Prof. Dr. Mario Fritz is a leading academic at the CISPA Helmholtz Center for Information Security and Saarland University , with a focus on Trustworthy Information Processing . His work sits at the intersection of AI, Machine Learning, Security, and Privacy, addressing challenges in foundation models, health data, and adversarial robustness. Key Projects : ELLIOT (Multi-Modal Foundation Models), ELSA (Secure AI), PriSyn (Synthetic Health Data), AIgency (Generative AI in Cybersecurity), HMSP (Medical Security) Research Themes : AI/ML security, privacy-preserving techniques, causal modeling, healthcare applications, and ethical AI Recent Publications : Focus on LLM sampling, causal inference, model stealing, and privacy-aware document analysis Collaborations : European Laboratory for Learning and Intelligent Systems (ELLIS), BMBF-funded initiatives, GHGA (Human Genome Archive) Academic Leadership : As a Professor , he coordinates large-scale EU projects and mentors emerging researchers in AI ethics and security.
Ali C. Begen is a Professor in the Computer Science Department at Ozyegin University in Istanbul, Turkey. He is also the founder of Networked Media , a technology consultancy specializing in IP video solutions. Prior to academia, he was a Technical Lead at Cisco in San Jose, California, and has over 40 US patents in media transport protocols. Education: PhD in Electrical and Computer Engineering (Georgia Tech, 2006); BSc in Electrical Engineering (Bilkent University, 2001) Professional Recognition: Emmy® Award for Technology and Engineering (2022); ACM SIGMM Test of Time Paper Award (2022); SVTA Fellow (2023); IEEE Distinguished Lecturer (2016-2018) Research Focus: Begen’s work bridges low-latency live streaming , media-over-QUIC transport (MOQ) , and adaptive streaming protocols (DASH, WebRTC) . His recent research explores unifying real-time communication and content delivery under a single protocol to reduce complexity and improve scalability. Publications: Recent articles analyze bandwidth prediction , QUIC prioritization , and cross-protocol collaboration , reflecting a trend toward data-driven, network-aware media delivery systems. Honors: Emmy® Award (2022) ACM SIGMM Test of Time (2022) IEEE Distinguished Lecturer (2016, re-elected 2018) SVTA Fellow (2023) Royal Society Newton Fellowship (2019) Advising & Collaborations: Begen has served on thesis committees for over 15 PhD/MS students at institutions like Ozyegin University, University of Klagenfurt, and Koc University. He actively contributes to standards bodies (IEEE, ACM) and industry consortia like the Streaming Video Technology Alliance.
Gerard de Melo is a Professor and Chair of Artificial Intelligence and Intelligent Systems at the Hasso Plattner Institute (University of Potsdam, Germany). He also serves as a member of the Cognitive Sciences Research Focus and ELLIS Unit Potsdam . Research Interests: AI and Machine Learning Natural Language Processing Cross-modal AI (vision-language models, knowledge graphs) Societal and philosophical aspects of AI Medical Informatics Recommendation Systems Graph Neural Networks Recent Article Trends: His work spans generative AI (Vector Grimoire), medical LLM evaluation (CliMedBench), distributed training (Efficient Parallelization), and social media analysis (WallStreetBets) with a focus on multimodal systems and ethical AI . Scientific Recognition: Rutgers CSGSS Best Professor Award (Teaching, Advising) Best Paper Awards: NeurIPS Workshop, CIKM, EACL LANTERN IEEE TCSE Distinguished Paper Award First Runner-Up ACM WebSci Advising & Grants: Mentored students like Rohan Sawahn (Better World Award) and Maximilian Schall (IEEE TCSE Award). Secured major funding including German BMBF Grant for AI Service Center Berlin-Brandenburg and DARPA SocialSim Program support.
