Simone Paolo Ponzetto is an Assistant Professor (Juniorprofessor) at the University of Mannheim since 2013, affiliated with the Research Group Data and Web Science. His research focuses on Semantic Web technologies, Natural Language Processing (NLP), and knowledge acquisition, particularly leveraging collaboratively built resources like Wikipedia. Prior to Mannheim, he held postdoctoral roles at Sapienza University of Rome and research positions at the University of Heidelberg and Stuttgart. His work includes pioneering projects like BabelNet, a multilingual semantic network. Ponzetto earned his PhD in Computational Linguistics from the University of Stuttgart, with interdisciplinary contributions to coreference resolution, semantic relatedness, and ontology learning. Research Interests: Unsupervised/weakly-supervised knowledge extraction Multilingual ontology learning and semantic networks Lexical semantics (word sense disambiguation, semantic similarity) Discourse semantics (coreference resolution, coherence modeling) Professional Contributions: Guest editor for a Artificial Intelligence Journal special issue on AI and Wikipedia Area chair for EMNLP-CoNLL 2012 and EACL 2014 Program committee member for ACL, AAAI, and other top conferences Lab/Team: Active in the Research Group Data and Web Science at Mannheim, advancing AI and NLP applications in collaborative knowledge systems.
Daniela Margareta Chiorean serves as University Professor in the Graphics Department at the University of Art and Design Cluj-Napoca, where she has held academic positions since 2000 and currently serves as Prorector responsible for quality assurance (2023-2024). She previously served as Dean of the Faculty of Plastic Arts (2012-2015) and Department Director for Graphics (2015-2023). Concurrently, she maintains an active legal practice as an attorney specializing in civil law and intellectual property at the Cluj Bar Association since 2007. Her educational background includes a PhD in Visual Arts (2007) from UAD Cluj-Napoca with thesis 'Man - Media. Traditional and new media in graphics,' a Master's in Civil Law (2005-2007), Law degree (2001-2005), and dual Master's degrees in Visual Arts (2001-2003). She completed specialized training in intellectual property at WIPO (2002) and cultural management/multimedia in Germany (2002-2003). Chiorean's research bridges graphic design, digital media, and intellectual property law, examining how traditional media interacts with contemporary digital environments. Her work explores symbolic dynamics in advertising, the evolution of typography from Gutenberg to virtual publications, and the legal frameworks governing creative works in digital spaces. She investigates how new technologies transform artistic practices while maintaining connections to historical techniques, with particular focus on remix culture and copyright issues in visual communication. Her publication trends reveal consistent exploration of media transitions, with recent work analyzing digital image processing, urban-nature representations in artificial environments, and comparative studies of display versus print media. These publications demonstrate her interdisciplinary approach connecting artistic practice with scholarly inquiry into media evolution. Premiul UAP România pentru grafică - tineret (2002) Premiul Vespasian Lungu pentru grafică (1997) As a researcher, she has led multiple nationally-funded projects including 'Vector – Design and Book Illustration' (2012) through the National Cultural Fund and 'Center for Promoting Entrepreneurship in Sustainable Development' (2010-2013). She coordinates international collaborations through Erasmus+ and Leonardo da Vinci programs, organizing workshops on infographics, bookbinding, and digital media. Her professional activities span academic leadership, legal practice in intellectual property, and active participation in national and international artistic communities as both creator and evaluator. She coordinates specialized workshops in infographics and bookbinding techniques, and participates annually in the Brâncuși International Workshops for sculpture, painting, and graphics. Her collaborative network extends across European institutions through multiple international partnerships, with recent involvement in Korea's 'O întâlnire și o altă călătorie începe' exhibition (2024).
