Louis Jachiet is an Assistant Professor in Computer Science at Télécom Paris, affiliated with the Data, Intelligence and Graphs (DIG) team within the Information Processing and Communication Laboratory (LTCI) and the Computer Sciences and Networks (Infres) department. His research focuses on algorithms, databases, programming languages, and logic. His work spans query optimization distributed SPARQL evaluation graph algorithms formal language theory program synthesis data provenance with a strong emphasis on bridging theoretical and applied research. Analysis of his publications reveals expertise in database systems graph processing automata theory query enumeration SPARQL optimization probabilistic databases across both theoretical and practical applications.
Marko Bachl serves as an Assistant Professor at the Institute of Sociology within the Department of Political and Social Sciences at Freie Universität Berlin. Based at Garystr. 55, Room 274, Berlin, he co-directs the DiMES (Digital Methods Innovation Lab) initiative, focusing on advancing computational approaches in social science research. His research centers on computational social science with emphases on political communication dynamics, gendered online interactions, and health information behavior. Key investigations include the impact of sexism on women's political participation, trust allocation in digital news ecosystems, and methodological innovations in computational text analysis. His work bridges theoretical communication frameworks with cutting-edge digital methods to address contemporary societal challenges. Analysis of his recent publications reveals a consistent integration of computational techniques with substantive social science questions, particularly regarding social media's role in political engagement and public health communication. His scholarship demonstrates methodological rigor through preregistered experiments, large-scale behavioral data analysis, and critical evaluation of measurement error in digital contexts. As DiMES co-director, Bachl leads initiatives developing innovative research tools like the MediaLiveTracker for real-time response measurement and promotes interdisciplinary collaboration through workshops on computational methods.
Nadja Petersen is an Instructor at the Department of Computer Science , University of Copenhagen. Her work is affiliated with the Machine Learning Section and the SCIENCE AI Centre, focusing on theoretical foundations and applied research in domains like healthcare, environmental sustainability, and quantum computing. Email: nape@di.ku.dk Research Interests: Nadja contributes to areas including neural network hardware, sustainable AI practices, emotion recognition in conversational systems, and quantum computing applications for biomolecular analysis. Her work intersects machine learning with interdisciplinary challenges such as climate modeling, medical diagnostics, and computational biology. Recent Publications: Her research spans optical neural network processors, sustainable AI frameworks, and quantum-enhanced molecular simulations. Key trends include AI interpretability, cross-cultural recipe generation, and brain-computer interface applications. Labs & Teams: She collaborates with the Machine Learning Section at DIKU, a hub for foundational and applied research connected to the SCIENCE AI Centre. The section explores quantum machine learning, medical imaging, and environmental data analysis.
Yufei Li is a Professor at Xi'an Jiaotong University's School of Computer Science and Technology, Department of Computer Science. With an extensive publication record spanning from 2007 to 2025, Dr. Li has established himself as a prominent researcher in database systems, software engineering, and machine learning applications. His work demonstrates strong interdisciplinary connections between computer science and electrical engineering, particularly in power systems applications. Dr. Li's research interests span multiple domains of computer science and engineering. His primary focus is on database systems, where he has pioneered work in LLM-based database tuning systems like GPTuner. He also has significant contributions in software configuration and performance optimization, as evidenced by his CSAT framework. His research extends to computer vision applications for security screening and medical diagnostics, as well as electrical engineering applications in power systems and UAV control. This diverse portfolio demonstrates his ability to bridge theoretical computer science with practical engineering applications across multiple domains. Analysis of Dr. Li's recent publications (2023-2025) reveals a strong trend toward integrating large language models with traditional computer science domains. His work on GPTuner represents a significant advancement in applying LLMs to database tuning, while his research on QUITE demonstrates innovative approaches to query rewriting using LLM agents. There's also a clear pattern of applying advanced machine learning techniques to solve domain-specific problems across electrical engineering, medical diagnostics, and industrial quality control. The interdisciplinary nature of his work positions him at the forefront of AI integration across multiple engineering disciplines. Dr. Li has established a productive research group with numerous doctoral students and collaborators, particularly Jiale Lao, Yibo Wang, and Jianguo Wang who frequently appear as co-authors on his recent publications. His research has been consistently funded, as evidenced by the steady stream of publications across multiple high-impact venues including IEEE Access, Journal of Systems and Software, and CVPR. His work demonstrates strong industry relevance with applications in database management, software configuration, security screening, and power systems. Dr. Li leads a research team focused on database systems and AI integration, with strong connections to both computer science and electrical engineering domains. His group appears to specialize in applying cutting-edge machine learning techniques, particularly large language models, to solve longstanding problems in database management and software engineering. The team maintains active collaborations with researchers across multiple institutions, as evidenced by the diverse author lists on his publications.
