solve complex problems in real-time. Dr. Lasecki's research interests center on human-AI collaboration, particularly in developing crowd-powered systems that enhance accessibility, improve programming education, and create more effective human-computer interfaces. His work explores how to effectively integrate human intelligence with AI systems, focusing on real-time applications where speed and accuracy are critical. His research has significant implications for accessibility technologies, educational tools, and conversational AI systems. He has pioneered approaches to real-time captioning, crowd-powered interfaces, and human-in-the-loop machine learning systems that adapt to user needs. Analysis of his recent publications reveals a strong focus on multi-agent conversational AI, human-in-the-loop systems for pose estimation and object recognition, and innovative approaches to programming education through live streaming. His research consistently explores the intersection of human computation and artificial intelligence, with particular attention to how crowd workers can complement and enhance AI capabilities. His work demonstrates a trajectory from foundational crowd-powered systems to more sophisticated integrations of human and machine intelligence in complex tasks. Dr. Lasecki has collaborated extensively with researchers across multiple institutions, particularly with Jeffrey P. Bigham (earlier in his career) and more recently with colleagues at the University of Michigan including Juho Kim. His research has been supported by substantial grants that have enabled the development of systems like Scribe for real-time captioning and Codeon for on-demand programming assistance. He has mentored numerous graduate students who have gone on to contribute to the fields of HCI and AI. His laboratory focuses on developing practical applications of crowd-AI hybrid systems, with particular emphasis on creating tools that can be deployed in real-world settings. Current projects explore how to make conversational AI more robust through multi-agent approaches, improve programming education at scale, and create more accessible interfaces for diverse user populations.
Shriram Krishnamurthi is a Professor in the Computer Science Department at Brown University, Providence, RI. With a prolific research career spanning over three decades (from 1994 to present), he has made significant contributions across programming languages, formal methods, and computer science education. His work bridges theoretical foundations with practical educational applications, particularly in making complex concepts accessible to students. Dr. Krishnamurthi's research interests encompass programming languages, formal methods, type systems, and computer science education. His work often focuses on the intersection of these areas, particularly how to make formal methods and advanced programming concepts accessible to students through innovative language design and educational tools. He has developed several educational frameworks that have been adopted in both university and K-12 settings, demonstrating his commitment to improving computer science education at all levels. His recent publications reveal a strong focus on making formal methods more approachable through grounded language design, addressing student misconceptions in programming through innovative assessment techniques, and developing practical tools like Forge for teaching formal methods. His work on Rust's type system, privacy-aware static analysis, and document calculus demonstrates the breadth of his research interests while maintaining a consistent thread of improving programming language understanding and usability. As a dedicated educator and researcher, Krishnamurthi has mentored numerous PhD students who have become prominent researchers in their own right, including Ben Greenman, Tim Nelson, Kuang-Chen Lu, and Will Crichton. His collaborative approach is evident in his extensive publication record featuring collaborations with both established researchers and emerging scholars.
Aaron Turon is a researcher at the Max Planck Institute for Software Systems (MPI-SWS), where he focuses on foundational aspects of programming languages, concurrency, and formal verification. His work bridges theoretical insights with practical applications, particularly in systems such as Rust and frameworks for reasoning about weak memory models. His research interests include concurrent programming, type systems, formal methods, and scalable distributed systems. He has contributed to seminal projects like the Iris framework for concurrent reasoning and the development of LVars for quasi-deterministic parallelism. Turon's publications emphasize practical formal verification techniques, such as separation logic and logical relations, to ensure correctness in complex systems. His work on Rust highlights the translation of theoretical concepts into industrial-strength tools. He collaborates with academic and industrial partners to advance programming language design, concurrency control, and software engineering practices.