Tiffany Barnes is a Researcher at the North Carolina State University , affiliated with the Department of Computer Science within the College of Engineering . Her work focuses on Computer Science Education , Intelligent Tutoring Systems , and Deep Reinforcement Learning applications in pedagogy. Research interests include: Metacognitive interventions in learning Programming education tools Game-based learning environments Cultural barriers in CS education AI-driven educational technologies Recent publications explore LLM-powered educational tools , Parsons Problems for code comprehension, and Reinforcement Learning in adaptive tutors. Trends highlight student motivation , code trace analysis , and personalized feedback systems . Collaborations span institutions like Duke University , University of North Carolina , and Georgia Tech . Key projects include Jigsaw (problem decomposition) and MerryQuery (LLM for education).
Prof. Dr. Gero Holthoff serves as Professor for Controlling at the THM Business School (Technische Hochschule Mittelhessen) in Giessen, Germany, a position he has held since 2018. Additionally, since 2023, he leads the Master's program in Corporate Management. Prior to his academic career, Dr. Holthoff worked in Strategic Group Controlling at Bayer AG (2013-2018) and completed his doctoral studies in Controlling at the Justus Liebig University, Giessen, where he also earned his business administration degree (2003-2009). Dr. Holthoff's research program explores the intersection of accounting systems, language, and business analytics. His scholarly work investigates how cognitive styles and linguistic elements impact management accounting communication, the practical implementation of predictive analytics in corporate forecasting, and the challenges organizations face with translated financial reporting standards. His research demonstrates particular expertise in corporate performance management systems and the integration of technical language in business contexts, often drawing from his industry experience at Bayer AG. His publication record reveals an evolving research trajectory from foundational work on accounting language and cognitive aspects of management accounting toward more recent applications of business analytics and predictive modeling in corporate settings. This progression reflects the growing importance of data-driven approaches in controlling functions while maintaining his focus on practical implementation challenges in real-world business environments. Dr. Holthoff actively contributes to business education innovation, with recent work focusing on teaching code-free business analytics using platforms like KNIME. His industry background informs his practical approach to both academic research and teaching in controlling and corporate management, bridging theoretical concepts with real-world business applications.
Yao Zhu is a Visiting Professor at the Chair of Information Theory and Data Analytics, RWTH Aachen University . His research focuses on advanced wireless communication systems, particularly in Edge Computing , Ultra-Reliable Low-Latency Communication (URLLC) , and Physical Layer Security , leveraging Finite Blocklength Codes for next-generation network optimization. Key research areas include: Optimization of resource allocation and task scheduling in distributed edge learning and fog computing environments Reliability and energy efficiency trade-offs in Industrial IoT and V2X networks Novel applications of NOMA (Non-Orthogonal Multiple Access) and short-packet communication for secure and fresh data transmission Integration of physical layer deception with semantic reliability models His work explores the interplay between telecommunications and computer science principles to address challenges in low-latency, high-reliability networked systems. The Chair of Information Theory and Data Analytics serves as his academic base, focusing on theoretical and practical advancements in data-driven communication frameworks.
Prof. Dr. Emanuel Kitzelmann is a Professor of Applied Artificial Intelligence at Brandenburg University of Technology and Scientific Director of the AI Laboratory since 2023. His work bridges classical symbolic AI and modern machine learning, with a focus on integrating Large Language Models (LLMs) with structured knowledge bases like knowledge graphs and ontologies to enable reliable, explainable AI. He co-leads the SCALE-C research project on secure AI content generation for cybersecurity and directs the SmartRetrieve project on GraphRAG for campus chatbots. University: Brandenburg University of Technology Department: Computer Science and Media Rank: Professor His research spans hybrid neurosymbolic AI, inductive program synthesis, and robotics as AI application areas. Recent publications explore hallucination mitigation in LLMs, RAG techniques, and AI educational tools. He actively collaborates with industry partners like membraPure and REMINE GmbH, supervising student projects in cybersecurity, chatbots, and image-based analysis. Key initiatives include workshops on machine learning with ZF Getriebe Brandenburg and program committee roles for ECAI 2025 and IJCLR 2025.