Saptarashmi Bandyopadhyay is a Tenure-Track Assistant Professor of Computer Science at the City College of New York and the Graduate Center at the City University of New York (CUNY). Her research focuses on Artificial Intelligence Agents and Autonomous Decision Making, with special emphasis on Multi-Agent Reinforcement Learning, Multi-Agent Imitation Learning, and related paradigms. She has established significant collaborations with leading institutions including Google DeepMind, Carnegie Mellon University, Oxford University, and MIT. Dr. Bandyopadhyay received her PhD from the University of Maryland, College Park, where she was advised by Professor John Dickerson and Professor Tom Goldstein. Prior to that, she graduated from Penn State in 2020 with a thesis on Multimodal Computer Vision in Medical Domain advised by Prof. William Evan Higgins. Her research expertise spans multiple domains of AI including Multi-Agent Systems, Reinforcement Learning, Imitation Learning, and Multimodal Perception. She specializes in developing AI agents for applications in climate conservation, economic systems, and AI safety. Her work integrates techniques from computer vision, natural language processing, and robotics to create more robust and explainable AI systems that can operate effectively in complex, real-world scenarios. Current work includes improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Analysis of Dr. Bandyopadhyay's publication record reveals a clear progression from foundational work in medical imaging and natural language processing toward increasingly sophisticated multi-agent AI systems. Her recent publications demonstrate a strong focus on Multi-Agent Reinforcement Learning frameworks like JAXMARL, with applications spanning from supply chain orchestration to climate conservation. The interdisciplinary nature of her work is evident in publications spanning computer vision, NLP, and multi-agent systems conferences including AAAI, NeurIPS, AAMAS, EMNLP, and ACL. DoGood Fellow (2022) UMD Dean's Summer Fellow (2021) Dr. Bandyopadhyay has been actively involved in securing research funding from major agencies including NSF, NIH, DoD, and ARL. She served as the lead PhD student RA in a DoD project for Multi-Agent Explainable AI to improve AI trustworthiness. Her service to the academic community includes membership on program committees for major conferences including IJCAI 2024, KDD 2024, ACL 2024, and AAMAS 2023-2024. She has also created the AI Agents Seminar Series at UMD in 2022 with over 1,000 participants from six continents. Currently, Dr. Bandyopadhyay leads research on improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Her lab collaborates prominently with researchers from Google DeepMind, Carnegie Mellon University, Oxford University, University of Sheffield, Waymo, Meta AI, and MIT, with special focus on Dr. Jakob Foerster's and Dr. Robert Loftin's groups.
Fajar Juang Ekaputra is a Tenure Track Assistant Professor at the Institute of Data, Process, and Knowledge Management (DPKM), WU Vienna and a part-time Postdoctoral Researcher at the Data Science research unit, TU Wien . With a focus on Semantic Web , Knowledge Graphs , and their integration with Machine Learning in Neurosymbolic AI systems, his work spans domains like Cyber-Physical Systems and Materials Engineering . Education: Dr.techn. (2018), TU Wien M.T. (2010) and S.T. (2008), Institute Teknologi Bandung (ITB) Research Interests center on hybrid AI systems combining Semantic Web and Machine Learning , with applications in Cyber-Physical Systems (e.g., smart grids, smart buildings), data privacy in smart cities, and materials engineering . His 102+ publications include frameworks like SWeMLS-KG and SHACL4Protege . Recent Articles (2024) address explainable AI in cyber-physical systems, privacy trust in data infrastructures, and neurosymbolic frameworks . Earlier works (2023–2022) explore ontology-based data management , auditable AI , and hybrid system architectures . Scientific Awards: Best Paper Awards (ICoDSE 2023, ICoDSE 2016) Best Poster Nomination (SEMANTiCS 2019) PhD Scholarship (Austria’s Agency for Education and Internationalisation, 2012) Advising includes supervising PhD students (e.g., Majlinda Llugiqi, Katrin Schreiberhuber) and master’s theses on topics like knowledge graph characteristics and data quality assessment . He leads projects such as FAIR-AI (FFG-funded, 2024–2026) and SENSE (Horizon Europe, 2023–2025).
Judit Gervain is a Full Professor at the Department of Developmental and Social Psychology at the University of Padua, Italy, and a CNRS Senior Research Scientist (Directeur de Recherche) at the Integartive Neuroscience and Cognition Center (CNRS & Université Paris Descartes), Paris, France. Her research focuses on early speech perception, language acquisition in monolingual and bilingual infants, and the neural mechanisms underlying language learning. She pioneered studies on newborn speech perception using near-infrared spectroscopy (NIRS), revealing prenatal influences on perceptual abilities and the emergence of grammatical structures in preverbal infants. Education: PhD in Cognitive Neuroscience (2002, SISSA, Trieste), postdoctoral research at the University of British Columbia (2007–2009), and CNRS researcher since 2009. She has authored over 100 peer-reviewed articles in journals like Science Advances , Nature Communications , and Developmental Science . Research Interests: Infant language processing, bilingualism, neuroimaging techniques (fNIRS), and comparing infant learning trajectories to artificial intelligence systems. Her work bridges developmental psychology, neuroscience, and computational linguistics, emphasizing the role of innate biases and environmental input in language acquisition. Her recent studies explore how infants’ learning mechanisms differ from Large Language Models (LLMs), focusing on input requirements, critical periods, and the role of multimodal integration. She also investigates the impact of prenatal auditory experience on neonatal speech perception and the neural foundations of linguistic structure detection. Labs/Teams: Affiliated with the CNRS’s Integartive Neuroscience and Cognition Center and the University of Padua’s developmental psychology group. Serves as associate editor for Developmental Science and Neurophotonics .
