Evgeny Kharlamov is a Senior Research Fellow at the Department of Computer Science, University of Oxford. His research focuses on semantic technologies, particularly addressing challenges in data integration and Big Data. He leads the EU-funded Optique project on scalable end-user access to Big Data, and has contributed to foundational data management projects like Webdam and ACSI. Education: PhD in Computer Science (2011) from the Free University of Bozen-Bolzano (FUB), with research conducted also at Télécom ParisTech. European M.Sc. in Computer Science (2006) from Dresden University of Technology and FUB. Alumni of Novosibirsk State University (Russia). Research interests include ontology evolution, knowledge representation, query answering over complex data models, and probabilistic data management. His work spans conferences like VLDB, ICDT, KR, and journals such as TODS and JCSS. Grants include EU projects Optique, Webdam, and ACSI. Collaborations with institutions like Inria Saclay (France), Edinburgh University, and Télécom ParisTech.
Dr. Jessica Rivera Villicana is a Research Fellow in Computational and Geospatial Data Science at RMIT University's School of Global, Urban and Social Studies (GUSS). Her work integrates interdisciplinary approaches across ecology, education technology, and biomedical engineering. She leads projects like liveability scorecards analyzing urban inequities in Australian local government areas (LGAs), using geospatial and machine learning methodologies. Her research also focuses on EEG-based seizure detection, contributing to both technical advancements and clinical professional insights. Collaborating with global teams, she explores AI applications in healthcare (e.g., caregiver support systems) and cybersecurity (e.g., MLGuard framework). Key research areas include: Urban policy analysis through geospatial data modeling Machine learning interpretability in medical diagnostics Embodied conversational agents for caregiving AI safety and security in critical systems Behavioral modeling in interactive narratives Her recent work highlights include: 2025 Burwood & Canada Bay LGA liveability reports 2023 seizure detection survey with medical professionals MLGuard cybersecurity tool for ML models Dr. Villicana holds an ORCID identifier (0000-0003-1955-3310) and is based at RMIT's City Campus in Melbourne, Australia.
Dr. Carlos A. Rincon C. is an Instructional Assistant Professor in the Computer Science Department at the University of Houston. His research focuses on real-time systems, operating systems, information theory applications to scheduling, and storage systems. He holds a PhD in Computer Science from the University of Houston and has industry experience in software development prior to his academic career. Current research projects include optimizing probabilistic compression algorithms based on symbol position, developing machine learning models for disk failure prediction in heterogeneous environments, and applying information theory principles to real-time multiprocessor scheduling. His work bridges theoretical computer science with practical system optimization challenges. Dr. Rincon teaches core computer science courses including Operating Systems (COSC 3360) and Introduction to Programming (COSC 1437), with learning objectives centered on foundational concepts in system design, process management, memory management, and object-oriented programming principles. As Director of the ConocoPhillips Computer Science Learning Center, he oversees academic support programs and resources for computer science students. His pedagogical approach emphasizes algorithmic thinking and practical problem-solving skills development.
Paul Bruce McIlvenny is a Professor at the Department of Culture and Learning within the Faculty of Social Sciences and Humanities at Aalborg University. He leads research initiatives in the CLD (Communication, Language, and Discourse) Centre and the VILA - Video Research Lab . His work focuses on qualitative research methodologies, particularly in video-based analysis and immersive technologies. Education: Not explicitly stated in provided texts. Projects: Leads major projects like Big Video, CAVA360VR, and SIMVIDIA, focusing on immersive analytics, collaborative tools, and volumetric capture. Research Interests: Includes conversation analysis, discourse studies, mobility research, and innovative uses of virtual reality in qualitative methods. He advocates for 'Big Video' approaches integrating spatial and sensory data. Labs/Teams: Director of VILA Lab, collaborating with transdisciplinary teams on immersive technologies and qualitative innovation. Grants/Activities: Over 20 projects since 1998, with recent emphasis on software development for collaborative analysis (e.g., AVA360VR). Active in international conferences and editorial roles. Publications emphasize methodological innovation, including volumetric performance capture and spatial audio analysis. His work bridges social sciences with digital humanities through immersive technologies.