Professor Jörg Hähner holds the Chair of Organic Computing at the University of Augsburg's Faculty of Applied Computer Science within the Institute of Computer Science. He leads a research team focused on evolutionary computation, self-organizing systems, and intelligent computing approaches. His educational background includes computer science studies at TU Darmstadt. His academic career progression shows steady advancement in the field of organic and self-organizing computing systems. Prof. Hähner's research spans multiple interconnected domains in computational intelligence. His primary focus is on Organic Computing, which involves developing systems that can adapt and self-organize in complex environments. Within this framework, he has made significant contributions to Evolutionary Algorithms, particularly Cartesian Genetic Programming and Learning Classifier Systems. His work explores how these techniques can be applied to real-world problems such as predictive maintenance, energy systems optimization, and industrial automation. The research demonstrates a strong emphasis on both theoretical foundations and practical applications of self-adaptive systems. An analysis of his recent publications reveals a strong concentration on evolutionary computation techniques, particularly Cartesian Genetic Programming variants and Learning Classifier Systems. His research group has been actively developing frameworks like CRust_GP and GRAHF to advance modular construction of evolutionary algorithms. There's a clear trend toward applying these techniques to industrial problems including predictive maintenance, resource allocation in networks, and energy management systems. The publications show consistent exploration of fundamental questions about algorithm behavior while maintaining strong connections to practical applications. Prof. Hähner leads an active research group with numerous PhD students and collaborators, including Karen Poloczek, Henning Cui, Victor Gerling, Dr. Michael Heider, Marco Hüller, Neele Kemper, Helena Stegherr, Jonathan Wurth, and Roman Sraj. His team regularly publishes in top-tier conferences and journals in evolutionary computation, intelligent systems, and industrial applications. The Organic Computing research group maintains a strong presence in both theoretical and applied research, with projects spanning from foundational algorithm development to industrial applications in manufacturing, energy systems, and network optimization. The group's work demonstrates a cohesive research vision centered on creating adaptive, self-organizing computational systems that can operate effectively in complex real-world environments.
Joanne M. Atlee is a Professor in the Department of Computer Science at the University of Waterloo, Faculty of Mathematics. She has been an active member of the software engineering research community since at least 2015, with continuous involvement in major conferences through 2025. Her research focuses on software product lines, domain-specific languages, cyber-physical systems, and model-driven engineering. She has made significant contributions to variability management and formal methods in software engineering, with publications spanning industrial case studies, model analysis techniques, and language workbenches. Her recent publications demonstrate a strong emphasis on practical applications of software engineering theory, particularly in the areas of dynamic product lines, declarative analysis, and human-in-the-loop verification systems. Her work bridges theoretical foundations with industrial applicability. Dr. Atlee has held numerous leadership positions in the academic community, including serving as General Chair for ICSE 2019, one of the most prestigious conferences in software engineering. She has also been active as a committee member, session chair, and mentor across multiple conferences including ASE, ICSE, ESEC/FSE, and MODELS. In terms of academic service, she has contributed to doctoral symposia, new faculty development, and equity initiatives, demonstrating a commitment to mentoring the next generation of researchers and promoting diversity in computing.
Michael Philippsen is a Professor at the Department of Computer Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he leads the Chair of Programming Systems (Lehrstuhl für Informatik 2). His research spans software engineering, programming languages, high-performance computing, and machine learning applications. He has directed multiple significant research projects including Holoware (software visualization in VR/AR), ORKA (OpenMP for FPGAs), and CS4MINTS (computer science education initiatives). Prof. Philippsen's research focuses on improving software quality and developer productivity through innovative approaches. His work in software testing includes novel methods for detecting flaky tests using version history and test execution data. In compiler research, he has pioneered automated testing techniques and optimization methods, particularly for FPGA acceleration using OpenMP extensions. His Holoware project revolutionized software visualization by applying city metaphors in virtual reality to enhance program comprehension. Additionally, he has made significant contributions to machine learning applications in software engineering, including few-shot out-of-domain detection in natural language processing systems. His publication record demonstrates a strong trend toward interdisciplinary research bridging traditional software engineering with emerging technologies. A significant portion of his recent work focuses on optimizing compiler techniques for heterogeneous architectures, particularly FPGA acceleration through OpenMP extensions. His research in software visualization has produced award-winning work on layered software city metaphors that significantly improve program comprehension compared to traditional visualization techniques. The consistent theme across his work is applying practical, measurable solutions to real-world software engineering challenges. Best Paper Award for 'Multipurpose Cacheing to Accelerate OpenMP Target Regions on FPGAs' (2023) Best Paper Award for 'A Layered Software City for Dependency Visualization' (2020) Prof. Philippsen has secured substantial research funding from the German Federal Ministry for Economic Affairs and Energy (BMWE), the Bavarian State Ministry of Science and the Arts (StMWK), and the Fraunhofer Society. His projects often involve industry collaboration to ensure practical applicability. He has supervised numerous student theses contributing to his research in compiler testing and software visualization. His research group maintains specialized laboratories for VR/AR software visualization, FPGA acceleration, and educational tool development as part of the CS4MINTS project.
