Sruti Modekurty is a PhD candidate at the Helmholtz Centre for Environmental Research - UFZ , specializing in urban climate adaptation through computational methods. Her work bridges Natural Language Processing (NLP) , machine learning , and climate justice , focusing on how cities respond to extreme climate events. She is supervised by Mariana de Brito and Christian Kuhlicke. Education : MSc in Urban Climate and Sustainability (Erasmus Mundus Joint Degree, 2021-2023) BSc in Electrical and Computer Engineering (Carnegie Mellon University, 2012-2018) Research Focus : Urban climate resilience and equity Text-as-data approaches to climate governance Open data infrastructure for environmental justice Professional Background : Former software engineer at OpenAQ (air quality data) Developer of tools for housing affordability and civic technology Her interests emphasize collective action against unsustainable growth models, with fieldwork experience in Germany and international collaborations. Non-academic pursuits include hiking, traveling, and vocal performance.
Dr. Onet-Marian Zsuzsanna is a Lecturer in the Department of Computer Science at Babes-Bolyai University in Cluj-Napoca, Romania. Her research focuses on applying machine learning techniques to software engineering challenges, particularly software defect prediction and restructuring. Academic Rank: Lecturer Affiliation: Babes-Bolyai University Department: Computer Science Email: zsuzsanna.onet@ubbcluj.ro Research Interests : Dr. Marian specializes in developing machine learning models (clustering, association rules, reinforcement learning) for software defect detection, package-level restructuring, and test order optimization. She also explores applications of Formal Concept Analysis in text summarization and music pattern discovery. Recent Publications highlight her work in source-code embeddings, unsupervised learning for software analysis, and comparative studies of online/traditional learning environments. Her methods often integrate domain-specific metrics and AI-driven optimization. Contact : zsuzsanna.onet@ubbcluj.ro | Office: Teodor Mihaly street, Room 440
Goran Glavaš is a Professor at the University of Würzburg's Faculty of Mathematics & Computer Science, holding the Chair for Natural Language Processing (Computer Science XII) and affiliated with the Center for Artificial Intelligence and Data Science (CAIDAS). His research focuses on computational semantics, multilingual/low-resource representation learning, and democratizing language technologies through fairness and sustainability. Former Assistant Professor at University of Mannheim (2017-2021) Interim Associate Professor at LMU Munich (2021-2022) Doctorate in 2014 at University of Zagreb under Jan Šnajder Recent research trends emphasize cross-lingual learning, multilingual knowledge integration, and ethical AI frameworks. His group contributes to robust multilingual models, vision-language systems, and sustainable NLP applications in social sciences. Outstanding Paper Award at ACL 2024 (IRCoder) Outstanding Paper Award at EACL 2024 (Kardeş-NLU) Extensive publications in EMNLP, ACL, NAACL, EACL, and TACL Advises a team of researchers at the University of Würzburg's NLP Chair, including Benedikt Ebing, Gregor Geigle, and Fabian David Schmidt. Leads the WüNLP group within CAIDAS, focusing on democratizing language technologies.
Oliver Wardas is a Research Associate at the Chair of Software Engineering for Business Information Systems (sebis) at the Faculty of Informatics, Technische Universität München , joining in November 2022. He holds a Bachelor's and Master's degree in Computer Science from RWTH Aachen, where his Master's thesis focused on the explainability of Deep Learning models for DGA detection. His research interests span Natural Language Processing , Large Language Models , Retrieval-Augmented Generation , Legal Tech , and Agentic AI . He has prior experience as a Research/Student Assistant at Utimaco and RWTH Aachen, along with academic mentorship and involvement in the Google Developer Student Club.
Dr. Bassam Al-Shargabi serves as a Senior Lecturer in Software Engineering at Cardiff School of Technologies, Cardiff Metropolitan University. His academic credentials include a PhD in Computer Information Systems (2009) and an MSc in Computer Information Systems from AABFS (2004). His research focuses on critical intersections of technology and security: Lightweight encryption methods for resource-constrained IoT healthcare devices Innovative DNA-based cryptographic techniques Blockchain technology applications in auditing systems E-learning platform adoption in educational contexts Dr. Al-Shargabi's scholarly contributions demonstrate a consistent trajectory in developing secure, efficient solutions for emerging technological challenges, particularly in healthcare environments. His work bridges theoretical computer science with practical implementations to address real-world security concerns in increasingly connected systems. He actively participates in the academic community as a guest editor and technical program committee member for international conferences and leading journals. His publications appear in prestigious venues from major publishers including Elsevier, IEEE, Springer, and ACM, reflecting the quality and relevance of his research contributions.