Sameer Singh is a Professor of Computer Science at the University of California, Irvine's Donald Bren School of Information and Computer Sciences. He also holds affiliations with Linguistics and EECS departments. His research primarily focuses on the robustness and interpretability of machine learning algorithms, along with models that reason with text and structure for natural language processing. Dr. Singh received his PhD from the University of Massachusetts, Amherst in 2014, an MS in Computer Science from Vanderbilt University in 2007, and a BEng in Electrical Engineering from the University of Delhi in 2004. His research interests span machine learning robustness, natural language processing, model interpretability, and knowledge representation. Singh investigates how to make AI systems more reliable and understandable, particularly focusing on testing methodologies for NLP models and developing techniques to improve model behavior. His work bridges theoretical understanding with practical applications in AI safety and reliability. Analysis of Singh's recent publications reveals a strong focus on language model interpretability, bias detection, and model robustness. His work explores how language models process information, where they fail, and how to make them more reliable. A significant portion of his recent research examines the limitations of multimodal models, language model alignment techniques, and addressing social biases in AI systems. Dr. Singh has received numerous prestigious awards including the Kavli Fellowship from the National Academy of Sciences, the NSF CAREER award, UCI Distinguished Early Career Faculty award, and the Hellman Faculty Fellowship. His papers have won multiple awards including at KDD 2016, ACL 2018, EMNLP 2019, AKBC 2020, and ACL 2020. His research group has secured substantial funding from major organizations including the Allen Institute for AI, Amazon, NSF, DARPA, Adobe Research, Hasso Plattner Institute, NEC, Base 11, and FICO. Singh previously served as an Allen Fellow at the Allen Institute for AI (2021-2023) and is currently a co-founder and CTO of Spiffy AI in Seattle. He completed postdoctoral research at the University of Washington after earning his PhD. Dr. Singh maintains an active presence in the AI community through his work on projects like AutoPrompt and Checklist, which have become influential tools for testing and interpreting NLP models. His research continues to shape how the field approaches model evaluation and interpretability.
Arijit Khan is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He leads the Data Engineering, Science and Systems group and is affiliated with the Technical Faculty of IT and Design. His research focuses on Graph Neural Networks , Blockchain , Data Management , and AI interpretability . He is the Principal Investigator (PI) of a major project on Data Management, Fundamental Algorithms, and Machine Learning for Emerging Problems in Large Networks (2022–2027). Research Interests : Graph Data Management & Machine Learning Blockchain Transaction Analysis Large Language Model + Knowledge Graph Synergies Healthcare AI (e.g., ICU glucose prediction) Explainable AI for Graph Neural Networks Research Trends : His publications emphasize neuro-symbolic systems , uncertain graph analysis , and AI-driven blockchain insights . Recent work bridges large language models with knowledge graphs and explores GPU performance optimization via shader code analysis. Awards & Grants : No explicit awards listed, but his active research grants include a 5-year project on large network analysis with interdisciplinary applications in life and health sciences. Funding emphasizes algorithmic innovation and data science integration. Labs/Teams : Head of the Data Engineering, Science and Systems research group, focusing on AI for societal impact ('AI for the People') and scalable graph data systems. Collaborations span blockchain analytics, healthcare informatics, and GPU architecture design.
Syrielle Montariol is a Researcher and Course Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Natural Language Processing Lab (NLP) under the School of Computer and Communication Sciences (IC). She holds a postdoctoral position and teaches courses related to computational linguistics and AI applications. Her research focuses on advancing NLP, medical language models, multimodal learning, and AI ethics. She works in the INR 240 office and maintains collaborations across EPFL's academic divisions. Research Interests: Her work spans interpretability of AI systems, cross-modal reasoning, medical domain adaptation, sustainability text analysis, and the societal impact of AI. Recent projects include developing explainable models (e.g., global mixture-of-experts frameworks) and benchmarking tools like Vinabench for visual narratives. Publications: Her recent work addresses critical challenges in AI, including vulnerability of higher education to LLMs, medical language model adaptation (Meditron), and robust geo-localization systems. Key themes include ethical AI, multimodal learning, and domain-specific NLP applications. Labs & Teams: She contributes to the NLP lab's initiatives on visual-language models and collaborates with interdisciplinary teams on projects like PAN-RSVQA for remote sensing and PICLe for low-resource NER systems.