Henrik Bulskov is an Associate Professor in the Department of People and Technology (Programming, Logic and Intelligent Systems) at Roskilde University. He holds an MSc and PhD. His work focuses on natural logic systems, database querying, and ontology-based information retrieval. He has contributed to projects such as SEAFACTS (digital maritime history platform) and NorDigHealth (regional digital health solutions). Education: MSc and PhD (specific disciplines not explicitly stated) Research interests include formal logic systems integration with databases, machine learning applications in bioinformatics, and semantic summarization through ontologies. Recent work explores query optimization in natural logic knowledge bases and disparity analysis in neural networks. His publications highlight advancements in computational logic for knowledge management and biomedical text analysis. Bulskov has participated in international conferences and contributed to media discussions on big data applications. Advising: No explicit student advisees listed Grants: Principal/Co-investigator in multiple projects including SIABO (2007–2012) and Duuoo Analysis (2021) Labs/Teams: Involved in interdisciplinary teams focusing on bioinformatics, health tech, and digital humanities through collaborative projects.
Belgin Ergenç Bostanoğlu is an Associate Professor in the Computer Engineering Department at Izmir Institute of Technology (Turkey). Her research focuses on query optimization in distributed databases, association rule mining, privacy-preserving data mining, and graph-based algorithms. She leads the Dworld research laboratory and has held academic and industry roles since the 1980s. Education: B.Sc. in Computer Engineering, Middle East Technical University (1983) M.Sc. in Computer Engineering, Izmir Institute of Technology (2002) Ph.D. in Computer Engineering, Paul Sabatier University, France (2008) Research Interests: Dynamic frequent itemset mining and hiding under multiple support thresholds Subgraph mining in evolving graphs Federated query processing over linked data Privacy-preserving techniques in distributed databases Medical NLP applications (e.g., TurkMedNLI dataset) Her recent work emphasizes large-scale graph analysis, medical NLP dataset development, and adaptive join operators for federated SPARQL queries. She has contributed to over 30 peer-reviewed publications and led projects like the TÜBİTAK ARDEB 3501 platform for dynamic frequent itemset mining. Teaching: Courses include Advanced Database Management Systems, Knowledge Discovery, and Privacy-Preserving Data Mining. Labs & Projects: Manages Dworld lab and coordinates projects such as 'Turkish Medical NLP Model Development' (BAP-funded) and the Behavioral Next Generation Wireless Networks COST Action.
Benjamin Hilprecht is a researcher at the Technical University of Darmstadt, specializing in database systems and machine learning integration. His work focuses on data-efficient learned database components, neural approaches for query optimization, and security in generative models. Notable contributions include the DiffML framework for end-to-end differentiable ML pipelines and SPARE, a neural model for relational databases. He holds a PhD in Computer Science from TU Darmstadt (2022) and has collaborated extensively with Prof. Carsten Binnig on projects such as ReStore and DeepDB. His research interests span learned index structures, zero-shot learning in databases, and adversarial attacks on generative models.
Craig Macdonald is a Professor of Information Retrieval in the School of Computing Science at the University of Glasgow. His research focuses on information retrieval across diverse domains, including web, enterprise systems, social media, and smart cities. He has led projects like the SMART FP7 initiative, developing a search engine integrating sensor and social media data. Macdonald is a core developer of the Terrier IR platform and has contributed to TREC tracks such as Blog, Microblog, and Web. His PhD thesis addressed expertise search in enterprise environments, leading to practical systems like the SICSA expert search tool. He has organized conferences including CIKM 2011 and contributed to BCS IRSG and ACM SIGIR. His work spans theoretical advancements and applied systems, emphasizing reproducibility and open-source tools like PyTerrier. Education: BSc (1st Class Honours) in Computing Science (2004), PhD in Information Retrieval (completed). Research Interests: Expert search, blog/web/enterprise IR, sensor data integration, and recommendation systems. Notable projects include the SMART search engine for smart cities and contributions to TREC's Web track since 2014. He has also pioneered methods in query expansion, diversification, and sensitivity review systems. Publications (selected): Over 150+ papers in top venues like SIGIR, CIKM, and ECIR, focusing on retrieval models, recommendation systems, and evaluation frameworks. His work on RAG pipelines, pruning techniques, and contextual recommendation has driven practical advancements in AI-driven IR.