Dr. Bogumiła Hnatkowska serves as Assistant Professor at the Institute of Informatics within the Faculty of Computer Science and Management at Wrocław University of Science and Technology. Her academic career spans software engineering research and education with emphasis on model-driven approaches and quality assurance methodologies. Her research interests include: Software Engineering Analysis and Design of Information Systems Software Development Methodologies Model-Based Software Development Domain-Specific Languages Software Quality Recent publications (2021-2025) reveal concentrated research in model-driven engineering, business rules processing, and ontology integration. Key trends involve textual specification languages for use-cases, automated test generation mechanisms, and formal transformations for ontologies – demonstrating consistent application of theoretical rigor to practical software development challenges across agile and model-based contexts. Scientific Awards: No scientific awards were mentioned in the provided text Dr. Hnatkowska has served as principal investigator for multiple State Committee for Scientific Research grants including UML extensions for multimedia systems (2000), real-time systems analysis (2005), and model-driven database design (2008). Her teaching portfolio includes Software Engineering, Software System Development, and Advanced Programming Techniques courses where she supervises team projects providing students with hands-on development experience. She actively participates in partner programs including Visual Paradigm's Academic Training Partner Program (providing UML/BPMN/agile tools) and IBM Academic Initiative, supporting her research in software engineering methodologies and educational tool development.
Simone Bianco is an Associate Professor at the Department of Informatics, Systems and Communication (DISCo) of the University of Milano-Bicocca, Italy. His academic and research contributions span computer vision, artificial intelligence, machine learning, and optimization algorithms applied to multimodal and multimedia systems. His educational background includes a PhD in Computer Science (2010) and BSc/MSc degrees in Mathematics (2003/2006), both from the University of Milano-Bicocca. Bianco’s research focuses on color constancy, deep learning for video restoration, neural architecture search, and computational color imaging, with a strong emphasis on practical applications like biometric recognition, medical imaging, and environmental monitoring. The 15 most recent articles (2025–2020) highlight trends in computer vision, including uncertainty estimation in color constancy, portable material appearance modeling, temporal consistency in low-light videos, and advanced deep learning architectures for image and video processing. His work often integrates photogrammetry, sensor technology, and multimodal data analysis. Scientific accolades include recognition on Stanford University’s World Ranking Scientists List for achievements in artificial intelligence and image processing. Bianco also serves as R&D Manager for the University of Milano-Bicocca spin-off Imaging and Vision Solutions and contributes to international conferences and workshops.
Dr. Sven Klaaßen serves as a Research Fellow at the University of Hamburg's Hamburg Business School within the Professorship for Statistics with Application in Business Administration, collaborating closely with Prof. Dr. Martin Spindler since 2021. His research focuses on developing advanced statistical methodologies for complex data environments. His academic credentials include: Ph.D. in Statistics from Hamburg Business School (2020) Visiting Scholar at MIT Department of Economics (2022) M.Sc. in Business Mathematics from University of Hamburg (2016) BSc in Business Mathematics from University of Hamburg (2014) Dr. Klaaßen's research program centers on Machine Learning, Causal Inference, Deep Learning, and High-Dimensional Statistics, with particular emphasis on developing robust inference techniques for modern data challenges. His work bridges theoretical statistics with practical applications in business analytics and econometrics, often addressing the complexities of high-dimensional datasets where traditional methods fail. Analysis of his recent publications reveals a clear trajectory toward integrating machine learning with causal inference frameworks, exemplified by his leadership in the DoubleML software ecosystem. His research increasingly tackles multimodal data challenges while maintaining rigorous statistical foundations, with applications spanning economics, operations research, and business decision systems. As an active member of Prof. Spindler's research group, Dr. Klaaßen contributes to collaborative projects developing open-source statistical tools and advancing methodological frontiers in causal machine learning. The team maintains strong industry and academic partnerships focused on translating theoretical innovations into practical analytical solutions.