Michael Lyu is a Professor at The Chinese University of Hong Kong specializing in software engineering with a focus on cloud reliability, AIOps, and log analysis. His research bridges the gap between theoretical advances and practical industrial applications in large-scale cloud systems. His research interests span Software Engineering , Cloud Computing Reliability , AIOps , and Log Analysis . Dr. Lyu's work addresses critical challenges in modern cloud operations, including failure diagnosis, anomaly detection, and reliability engineering. His recent research has pivoted toward leveraging large language models for software engineering tasks, particularly in code generation and log analysis. His publication portfolio demonstrates consistent contributions to major software engineering conferences (ASE, ICSE, ESEC/FSE) from 2018-2025, with a noticeable increase in LLM-related research since 2023. The trend shows a clear evolution from traditional software engineering topics toward AI-driven approaches for cloud operations. ICSE 2021 Keynote: "Reliability-Driven AIOps for Cloud Resilience" ASE 2023: Maat: Performance Metric Anomaly Anticipation for Cloud Services ASE 2024: LILAC: Log Parsing using LLMs with Adaptive Parsing Cache Dr. Lyu actively mentors students, with numerous co-authored publications showing his advisees as first authors. His work receives significant attention in both academic and industrial software engineering communities, addressing practical problems faced by large-scale cloud service providers. His research group appears focused on developing data-driven approaches for improving cloud system reliability through advanced analytics of logs, traces, and KPIs.
Xavier Devroey is an Assistant Professor of Software Engineering at the University of Namur in Belgium. He co-leads the SNAIL Team with Benoît Vanderose, focusing on innovative approaches to software testing and automation. His work bridges academic research with practical applications in the software engineering community. His educational background includes a Ph.D. and Master's in Computer Science from the University of Namur, plus a Bachelor's in Analyst Programming from Haute Ecole de Bruxelles, Belgium. This comprehensive academic training informs his research and teaching approach. Devroey's research interests center on Software Testing , with particular emphasis on Search-Based Software Engineering and Software Variability . His specific focus areas include: Search-Based Testing and Fuzzing Model-Based Testing Mutation Testing Variability Modeling Software Product Line Testing Test suite augmentation DevOps integration These interests reflect his commitment to advancing automated approaches for test case design, generation, selection, and prioritization. His recent publication portfolio (2019-2025) demonstrates consistent contributions to software testing research, with particular focus on crash reproduction, API testing, and innovative approaches to test automation. The articles reveal a strong emphasis on practical applications of search-based techniques across various testing domains. Devroey maintains active engagement with the academic community through conference participation, having served on program committees for major software engineering conferences including ASE, ICSE, ISSTA, and ICST across multiple years (2019-2025). He also contributes to educational aspects of software engineering, with publications examining testing education approaches and tools for programming exercise assessment. His personal website (xdevroey.be) and GitHub profile demonstrate his commitment to open academic practices and community engagement.
