Dr. William Eiers is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology. His research focuses on automated software verification, cloud security, and the intersection of AI with software engineering. He leads research on applying large language models to formal verification and policy synthesis. He currently advises graduate students and teaches courses including Algorithms and Discrete Structures. His work has been published in top conferences including ICSE, ASE, and ISSTA. He holds multiple patents related to access control policy analysis. Honors include UCSB Outstanding Graduate Student and teaching awards. He serves on program committees for major conferences including ICRA and IROS.
George T. Heineman is an Associate Professor of Computer Science at Worcester Polytechnic Institute (WPI). He holds a BS from Dartmouth College (1989), an MS (1990), and a PhD (1996) from Columbia University. His research focuses on software engineering, component-based systems, and modularity, with notable contributions to type-safe modular software evolution through the CoCo design pattern. Heineman emphasizes professional software engineering practices in teaching, challenging students with industry-relevant projects to foster best practices. His work has been published in leading venues like ECOOP, with a 2021 paper on CoCo gaining attention for its impact on Java language design. He received the WPI Trustees' Award for Outstanding Teaching in 2022, reflecting his dedication to education. His research spans algorithm design, system architecture, and cybersecurity, with publications ranging from foundational theory to practical applications in automated assessment and network security. Education: BS in Computer Science, Dartmouth College, 1989 MS in Computer Science, Columbia University, 1990 PhD in Computer Science, Columbia University, 1996 Research interests include software evolution, design patterns, and modular software systems. His recent work addresses challenges in maintaining stable APIs and enabling cohesive extensions in object-oriented systems. Collaborations with institutions like the University of Copenhagen and TU Dortmund highlight his international academic engagement. Beyond research, Heineman contributes to curriculum development, including WPI's new graduate programs in computing and workforce development initiatives.
Prof. Dr. Jana-Rebecca Rehse serves as Assistant Professor for Management Analytics at the University of Mannheim Business School, where she leads the Chair of Management Analytics within the Information Systems department. Her academic work bridges theoretical research with practical business applications, focusing on data-driven approaches to business process optimization. Her primary research interests encompass User Behavior Mining , Process Mining , and AI applications in business process management . Rehse investigates how organizations can leverage process mining techniques to extract meaningful insights from event logs, with particular attention to conformance checking, process resilience assessment, and the practical implementation challenges businesses face when adopting these technologies. Her work frequently addresses the intersection of human behavior and process execution, examining how user interactions with IT systems can be analyzed to improve process design and user experience. Analysis of her recent publications reveals a clear research trajectory toward increasingly sophisticated integration of artificial intelligence with traditional process mining techniques. Starting with foundational work on reference model mining and process discovery methodology, her research has evolved to address cutting-edge applications of generative AI, explainable AI, and predictive analytics in business process contexts. The majority of her work appears in top-tier information systems and business process management journals including Information Systems, Process Science, and ACM Transactions publications, demonstrating her significant contributions to the field. Professor Rehse actively collaborates with industry partners including Siemens and MEHRWERK, offering thesis opportunities and research projects that address real-world business challenges. Her current call for applications includes work-study programs at Siemens and master thesis topics focused on conformance checking in cooperation with MEHRWERK. She has recently introduced innovative thesis topics exploring the use of Generative AI for Emotion Identification, reflecting her forward-looking research agenda that anticipates emerging technological trends and their business implications.