April Yi Wang is a tenure-track Assistant Professor at the Department of Computer Science, ETH Zürich, leading the PEACH Lab. She holds core faculty roles at the Institute for Intelligent Interactive Systems and the ETH AI Center. Her research focuses on human-centered approaches in programming, education technology, and data science collaboration. She earned her PhD from the University of Michigan (2023) and MSc from Simon Fraser University (2018), advised by Steve Oney and Christopher Brooks. Education: PhD in Information Science, University of Michigan (2018–2023) MSc in Computer Science, Simon Fraser University (2016–2018) B.Eng in Computer Science, Zhejiang University (2013–2016) Research Interests: Human-Computer Interaction (HCI) Programming Support Systems Collaborative Data Science AI-Enhanced Education Literate Programming Accessibility in Technology Recent Work Trends: Her 2025 publications emphasize AI-driven educational tools (e.g., Math2Visual for math pedagogy, datAR for data literacy), emotion-aware moderation systems, and studies on workplace multitasking. Her work bridges HCI with computational education, focusing on intuitive programming interfaces and inclusive design. Awards: Gary M. Olson Award (2023), ACM CHI Honorable Mentions (2023/2020/2018), Rising Stars in EECS (2022). Grants: Innovedum funding for Coducate project (2025). Lab Focus: Designing expressive systems for programming and data literacy through visual/tangible interfaces, AI co-decomposition tools, and interdisciplinary metaphors. Teaching: Courses on Human-Computer Interaction, Educational Technology, and Mixed Reality at ETH Zürich.
Haipeng Cai is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. Previously, he served as an Assistant Professor (2016–2022) and tenured Associate Professor (2022–2024) at Washington State University. His research focuses on software engineering and security, particularly program analysis, machine learning for security applications, and multilingual software systems. He holds a PhD in Computer Science and Engineering from the University of Notre Dame (2015). Education: PhD in Computer Science & Engineering (University of Notre Dame, 2015). Research interests include: Software security and reliability of distributed systems Machine learning-driven vulnerability detection and patching Analysis of multilingual software and cross-language bugs Tools like VGX (vulnerability data generation), PyRTFuzz (Python runtime fuzzing), and PolyFuzz (multi-language system fuzzing) Notable awards include the 2025 TOSEM Outstanding Paper Award for malware detection sustainability, multiple Distinguished Reviewer Awards, and the 2022 Junior Faculty Research Award. His work has led to over 50 publications, with a focus on practical security solutions. Advising: Mentor to students like Wen (PhD 2024, now at Utah State), Xiaoqin (first PhD graduate from his lab), and current advisees Haoran, Jiawei, and Yu. Projects include grants from NSF, ONR, and the Open Technology Fund. Key contributions include tools like VulGen (vulnerability dataset generation) and FlowDist (distributed system analysis), and labs focused on AI-driven security and multilingual software engineering.
Raúl Pardo is an Associate Professor in Computer Science at the IT University of Copenhagen (ITU). He previously held postdoctoral positions at ITU's SQUARE group and Inria's Privatics team. He earned his PhD from Chalmers University of Technology, focusing on formalizing privacy policies for social networks under Gerardo Schneider's supervision. His research integrates formal methods with privacy and security, addressing topics such as privacy risk analysis, probabilistic programming, and GDPR compliance. Key domains include genetic data, IoT, and social networks. Publications highlight contributions to privacy quantification, formal verification of consent mechanisms, and probabilistic systems analysis. He actively participates in conferences like SEFM, SOSYM, and ETAPS, and has organized tracks on verification and learning for autonomous systems. Pardo teaches courses on probabilistic programming, formal methods, and security at ITU, INSA Lyon, and Chalmers. Notable students include Rasmus Carl Rønneberg (SEFM'23) and Ivana Kellyérová (TIME'16). His work has been recognized, including a nomination for the Danish MSc thesis award. He leads the SQUARE research group at ITU, focusing on privacy-preserving software systems and probabilistic modeling.