Dr. Timo Keller is a Visiting Professor of Mathematics at the Institute of Mathematics, University of Würzburg, holding a temporary professorship from October 2024 to March 2027. His academic position is specifically in the Chair of Mathematics I (Algebra), with his office located in Building 30 (Mathematik West), Room 03.010 at Emil-Fischer-Straße 30, 97074 Würzburg. Dr. Keller's research focuses on Arithmetic Geometry, particularly the arithmetic and computational aspects of curves and abelian varieties over arithmetic fields. His work encompasses rational points (especially of modular curves), modular forms, Galois representations, L-functions, and cohomology of abelian varieties. A significant portion of his research addresses the Birch–Swinnerton-Dyer conjecture, which establishes profound connections between algebraic and analytic, local and global invariants of elliptic curves over Q and more generally abelian varieties over global fields. He also investigates rational points on modular curves, which serve as moduli spaces for elliptic curves with additional data like level structure. His recent scholarly output demonstrates a strong emphasis on computational methods in number theory, particularly the Chabauty–Kim method for finding rational points, verification of the Birch-Swinnerton-Dyer conjecture for abelian varieties, and exploration of modular curves. His work bridges theoretical developments with computational implementations, as evidenced by his GitHub repository for published article code. Marie Skłodowska-Curie postdoctoral fellowship Dr. Keller previously held positions at Leibniz Universität Hannover and Universität Bayreuth before his postdoctoral fellowship in Groningen. His academic trajectory reflects a deep engagement with arithmetic geometry, with a consistent focus on computational approaches to classical problems in number theory.
Dr. Tim Gerrits is a researcher at the Institute for Visualization (VIS) at RWTH Aachen University, where he leads the Visualization Team. His work bridges scientific visualization, high-performance computing, and immersive technologies, with a strong focus on in-situ and in-transit analysis for large-scale simulations. University: RWTH Aachen University Institute: Institute for Visualization (VIS) Role: Lead of the Visualization Team Tim Gerrits' research centers on developing tools and frameworks for efficient and interactive visualization of complex scientific data. His interests include ensemble data analysis, uncertainty visualization, virtual reality interaction techniques, and leveraging game engines like Unreal Engine for scientific applications. He is particularly active in the domain of neuronal network simulations and oceanographic modeling. His recent publications highlight a strong trend toward accessible, real-time, and hybrid visualization workflows. He has contributed to the development of DaVE, a curated database of visualization examples to support HPC users, and Insite, a lightweight pipeline for in-transit processing in neuroscience simulations. His work emphasizes usability, performance, and integration with existing scientific workflows. Scientific Awards: Best Paper Award at IEEE Uncertainty Visualization Workshop, 2024 Honorable Mention Award at Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), 2022 Dr. Gerrits actively mentors and collaborates on interdisciplinary projects involving computational neuroscience and climate modeling. He has led the curation of datasets for the IEEE SciVis Contest and promotes open science through Zenodo-hosted resources. His lab focuses on building scalable, user-centered visualization systems that empower domain scientists to gain early insights from massive simulations.
Alexander Repenning is a Professor in the Department of Computer Science at the University of Colorado Boulder's College of Engineering and Applied Science. He has been a leading researcher in computer science education, particularly in the areas of visual programming, computational thinking, and game-based learning for over three decades. Repenning's research focuses on making programming accessible to novices through innovative environments like AgentSheets and AgentCubes. His work emphasizes blocks-based programming, computational thinking patterns, and scalable approaches to computer science education. He has pioneered the Scalable Game Design methodology which has been implemented in schools worldwide to teach computational thinking through game design. His recent publications (2021-2024) show a continued focus on blocks-based programming environments, with emerging work integrating AI concepts into educational programming tools. His research demonstrates consistent interest in understanding how to make programming more accessible while preserving computational thinking principles, with particular attention to flow theory, scaffolding techniques, and measuring educational impact. Significant contributions to scalable game design methodology Pioneering work in blocks-based programming for education Development of AgentSheets and AgentCubes environments Repenning has mentored numerous researchers who have become leaders in computer science education themselves, including Ashok Basawapatna, Andri Ioannidou, and David Webb. His work has been supported by multiple NSF grants focused on broadening participation in computing and developing scalable approaches to computer science education. He leads the Scalable Game Design project which has created a comprehensive ecosystem for teaching computational thinking through game design. His laboratory focuses on developing and evaluating visual programming environments, with particular attention to how students engage with computational concepts through game design activities. The team has developed sophisticated analytics to measure computational thinking patterns in student projects, enabling formative assessment in programming education.