Djamel E. Khelladi is a CNRS researcher affiliated with the IRISA research lab and the DIVERSE team at University of Rennes 1 , France. Previously, he held postdoctoral and PhD positions at Johannes Kepler University (JKU) Linz, Austria, and Université Pierre et Marie Curie (UPMC), France. Research interests include: Model-Driven Engineering Software Evolution & Co-evolution AI and Generative AI Applications Polyglot Programming Digital Twins Recent article trends focus on integrating Large Language Models (LLMs) for code-metamodel co-evolution, polyglot programming challenges, incremental build optimization in configurable systems, and empirical studies on software evolution. His work often bridges theoretical modeling with practical implementation in industrial contexts. Academic service roles include: Proceedings Co-Chair @MODELS 2025 Co-Organizer of Models and Evolution (ME) workshops (2023-2025) Co-Editor for special issue on Model Driven Engineering for Digital Twins (SoSym 2024/25) PC member in top venues: ICSE , ASE , MODELS , ECMFA , MSR , FSE
David Karger is a Professor of Computer Science at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds a B.A. from Harvard University and a Ph.D. from Stanford University. His research spans algorithms, information retrieval, human-computer interaction, and theory of computation. He leads the Haystack group, focusing on information management systems and collaborative tools. Notable contributions include the Scatter/Gather browsing system, the Mavo web application framework, and projects like Wikum and Squadbox for online collaboration and harassment prevention. Education: A.B. Summa cum Laude in Computer Science, Harvard University (1989) Ph.D. in Computer Science, Stanford University (1994) Research Interests: Karger’s work integrates algorithmic theory with practical systems, emphasizing human-centered design. Current projects address misinformation detection, social interaction systems, and educational tools. His research bridges theoretical computer science and applied domains such as web technologies and healthcare informatics. Awards: ACM Doctoral Dissertation Award (1994) Mathematical Programming Society Tucker Prize (1997) National Academy of Sciences Award for Initiative in Research (2004) Advising & Grants: Karger has advised over 30 students, many of whom have gone on to leadership roles in academia and industry. His work has been supported by grants from the MIT Schwarzman College of Computing and collaborations with companies like Akamai and Google. Labs & Teams: He leads the Haystack Group within CSAIL, collaborating with interdisciplinary teams on projects such as Mavo, Wikum, and Eyebrowse. His research also intersects with the Theory of Computation and Algorithms groups at MIT.
Prof. Wolfgang Ecker is a Professor at the Technical University of Munich (TUM), affiliated with the Chair of Design Automation within the TUM School of Computation, Information and Technology . His research focuses on Electronic Design Automation (EDA), RISC-V processor architectures, and hardware-software co-design. He leads projects advancing EDA tools for embedded systems, neural network acceleration, and formal verification methodologies. Ecker's work bridges machine learning techniques with traditional EDA challenges, addressing topics like energy-efficient AI inference and automated documentation generation. His contributions span compiler optimization, FPGA implementations, and fault analysis in digital systems. Recent research highlights include contributions to the TRISTAN project for RISC-V ecosystem development, model-driven architecture frameworks, and AI-driven timing analysis. He actively collaborates on open-source EDA tools and explores Rust-based embedded systems development. Ecker’s lab emphasizes practical applications in edge computing and automotive microcontroller safety, with a strong emphasis on interdisciplinary collaboration across TUM’s CIT School. His publications (15 most recent listed) reflect a focus on EDA tool innovation, processor design, and leveraging machine learning for hardware optimization. While no specific awards are mentioned, his involvement in ERC-funded projects and leadership in international collaborations underscores his academic impact.
Elisa Baniassad is a Teaching Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. She specializes in software engineering education and has received numerous teaching accolades including the UBC Killam Teaching Prize and CS-Can/INFO-CAN Excellence in Teaching Award. Her courses focus on software construction, engineering principles, and advanced software design. Dr. Baniassad has taught CPSC 310 (Introduction to Software Engineering), CPSC 210 (Software Construction), and CPSC 410 (Advanced Software Engineering) across multiple terms since 2000. Her research interests span educational methodologies in software engineering, aspect-oriented programming, and the design of effective learning tools. Notable contributions include studies on team dynamics in software development, automated assessment techniques, and pedagogical frameworks for large-scale programming courses. She has authored over 50 peer-reviewed articles on topics ranging from mutation analysis in student tests to the efficacy of online learning environments. Awards include recognition for teaching excellence at UBC and contributions to computer science education. Her work emphasizes practical applications of software engineering principles in academic settings, with a focus on fostering student mastery through innovative assessment strategies and feedback mechanisms.
Johan Jeuring is Professor of Software Technology for Learning and Teaching at Utrecht University's Department of Information and Computing Sciences. His research focuses on intelligent tutoring systems, computational thinking education, and game-based learning environments. Research Interests: Prof. Jeuring's work explores computational thinking pedagogy, automated feedback systems, and educational game design. His research bridges artificial intelligence with educational psychology to develop effective learning tools. Publications: Recent publications focus on augmented reality learning environments, programming education methodologies, automated assessment systems, and AI applications in education. His work combines empirical studies of learning behaviors with technological innovations in educational tools.