Dr. Ajay Bansal is a Teaching Professor and Associate Program Chair of the Software Engineering program at Arizona State University's School of Computing and Augmented Intelligence. He joined ASU in 2011 and previously served as a visiting assistant professor at Georgetown University. His academic journey includes a Ph.D. in Computer Science from the University of Texas at Dallas, an MS from Texas Tech University, and a B.Tech. from NIT Warangal. His research focuses on declarative programming languages, including Logic Programming, Answer Set Programming, and Knowledge Representation. He also explores Computing Education, particularly Individualized Learning and Game-based approaches. Notable achievements include the Test of Time Award at ICLP 2016 for coinductive logic programming and multiple teaching awards, including the Fulton Teaching Excellence Award (2019, 2022). Dr. Bansal has over 80 peer-reviewed publications spanning journals, conferences, and book chapters. His work bridges theoretical contributions (e.g., stableKanren integration) and practical applications like automated assessment systems. He actively contributes to courses such as Intro to Programming Languages and Database Management. Education: Ph.D. Computer Science, University of Texas-Dallas (2007) M.S. Computer Science, Texas Tech University (2002) B.Tech. Computer Science and Engineering, NIT Warangal, India (1999) Awards: Test of Time Award (ICLP 2016) Fulton Teaching Excellence Award (2019, 2022) Best Teacher Award (Top 5%) (6x) Advising & Grants: While no specific student names are listed, his roles involve academic advising and curriculum development. Industry experience includes software engineering roles at SIEMENS India, Metallect Corp., and Tyler Technologies. Labs/Teams: Engaged in educational technology initiatives and declarative programming research groups.
Adrian Johnstone is a Professor of Computing in the Department of Computer Science at Royal Holloway, University of London. He is affiliated with the Centre for Software Language Engineering and the Centre for Programming Languages and Systems. His research focuses on programming language theory and implementation, with notable contributions to generalized parsing algorithms like GLL and ambiguity retained translation (ART). He has collaborated on projects such as PLanCompS (EPSRC-funded) and leads research on Babbage's language of thought (Leverhulme Trust). Key research interests include context-free grammars, customisable processors, and the history of computing. He has authored over 50 publications, with recent work presented at the ACM SIGPLAN International Conference on Software Language Engineering (SLE). His software contributions include the ART toolkit and reference GLL implementations available on GitHub. Adrian has supervised three major research projects and contributed to datasets like the reference GLL implementation codebase. His work bridges theoretical foundations with practical parser development, emphasizing performance testing and principled algorithm design.
Mark van den Brand is a Full Professor of Software Engineering and Technology at Eindhoven University of Technology (TU/e), where he holds the chair in Software Engineering and serves as Scientific Director of the Digital Twin Lab (part of EAISI). He concurrently holds a Visiting Professorship at Royal Holloway University of London. His academic journey includes a PhD in Computer Science from Radboud University (1992), prior roles as part-time Associate Professor at Vrije Universiteit Amsterdam, and senior research positions at CWI. He also serves as Editor-in-Chief of the Journal on Software Engineering of Autonomous Systems (JSEAS) and editorial board member of Computer Languages, Systems and Structures . His research focuses on software engineering fundamentals, digital twins, model-driven engineering, safety-critical systems, and automotive software. Key contributions include work on domain-specific languages, model consistency, and safety assessment frameworks. He leads the Software Engineering and Technology cluster at TU/e and directs educational initiatives in the Department of Mathematics and Computer Science. Recent publications emphasize digital twin engineering, model management, and automotive systems safety. He collaborates internationally on projects like the C-ITS reference architecture and functional safety for autonomous driving. His work bridges academic research with industrial challenges through tools like SAMOS and COGENT, addressing scalability and consistency in large-scale systems. Current educational responsibilities include courses on programming, digital systems, and data science. He actively engages with industry through the TU/e innovation ecosystem, focusing on impactful research solutions for modern software engineering problems.
Morris J.J. Weimerskirch is a Tutor affiliated with the University of Vienna and FH Technikum Vienna. He is involved in academic programming education, specifically teaching Programming for Physicists at the University of Vienna and conducting a Warm-Up course in Electronics at FH Technikum Vienna. His work focuses on developing computational skills for STEM students through structured coursework and practical exercises. He maintains an active educational portal with course materials covering topics like Python programming, version control systems (VCS), and object-oriented programming concepts demonstrated through projects like a Connect4 game implementation. His instructional resources emphasize systematic debugging, software development cycles, and configuration management using tools like Neovim and Jupyter. No academic awards or formal research publications are explicitly mentioned in the provided materials. His current roles involve curriculum development and student mentoring in computational physics and electronics education.