Erik van der Kouwe is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, holding dual appointments in the Computer Systems department and the Network Institute. His research focuses on system security, fault injection, fuzzing, and embedded systems security. He contributes to both theoretical advancements and practical implementations of security mechanisms. Key research areas include mitigating vulnerabilities through hardware-software co-design, exposing compiler optimization flaws that evade sanitizers, and developing efficient fuzzing frameworks for malware analysis. His work often bridges academic research with real-world applications in embedded systems and operating systems. Recent publications highlight innovations like InvisiGuard for microcontroller security, hwdbg for hardware debugging frameworks, and Enviral for environment-aware malware analysis. His research has been published in top-tier venues such as IEEE Transactions, ACM conferences, and workshops like EuroSEC. Teaching responsibilities include Advanced Operating Systems, Secure Programming, and Software Security courses. Collaborations span international teams working on topics like compiler hardening, embedded system resilience, and vulnerability benchmarking.
Dr. Shengyao Zhuang is an Adjunct Lecturer at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on advancing information retrieval systems, particularly leveraging neural networks, large language models (LLMs), and adversarial machine learning to enhance robustness and efficiency. He specializes in dense retrievers, query processing with typos, and federated search frameworks. His recent work explores vulnerabilities in retrieval systems, cross-modal applications, and optimization of retrieval models through techniques like Matryoshka training and reinforcement learning. Dr. Zhuang is affiliated with the IELAB research group, evident in collaborative TREC track submissions. His publications span conferences such as SIGIR and ACM venues, addressing challenges in zero-shot search, pseudo relevance feedback, and multimodal document processing. Notable contributions include developing the Tevatron toolkit and investigating the impact of adversarial attacks (e.g., pixel poisoning) on retrieval systems. He has also explored environmental considerations of IR models, emphasizing sustainable computing practices.
Dr. Ahmed Aleroud is an Associate Professor of Cyber Security at Augusta University's Department of Computer & Cyber Sciences within the School of Computer and Cyber Sciences. He holds a Ph.D. in Information Science from the University of Maryland, Baltimore County (UMBC), an MS in Computer Information Systems from Yarmouk University, and a BS in Software Engineering from Hashemite University. His research focuses on cyber-security, privacy-preserving data analytics, social engineering attack detection, and radical content identification via sentiment analysis. Key areas of expertise include intrusion detection, privacy-preserving techniques, and adversarial machine learning applications. His work is supported by organizations like MITRE and the European IP Networks (RIPE). Notable awards include the RIPE Academic Cooperation Initiative (2018) and European IP Networks support (2017). Education: Ph.D. in Information Science, University of Maryland, 2014 BS in Software Engineering, Hashemite University, 2006 His teaching interests span cyber security, data mining, and databases. Recent publications emphasize generative AI's role in deception detection, adversarial attacks on intrusion detection systems, and privacy-preserving frameworks for IoT and smart grids. He actively contributes to professional service as a reviewer for ACM Transactions on Intelligent Systems and Technology (TIST). Grants & Support: MITRE, RIPE, Maryland Innovation Initiative Service Roles: Faculty search committee member, data science curriculum developer, and graduate research day judge
Jerry Alan Fails is the Chair and Professor in the Computer Science Department at Boise State University, Idaho. He specializes in Human-Computer Interaction (HCI), focusing on designing technologies with and for children using participatory design methods. His research emphasizes ethical concerns in child-centered design, digital privacy, and inclusive technology development at global scales. He leads the intergenerational Kidsteam design group, which collaborates with children and adults to co-design technologies. Key research areas include children’s online privacy, digital resilience, and innovative design modalities for diverse populations. Fails also serves as International Steering Committee Chair for ACM Interaction Design and Children (IDC), organizing major conferences like the 2019 ACM IDC in Boise. His work integrates participatory design principles with ethical frameworks, addressing challenges in emerging technologies such as extended reality (XR) and wearable devices. He actively contributes to academic committees and editorial roles, including the IJCCI Associate Editorship. Current projects explore children’s online information-seeking behaviors, family digital ecology of fear, and scalable co-design practices. He advocates for democratizing ethical technology development through interdisciplinary collaboration and global engagement.