Philippas Tsigas is a Professor at the Department of Computer Science and Engineering at Chalmers University of Technology. He leads the Distributed Computing and Systems Research Group and has held roles as co-leader of research initiatives such as the PEPPHER project. His research spans distributed/parallel computing, information visualization, and fault-tolerant communication mechanisms. He has supervised numerous PhD students, including Yi Zhang, Håkan Sundell, and Farnaz Moradi. Research interests include lock-free data structures, multicore algorithms, secure network services, and visualization tools like Lydian and DataMeadow. Notable awards include Best Paper Awards at IPDPS 2003 and SNS 2012. His work has been published in top venues like IEEE Transactions on Parallel and Distributed Systems and ACM Journal of Experimental Algorithmics. Awards highlight contributions to lock-free algorithms and network modeling. Students have contributed to projects like NBmalloc (memory reclamation) and GPU Quicksort. Collaborations with institutions like SSF and VR have supported his research. Tsigas is also involved in teaching distributed systems and mentoring early-career researchers.
Professor Moe Thandar Wynn is a Co-Director of QUT's Centre for Data Science and holds a Professorship in the School of Information Systems at Queensland University of Technology (QUT). She leads the Process Science Academic Program and serves as the Academic Lead of Research for the School of Information Systems. Her expertise spans Process Mining, Data Quality, and Robotic Process Automation (RPA). Prof Wynn has attracted over AUD 6 million in research funding and holds an h-index of 41 with 8700+ citations. She is a member of the Australian Research Council College of Experts (2023–2025) and has received prestigious awards including the QLD Women in Technology Excellence Award (2024). Her research focuses on formal foundations of process modeling, verification, and automation. She has contributed to international conferences as a co-chair and program committee member, and co-edited special issues on RPA and process dynamics. Prof Wynn collaborates with industries like healthcare, insurance, and agriculture to optimize business processes through data-driven insights. Current research includes privacy-preserving process mining and quality-driven event log enhancement. Education: PhD (QUT, 2006), M. Information Technology (Research, QUT) Key Projects: Hospital Capacity Optimization, Liquid Process Model Collections, Risk-Aware BPM Supervision: Over 10 completed PhD/MSc students in process mining and analytics Awards: Multiple QUT Excellence Awards, ARC College Membership Her lab focuses on advancing process intelligence and RPA, with ongoing efforts in data quality frameworks and process mining standards (e.g., IEEE XES). She actively participates in industry partnerships, such as the CRC Food Agility project, to bridge research and real-world applications.
Elli Anastasiadi is an Assistant Professor in the Department of Computer Science at Aalborg University, part of the Technical Faculty of IT and Design. She is a member of the DEIS (Distributed, Embedded and Intelligent Systems) research group, which focuses on formal methods, verification, and intelligent systems. Prior to her current role, she was a postdoctoral researcher at Uppsala University and completed her PhD at Reykjavik University. Education: PhD in Computer Science, Reykjavik University (2022) Master’s in Applied Mathematics and Computer Science, NTUA, Greece Her research centers on formal verification of concurrent and parallel systems , with emphasis on runtime verification, process algebra, and logical foundations. She works extensively with hyperproperties, modal logic, and equational reasoning. Her work bridges theoretical computer science with practical verification tools. Recent publications show a strong trend in logic-based verification , particularly in modal and temporal logics, recursion, and monitor synthesis. Her work often involves complexity analysis and axiomatization of logical systems. Scientific Awards: PhD grant from Reykjavik University research fund She has advised no publicly listed students yet and is actively involved in academic service, including co-organizing workshops and being an invited speaker. She collaborates closely with leading researchers in concurrency theory and formal methods. Labs and Teams: Member of the DEIS research group at Aalborg University, contributing to projects on verification, distributed systems, and intelligent decision-making.
Dr Adam Barwell is a Lecturer and Researcher at the School of Computer Science, University of St Andrews. His research focuses on parallel programming, dependently typed languages, and formal methods in concurrent systems. He advises PhD student Leonid Nosovitskiy and has contributed to projects involving session types, distributed systems, and type-safe refactoring techniques. His educational background includes a PhD (not explicitly detailed here), and he actively engages in academic activities such as organizing events like Doors Open @ Computer Science 2025. Key research themes include concurrency control, functional programming paradigms, and improving code security through dependent types. Recent publications emphasize advancements in parallel language design, fault-tolerant protocols, and type-driven development. Collaborations span international conferences and institutions, reflecting his contributions to both theoretical and applied computer science domains.