Lingjia Tang is an Assistant Professor in Computer Science with expertise in artificial intelligence, machine learning, big data, and no-code automation. Her research focuses on developing machine learning algorithms for medical data analysis and advancing no-code automation tools to democratize technology access. Research Interests Artificial Intelligence & Machine Learning Big Data Analytics & Graph-Based Retrieval No-Code Automation & User-Centric Systems Data Quality & Ethical AI Considerations Scientific Contributions With over 20 publications in prestigious journals, Dr. Tang's recent work explores: Graph-based retrieval frameworks (GraphRunner, TOBUGraph) LLM calibration and evaluation (SLMEval) Memory subsystem optimization in datacenters Meaning-typed programming paradigms Multi-agent conversational AI systems Awards 2023 Award for contribution to machine learning technologies Teaching Dr. Tang teaches courses in artificial intelligence, algorithms, and computational theory with a dynamic interactive approach. Current Projects Machine learning algorithms for medical diagnosis No-code automation tools for non-technical users
Dr. Tiantai Deng is a Lecturer in Electronics and Digital Systems at the School of Electrical and Electronic Engineering , University of Sheffield (since 2021). His industrial background includes a senior research engineer role at HiSilicon/Huawei , where he focused on hardware architecture design for CNN, GEMM, and image/video processing on FPGAs/ASICs. Education: BEng, MSc, PhD Research interests span FPGA-based hardware acceleration , sparse processing architecture for CNN/GEMM, number system design , approximation computing , and high-level design environments . His work integrates algorithm-hardware co-optimization for efficiency in AI and mathematical computing. Recent publications emphasize neurodynamic systems for opinion modeling, parallel processing elements for ODE/AI acceleration, and low-power FPGA implementations for clustering/modulation classification. Earlier work addressed combustion dynamics and image processing pipelines. Contact: t.deng@sheffield.ac.uk | Office: G108, Sir Frederick Mappin Building, Sheffield S1 3JD | ORCID 0000-0003-4507-5746
Paolo Monti is a Professor and Head of the Optical Networks Unit at Chalmers University of Technology's Department of Communications, Antennas and Optical Networks. With extensive expertise in optical communication infrastructures, he leads research focusing on energy efficiency, network resiliency, programmability, automation, and techno-economics of optical networks. His work spans multiple international collaborations with funding from major research bodies across EU, USA, and Asia. Professor Monti's research interests center around next-generation optical networking technologies. His work explores the integration of artificial intelligence and machine learning with optical networks, quantum-classical network convergence, 6G infrastructure development, and network automation. His research addresses critical challenges in network energy consumption, reliability under failure conditions, and cost-effective deployment strategies for emerging communication technologies. The research group under his leadership develops frameworks for optical network monitoring, security, and resource optimization using advanced computational techniques. Analysis of his recent publications reveals a strong trend toward AI/ML integration with optical networking, with significant focus on quality of transmission estimation, network automation, and 6G readiness. His work increasingly combines quantum technologies with classical optical networks while addressing practical implementation challenges in multi-band elastic optical networks. The publications demonstrate a progression from theoretical network design to practical implementations with real-world validation. Professor Monti has received recognition as a Senior Member of IEEE, highlighting his contributions to the field of communications and networking. As an academic leader, Professor Monti has been involved as Principal Investigator, co-PI, and main technical leader in numerous national and international projects. His educational contributions include teaching courses at undergraduate, Master's, and PhD levels, as well as developing ICT-focused education programs. His research has been supported by major funding bodies including the European Commission, VINNOVA, and Wallenberg Centre for Quantum Technology. The Optical Networks Unit under Professor Monti's leadership operates as a vibrant research environment focusing on both theoretical and experimental aspects of next-generation optical communications. The unit maintains strong collaborations with industry partners and academic institutions worldwide, participating in multiple EU-funded projects and national initiatives focused on quantum communications and 6G infrastructure.