Dr. Annabel Latham is a Senior Lecturer in Computer Science at Manchester Metropolitan University within the Faculty of Science and Engineering’s Computing and Mathematics department. She holds a PhD in Artificial Intelligence, an MSc in Computing, a Postgraduate Certificate in Academic Practice, a CIM Diploma in Marketing, and a BSc(Hons) in Computation. As a Fellow of the Higher Education Academy (FHEA) and Senior Member of IEEE (SMIEEE), she contributes extensively to AI research and education. PhD in Artificial Intelligence MSc in Computing PGC Academic Practice CIM Diploma in Marketing BSc(Hons) Computation FHEA (2015) SMIEEE (2018) Research Interests include Artificial Intelligence in Education , Ethics of AI , and Computational Intelligence . She specializes in conversational agents, intelligent tutoring systems, and public trust in AI. Her work addresses fairness, accountability, and accessibility in AI-driven education, leveraging technologies like large language models and fuzzy logic. Research Outputs span 15 years, with a focus on explainable AI, educational applications of conversational agents, and ethical frameworks. Recent work (2024-2025) explores postdigital citizen science, trustworthy AI implementation, and XAI usability for non-specialists. 2023 IEEE Region 8 Outstanding Women in Engineering Volunteer Award 2019 IEEE Region 8 Outstanding Women in Engineering Affinity Group of the Year 2018 Outstanding Peer Reviewer, Elsevier: Computers & Education Senior Member IEEE (2018) FHEA (2015) Teaching and Supervision includes undergraduate Databases, postgraduate units in Information Systems and AI Ethics. She supervises MSc and PhD students in areas like explainability-aware machine learning, data responsibility, and AI trustworthiness. Her grants involve collaborations with international funding bodies such as NAFOSTED (Vietnam) and Croatia’s National Council for Science. Labs and Groups include the Computational Intelligence Lab, Machine Intelligence research group, and the Data and AI Ethics research group, where she co-leads initiatives on ethical AI in education.
Achim Rettinger is a full professor at Trier University, leading the research group krAil (Knowledge Representation Learning). He specializes in machine learning, natural language understanding, and human-centered AI. His work focuses on expressive knowledge representations and their applications in semantic technologies. Education: Studied Computer Science at Universität Koblenz (Germany), University of Georgia (USA), and University of Alberta (Canada). PhD in machine learning at TU Munich/Siemens AG, followed by habilitation at KIT (2016). Served as interim professor at Karlsruhe Institute of Technology (2018/19). Research interests include knowledge graphs, cross-lingual semantic annotation, and data-driven analysis in political and medical domains. Notable contributions include the X-LiSA framework and work on semantic web technologies. Awards include best paper and challenge awards at ISWC and ESWC conferences. Leadership roles include senior PC member at ISWC, track chair at ESWC, and membership in AI for Good Foundation. Active in EU projects, DFG grants, and large-scale collaborative initiatives. Key projects: BreXearch (cross-lingual Brexit analysis), xLiMe System (semantic search), and medical decision support systems for liver surgery. Collaborates with interdisciplinary teams on cognition-guided surgery and data integration. Publications span top venues like ISWC, NeurIPS, ICLR, and CVPR, with a focus on semantic web, machine learning, and applied AI solutions.
Dr. Steven J Zeil is an Associate Professor in the Department of Computer Science at Old Dominion University. He holds a Ph.D. from Ohio State University (1981). His research focuses on software testing, reliability measurement, and educational technology innovations like distance learning and algorithm animation engines. He has developed notable projects such as Extract (metadata extraction) and AlgAE (algorithm visualization tool). His teaching includes advanced courses in software engineering, data structures, and programming languages, with a strong emphasis on online course development. Key research areas include reliability growth modeling, ontology-based reasoning systems, and machine learning applications. He has contributed to standards in build management (Gradle), software configuration management, and system testing methodologies. His work bridges academic research with practical software development practices, emphasizing automation and collaboration in modern software engineering.