Anna Breger is a Senior Postdoctoral Researcher at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, and a Research Fellow leading the iDeal project at the Medical University of Vienna. She holds the prestigious Hertha Firnberg Fellowship from the Austrian Science Fund, focusing on image quality assessment and medical imaging applications. Her work bridges theoretical mathematics with practical challenges in healthcare and cultural heritage preservation. Research interests include mathematical image processing for medical diagnostics, data representation, and cultural heritage restoration. She has pioneered AI-driven methods for analyzing historical sheet music and medical imaging data, collaborating with institutions like the Fitzwilliam Museum and Cambridge University Library. Key Projects: iDeal (Medical Image Quality), C2D3-funded Cultural Heritage AI, AIX-COVNET collaboration for X-ray analysis. Awards: Hertha Firnberg Fellowship, City of Vienna Promotion Award, L’OREAL Fellowship. Grants: C2D3 Accelerate, Austrian Science Fund. Publications emphasize advancing IQA metrics for medical images and developing clustering algorithms (visClust). She also contributes to interdisciplinary initiatives like Her Math’s Story and the AI for Cultural Heritage Hub (ArCH).
Mark Plumbley is a Professor of Signal Processing at the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering at the University of Surrey. He holds an EPSRC Fellowship in 'AI for Sound' and has led major research initiatives, including the DCASE challenges. His work focuses on AI-driven analysis of acoustic scenes and events, with contributions to machine learning, audio source separation, and sparse representations. Previously, he was Director of the Centre for Digital Music at Queen Mary University of London and Head of the School of Computer Science at Surrey. Education: PhD in Neural Networks (1991). Academic roles include Professorships at King’s College London (1991–2002) and Queen Mary University of London (2002–2014). Research spans audio event detection, sound scene classification, and generative AI for audio synthesis. He leads projects like the EPSRC-funded 'Making Sense of Sounds' and 'Musical Audio Repurposing using Source Separation', and co-edited the Springer book on Computational Analysis of Sound Scenes and Events. Research Interests: AI for Sound: Machine learning applied to real-world audio analysis. Acoustic Scene and Event Recognition: Developing models for sound classification and localization. Generative Audio Models: Text-to-audio systems and diffusion models for sound synthesis. Healthcare Applications: Audio-based diagnostics and bioacoustic signal processing. Grants and Awards: EPSRC Fellowships, EU-funded networks (SpaRTaN, MacSeNet), and Fellowships from IET and IEEE. Notable awards include the IEEE Young Author Best Paper Award (co-authored with students) and leadership in the DCASE community. Labs and Collaborations: CVSSP at Surrey, collaborations with BBC R&D, and interdisciplinary projects on urban soundscapes and noise pollution (UK Acoustics Network Plus).
Colin Milburn holds the Gary Snyder Chair in Science and the Humanities at UC Davis, with appointments in Cinema and Digital Media, English, and Science and Technology Studies. He directs the UC Davis ModLab. His interdisciplinary scholarship examines relations between literature, science, and technology, with focus areas including science fiction, history of biology and physics, nanotechnology, video games, and digital humanities. Recent publications explore speculative technologies, citizen science games, and quantum technology's societal implications. As an expert on gaming culture, he has advised on game design and cost management for players. His work combines critical theory with media studies to analyze how games and speculative fiction shape scientific imagination.
Robert Berry is a Senior Lecturer at the Faculty of Computing, Engineering and Science at the University of South Wales. He holds a PhD in Geographical Information Systems, an MSc in GIS, and a BSc in Geography. His research focuses on geocomputation, integrating AI, NLP, and GIS to address geospatial challenges, with recent work in accessibility modeling and 3D landscape visualization. He is affiliated with the GIS Research Centre and WISERD, an interdisciplinary social science institute. Dr. Berry teaches modules in computing and information systems, including big data analytics, GIS, and project management. He has supervised numerous MSc and PhD students and contributed to over 50 research outputs, including peer-reviewed articles, conference papers, and technical reports. His expertise spans FOSS tools, programming (R, Python), and cloud computing for geospatial applications. Key projects include the 'People, Places and the Public Sphere' initiative (2024–2027) and evaluations of agricultural risk management policies. He has collaborated with UK government agencies, local authorities, and third-sector organizations on projects involving environmental planning, flood management, and cultural heritage preservation. Dr. Berry is a Fellow of the Royal Geographical Society and actively engages in advancing participatory technologies for environmental decision-making. His work emphasizes enhancing public participation through digital tools and addressing socio-economic challenges via geospatial analysis.