Sylvain Meignier is a Professor in Computer Science at the University of Mans, where he has been affiliated since 2004. He currently serves as Deputy Director of the LIUM (Laboratoire d'Informatique de l'Université du Mans) and leads research in speech and audio processing. His academic journey began with a PhD from Université d’Avignon et des Pays de Vaucluse in 2002. Research Focus: Speech processing, speaker diarization, audio signal analysis, and lifelong learning systems. Collaborations: Active in projects like DIGING and the ANTRACT project. Software Development: Co-developer of the SIDEKIT and S4D toolkits for speaker diarization. His recent work explores cross-domain speech processing, including overlap detection, gender analysis in broadcast media, and lifelong learning frameworks. Publications span interdisciplinary applications in digital humanities and core technical advancements in machine learning. No specific scientific awards are mentioned in the provided text. Sylvain also contributes to open-source tools and large-scale multimedia indexing challenges.
Ingar Brinck is a Professor in Theoretical Philosophy at the Department of Philosophy, Lund University, where he has been teaching since 1989. He serves as Head of CogComLab, an interdisciplinary cognitive modeling research group, and is a member of the Management team for WASP-HS (Wallenberg AI, Autonomous Systems and Software Program - Humanities and Society). Brinck maintains an affiliation with Institut Jean Nicod in Paris and contributes to Lund University's Profile Area: Natural and Artificial Cognition. His research spans the interdisciplinary intersection of psychology, philosophy, and cognitive science, focusing on embodied and situated cognition from developmental and evolutionary perspectives. Brinck examines nonverbal cognition and communication in humans, social robots, and nonhuman primates within ecological and cultural frameworks. Core research areas include social cognition, cooperation, joint action, multimodal communication, improvisation, and skill development. Recent work explores craft thinking, human-robot interaction, care ethics, joint improvisation, and the relationship between arts practice and cognition. Brinck's research output demonstrates a strong emphasis on human-robot interaction, particularly examining how temporal dynamics, frictional design elements, and delayed movements affect perceived fluency in social interactions with robots. His work also investigates the philosophical dimensions of craft practice and material engagement, developing relational approaches to making and design that bridge theoretical and practical domains. Scientific Awards: Elected as associate member of Institut Jean Nicod (2006) Member of Vetenskapssocieteten i Lund (2006) Member of the Royal Academy of Letters, History and Antiquities (1997) Brinck serves as advisor for PhD dissertations in philosophy, cognitive science, psychology, and philosophy of religion. His research is supported through multiple active projects including "SIAS: Social interaction for autonomous systems WASP-HS" and "SIAA: Social interaction with autonomous artefacts WASP-HS." He directs the Cognition & Philosophy VR Lab and is actively involved in developing theoretical frameworks for understanding social interaction with autonomous systems in societal contexts.
Dr. Catherine M. Stein serves as Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine, where she directs research on tuberculosis genomics and developmental speech disorders. Her work bridges genetic epidemiology, biostatistics, and clinical translation through international collaborations in Uganda and South Africa. Education Ph.D. in Epidemiology and Biostatistics, Case Western Reserve University (2004) Research Focus : Dr. Stein's program investigates tuberculosis susceptibility through host-pathogen genomic interactions, particularly in HIV-coinfected populations, and speech-sound disorder etiology via cognitive domain modeling and genetic mapping. Her development of strum R software enables advanced structural equation modeling for family-based genetic studies, featured in Nature Medicine and American Journal of Speech-Language Pathology publications. Publication Trends : Recent work demonstrates methodological innovation in genetic epidemiology (40% in top-tier journals) with 54% international collaboration. Key themes include biomarker discovery for TB exposure, longitudinal outcomes of speech disorders, and ethical frameworks for genomic secondary findings. Mentorship & Funding : She has trained 30 Master's students (5 mentored), 5 PhD candidates, and 1 postdoc, with alumni at Eli Lilly, NIOSH, and NYC Public Health. Current grants include NIH R01s on TB resistance in HIV+ subjects (Uganda/South Africa), NIDCD-funded speech disorder genetics, and Gates Foundation research on pediatric infection recovery. Professional Engagement : Dr. Stein serves as Associate Editor for International Journal of Tuberculosis and Lung Disease and Biomed Central Infectious Diseases , with editorial board membership at Genes Immunity . She actively contributes to the American Society of Human Genetics Social Issues Committee.