Alessandro Orso is the Dean and Professor of the University of Georgia College of Engineering. Previously, he served as a professor at Georgia Institute of Technology, where he directed the Scientific Software Engineering Center and held leadership roles, including interim dean of the College of Computing (2023–2024). He earned his Ph.D. and master’s in computer science and electrical engineering from Politecnico di Milano, Italy. Orso’s research focuses on software testing, program analysis, and debugging, with applications in improving software reliability and security. His work includes developing automated testing tools (e.g., AutoRestTest), methodologies for scientific software engineering, and techniques for program debloating. His research has secured funding from agencies and industry partners like Google, IBM, and Microsoft, including a $11M grant from Schmidt Futures. His scientific awards include being a Distinguished Member of the ACM and IEEE Fellow. He advocates for innovative education, such as Georgia Tech’s Online Master of Science in Computer Science and his virtual software engineering course. His publications span automated testing, API analysis, and security, reflecting his commitment to advancing both academic research and practical software solutions. Orso’s leadership includes directing the Scientific Software Engineering Center, which pioneers methodologies for scientific software improvement and trains a new generation of engineers. His career blends academic excellence with impactful industry collaborations, driving advancements in software engineering practices.
Rahul Bhargava is an Assistant Professor in Journalism and Art + Design at Northeastern University, leading the Data Culture Group. His work focuses on creative data storytelling, computational journalism, and community-driven data practices to promote social justice and civic empowerment. He holds a Master of Science from MIT's Media Arts & Science program and a Bachelor of Science in Electrical and Computer Engineering from Carnegie Mellon University. Research interests include participatory data methods, feminist data science, AI ethics in newsrooms, and bridging gaps between technical and non-technical communities. Notable projects include the Data Theatre Collective (funded by Mellon Foundation), Data Murals , and Databasic.io tools. His 2025 book Community Data explores empowering marginalized groups through collaborative data practices. Key grants: Mellon Foundation ($500K), NSF ($724K), Knight Foundation Publications span journals like International Journal of Communication and conferences such as AEJMC and ICWSM Labs/affiliations: Media Ecosystems Analysis Group, MIT Data + Feminism Lab Teaching emphasizes hands-on data literacy, with courses on computational journalism and physical computing. His work has been featured in museum exhibits globally and recognized through awards including the Knight Foundation grants and Best Software (American Political Science Association).
Anastassiya Tchaikovsky is a postdoctoral researcher at the Institute of Analytical Chemistry, Department of Natural Sciences and Sustainable Resources, University of Natural Resources and Life Sciences, Vienna (BOKU). She holds a PhD in analytical chemistry and has an extensive research background in elemental and isotopic analysis, particularly using ICP-MS techniques. Her work spans food authentication, environmental forensics, and trace metal speciation. Her research interests include: Inductively Coupled Plasma Mass Spectrometry (ICP-MS) Chemometrics and multivariate data analysis Isotopic and elemental fingerprinting for provenance determination Metrology and method validation Separation techniques coupled to mass spectrometry Applications in food science, ecology, and human health Her recent publications demonstrate a strong trend toward interdisciplinary applications of analytical chemistry, particularly in verifying the geographical origin of food products (e.g., carrots, caviar, fish) using combined isotopic, elemental, and metabolomic fingerprints. She frequently employs chemometric modeling and data fusion techniques to enhance discrimination power. Her work also extends to biomedical applications, such as iron metabolism in neurodegenerative diseases and lead detoxification. She has received multiple scientific awards, including: Scholarship for a postdoc mentoring program (2019) Best talk award at DocDay, Tulln (2014) ESF-Studienabschluss scholarship (2013) Merkur scholarship from TU Vienna (2013) Best student lecture award at ICPMS Anwendertreffen (2012) Science scholarship from TU Vienna (2012) She has been involved in several research projects funded by the European Commission, Austrian federal and local governments, and private institutions. Her collaborative work includes advising on fish migration, caviar traceability, and citizen science initiatives like IsoPROTECT. She actively presents her research at international conferences and contributes to knowledge transfer in food safety and analytical methodology. She is affiliated with the Institute of Analytical Chemistry at BOKU and has no listed advisees, though she collaborates extensively with senior researchers and interdisciplinary teams.