Engin Erzin is a Professor at Koç University's College of Engineering, leading the KUIS AI Lab and Multimedia, Vision and Graphics Lab . His research focuses on AI-driven human-centric systems, affective computing, and multimodal interaction analysis. He has contributed extensively to robotics, speech processing, and human-robot interaction through over 70 peer-reviewed publications since 2008. Research interests include: Affective computing and emotion recognition from speech/gestures Human-robot interaction and socially engaging agents Speech-driven animation and gesture synthesis Multimodal data fusion for interaction analysis Deep learning applications in robotics and biomedical engineering Recent work emphasizes: Developing adaptive pHRI controllers for manufacturing tasks Creating engagement measurement frameworks for human-machine interfaces Advancing Turkish speech recognition through self-supervised learning Designing multimodal databases for interaction studies Labs: KUIS AI Lab : Focuses on AI applications in robotics and human-computer interaction Multimedia Lab : Specializes in vision, graphics, and audiovisual analysis
Slim Essid is a Full Professor at Télécom Paris, leading the Audio Data Analysis and Signal Processing (ADASP) group. He holds a Doctorat (Ph.D.) and Habilitation from Université Pierre et Marie Curie (UPMC). With 15+ years of research experience, he has advised 15 PhD graduates and currently co-advises 10 others. His work focuses on machine learning, signal processing, and multimodal systems, publishing over 150 peer-reviewed papers. He serves as a reviewer for top journals/conferences (e.g., IEEE Transactions) and research funding agencies. Education: State Engineering Degree, École Nationale d’Ingénieurs de Tunis (2001) M.Sc. (D.E.A.) in Digital Communication Systems, École Nationale Supérieure des Télécommunications, Paris (2002) Ph.D., Université Pierre et Marie Curie (2005) Habilitation (HDR), UPMC (2015) Research Interests: Multimodal learning, self-supervised representations, audio-visual segmentation, music structure analysis, domain generalization, and speech enhancement. Recent publications highlight innovations like TACO (training-free sound-prompted segmentation) and CLOUDS (domain-generalized semantic segmentation framework using foundation models). His work bridges audio processing with vision and language models, emphasizing unsupervised/zero-shot approaches. Key achievements include state-of-the-art methods in sound event detection, speaker diarization, and music segmentation. He collaborates with 14 post-docs and leads projects funded by French/EU agencies.
Timo Fleischer is an Associate Professor in the Chemistry Didactics Working Group at the University of Salzburg, affiliated with both the Faculty of Natural and Life Sciences and the School of Education. He leads key educational innovation projects including EXBOX-Digital, ChemGerLab-VR, and RECC Salzburg, and plays a significant role in digital science education development. Education: Bachelor of Science in Geography and Chemistry, University of Kiel (2011) Master of Education in Geography and Chemistry, University of Kiel (2013) PhD in Science Education, TUM School of Education (2017) Habilitation in Chemistry Didactics, University of Salzburg (2024) His research focuses on the effectiveness of digital media in teacher education and chemistry classrooms, the development of digital teaching and learning materials, and the integration of experimentation and modeling in science education. He is particularly interested in how digital tools support the learning of chemical language and representations. His work bridges educational theory and practical classroom innovation. His recent publications demonstrate a strong emphasis on digital scaffolding, augmented and virtual reality in chemistry labs, eye-tracking during experiments, and the use of fiction as an anchor in science teaching. These works reflect a trend toward immersive, technology-enhanced, and student-centered learning environments in science education. Scientific Awards: Ehrenurkunde Polytechnik-Preis 2022 – Projekt EXBOX-Digital Kulturfondspreis 2020 für das Projekt MINT:labs Science City Itzling Comenius-EduMedia-Siegel für EXBOX-Digital (2020) Qualitätslabel „Regional Educational Competence Centre“ (RECC) (2018–2021) Junior-Fellowship, Kolleg Didaktik:digital (2016–2017) Timo Fleischer has supervised various research projects and collaborated with numerous colleagues and students, though specific advisees are not listed. He has secured recognition and funding for initiatives such as EXBOX-Digital and MINT:labs, indicating successful grant acquisition. He is actively involved in teacher training, curriculum development, and science communication. He leads several innovative labs and teams, including ChemGerLab-VR (a virtual reality chemistry lab), EdTechAll, and the development of MINT:labs Science City Itzling. These initiatives focus on creating digital, hands-on, and inclusive science learning environments for both students and teachers.