Pieter François is Professor of Cultural Evolution at the University of Oxford and a Tutorial Fellow in Human Sciences at Regent’s Park College. He is also the Academic Lead on Data Science and AI for the Arts and Humanities at the Alan Turing Institute and serves as Executive Director of the Seshat: Global History Databank. He leads major interdisciplinary projects including 'Data/Culture: Building Sustainable Scholarly Communities' and the Freedom of Religion or Belief Leadership Network. His research centers on cultural evolution , social complexity , and historical dynamics , with specific interests in ritual, warfare, inequality, and religious tolerance. He develops and applies methodologies for large-scale collaborative research in the digital humanities, leveraging structured historical data to test theories about societal development and collapse. His recent publications highlight a growing focus on artificial intelligence and historical reasoning , particularly through the HiST-LLM benchmark, which evaluates LLMs on PhD-level historical knowledge. These works reveal a trend toward integrating computational methods with deep historical analysis, emphasizing empirical validation and cross-cultural comparison. Scientific contributions and leadership roles include: Founding Director and Executive Director, Seshat: Global History Databank Principal Investigator, AHRC-funded 'Data/Culture' project Academic Lead, AI for Arts and Humanities, Alan Turing Institute Co-leader, Social Complexity and Collapse Group, Complexity Science Hub (CSH) He actively mentors researchers and leads collaborative teams across institutions, fostering sustainable scholarly communities around humanities datasets and software. His work is supported by major grants and involves extensive international collaboration, with future research likely to expand AI applications in historical sciences and deepen understanding of societal resilience and transformation. He is affiliated with key research groups such as the Social Complexity and Collapse Group at the CSH and contributes to major workshops and public-facing science communication, as seen in recent press coverage on AI and history.
Paweł Garbacz is an Assistant Professor at the Department of Computer Science Fundamentals within the Faculty of Philosophy at the Catholic University of Lublin. His work bridges formal logic, ontology, and their applications in computer science and philosophy. He has authored two books: Sentence Logic - One or Many and Logic and Artifacts . Research Focus: Formal logic, computational ontologies, philosophy of technical artifacts, and semantic interoperability. Publications: His recent work explores identity criteria, temporal logic, and the intersection of metaphysics with artificial intelligence. Collaborations: Active in interdisciplinary projects connecting philosophy with computer science, including contributions to the FOIS (Formal Ontology in Information Systems) conference series.
Aleksa Iričanin is a doctoral student and teaching assistant at the Department of Information Technologies, Faculty of Technical Sciences Čačak, University of Kragujevac. His academic career began during his master's studies, and he has been actively contributing to research in software engineering, machine learning, and data privacy. Education: Bachelor's and Master's degrees in Information Technology from Faculty of Technical Sciences Čačak Currently pursuing PhD in the same program Research Interests: Focused on code churn analysis, privacy-preserving machine learning, and horizontal scaling of PHP applications. His work integrates artificial intelligence with software maintenance and data privacy techniques like differential privacy. Publications: Recent articles explore trends in predictive modeling for software development, ethical AI frameworks, and cloud computing solutions for web applications. Contact: Office No. 025, Faculty of Technical Sciences Čačak, Svetog Save 65, Čačak, Serbia.
Aslan Askarov is an Associate Professor at Aarhus University, affiliated with the Department of Computer Science. His research bridges computer security and programming languages, focusing on formal methods for robust security guarantees. Current Projects : Troupe: A programming language for concurrent/distributed programming with dynamic information-flow control DenIM: Protocol for secure instant messaging with metadata privacy Teaching : Fall 2024: Compilers course Spring 2024: Language-Based Security course (link to 2023 version) His recent publications highlight trends in programming language security, formal verification of virtual machine safety, oblivious execution for reactive systems, and foundational work on noninterference and declassification. These align with subfields like separation logic, metadata privacy, type systems, and traffic analysis. Professional activities include program committee roles for PLDI 2025, CSF 2025, and prior conferences including PriSC, POST, and PLAS. He also co-chaired FCS 2017 and Nordsec 2019.
