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Ori LahavView profile

Associate Professor

• Programming Languages
• Verification
• Concurrency
• +4 more

Ori Lahav is a faculty member in the School of Computer Science at Tel Aviv University. His research is generously supported by an ERC Starting Grant and an ISF Grant. He actively supervises PhD and MSc students, and seeks highly motivated candidates for postdoc, PhD, and MSc positions in programming language theory, concurrency, and formal methods. Dr. Lahav completed his PhD at Tel Aviv University under the supervision of Arnon Avron. In 2014, he was a postdoctoral researcher at Tel Aviv University hosted by Mooly Sagiv. From 2014 to September 2017, he was a postdoctoral researcher at MPI-SWS in Germany hosted by Viktor Vafeiadis and Derek Dreyer. His primary research areas focus on programming languages and verification, with specialization in concurrency and relaxed memory models. He also has significant interests in proof-theory, semantics of non-classical logics, and automated reasoning. His work bridges theoretical foundations with practical applications in programming language design and implementation. Dr. Lahav's publication record shows a consistent trajectory of high-impact research in top-tier conferences including PLDI, POPL, OOPSLA, and ESOP. His recent work (2023-2025) demonstrates continued leadership in memory models, concurrency semantics, and verification techniques. His research spans both theoretical contributions in denotational semantics and practical tools for verification. Best Paper Award DISC 2024 Best Student Paper Award DISC 2024 Distinguished Artifact Award ESOP 2022 Distinguished Paper Award OOPSLA 2021 Kleene Award for Best Student Paper LICS 2013 Dr. Lahav actively advises students including Yoav Ben Shimon, Yotam Dvir, Amir Karniel, and Roy Margalit (PhD students), Yuval Katsman Ezra (MSc student), and has alumni including Ori Saporta (MSc) and Abhishek Kr Singh (postdoc, now Assistant Professor at IIIT Hyderabad). He has organized significant events including VMCAI 2024 and Dagstuhl Seminars on persistent programming. His teaching portfolio includes courses on Shared Memory Concurrency Semantics, Programming Language Foundations, and Software Foundations in Coq.

• Cybersecurity
• Computer Security
• Information Security
• +6 more

Matthew Allen Bishop is a Professor in the Department of Computer Science at the University of California, Davis. His primary affiliation is with the College of Engineering. Bishop's research focuses on cybersecurity, including secure programming, insider threat detection, malware analysis, and cybersecurity education. He has contributed extensively to curricular guidelines (e.g., CSEC 2017) and frameworks for cyber defense. His work spans theoretical advancements (e.g., intrusion detection models) and applied systems (e.g., secure voting platforms). Notable research areas include: Cybersecurity Education: Developing curricula and pedagogical frameworks for secure coding and ethical practices. Insider Threat Mitigation: Declarative approaches and behavioral analysis for detecting and preventing attacks. Malware Mitigation: Techniques leveraging uncertainty principles and defensive programming. Election Security: Analyzing vulnerabilities and designing secure voting systems. Bishop has collaborated with institutions like the Department of Homeland Security (DHS) and National Security Agency (NSA) on critical infrastructure protection. His publications span conferences like IEEE Security & Privacy, HICSS, and NSPW, emphasizing real-world applications of cybersecurity principles.

• Database Systems
• Big Data Analytics
• Cloud Database Systems
• +5 more

Michael J. Franklin is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley's College of Engineering. He has a prolific publication record spanning over three decades with more than 300 publications in top-tier database and systems conferences and journals, demonstrating his continued active research and leadership in the field. Franklin's research spans multiple areas within data management, with a recent focus on time-series analysis, AI-integrated database systems, cloud-native databases, and data quality. His work has evolved from traditional database systems to address modern challenges in big data, machine learning integration, and distributed systems. He has made significant contributions to data cleaning, crowdsourced data management, and stream processing systems. Analysis of his recent publications (2022-2025) reveals a strong trend toward integrating AI/ML capabilities with database systems, particularly in time-series anomaly detection, LLM applications for data management, and resource-adaptive query processing for cloud environments. His work increasingly focuses on practical systems that address real-world data challenges, often involving collaborations with industry partners and other leading academic researchers. Throughout his career, Franklin has mentored numerous PhD students who have become prominent researchers in their own right, including Sanjay Krishnan, Aaron Elmore, and Jiannan Wang. His collaborative research has frequently involved significant funding from NSF and industry partnerships, enabling large-scale systems research with real-world impact. Franklin leads research efforts that bridge theoretical database principles with practical system implementations. His work on projects like Data Station demonstrates his commitment to building trustworthy infrastructure for data sharing and analysis, addressing critical challenges in data privacy, security, and usability in collaborative environments.

• Computer Science Education
• Learning Technologies
• Virtual Reality in Education
• +5 more

Nadine Bergner is a Professor in the Teaching and Research Area of Learning Technologies (Informatik 9) at the Faculty of Computer Science, Dresden University of Technology (TU Dresden). Her work focuses on innovative approaches to computer science education across various educational levels, from primary schools to university settings. She leads research on learning technologies, VR applications in education, and teacher training in computer science. Prof. Bergner's research spans multiple dimensions of computer science education. She has developed and evaluated numerous educational approaches including VR serious games, physical computing with Arduino, and specialized learning environments like the InfoSphere student lab. Her work emphasizes making computer science accessible and engaging for diverse learners, including young children, senior citizens, and pre-service teachers. She has made significant contributions to understanding how students form mental models of computer science concepts and how these can be effectively developed through targeted educational interventions. Her recent publications reveal a strong focus on emerging technologies in education, particularly virtual reality applications for computer science learning. She has been exploring how different interaction modalities in VR affect learning outcomes, presence, and enjoyment. Another major thread in her recent work involves machine learning education, where she has developed concept inventories and game-based learning approaches to make complex ML concepts accessible to learners. She has also been actively researching how to integrate computer science and AI competencies into teacher education across various disciplines. Prof. Bergner has made substantial contributions to the development of educational materials and frameworks for computer science education. Her work includes creating resources for programming education ("Apps programmieren für Dummies Junior"), designing concept inventories for machine learning assessment, and developing comprehensive approaches to building computational thinking skills from early childhood through higher education. She has been instrumental in shaping computer science education policy through initiatives like the Dagstuhl Declaration on education in the digital networked world. Her research often involves collaborative projects with schools and teachers, focusing on practical implementation and evaluation of computer science education approaches. She has been particularly active in developing teacher training programs and materials to support the integration of digital competencies into school curricula. Her work with the InfoSphere student lab has provided valuable insights into how extracurricular learning environments can shape students' perceptions of computer science.

• Software Systems
• Programming Languages
• Compiler Construction
• +5 more

Johannes Waldmann is a Professor and Dean of Computer Science at the Faculty of Computer Science and Media, Leipzig University of Applied Sciences (HTWK Leipzig). He serves as a Member of the Faculty Council and maintains active engagement with students through regular office hours on Wednesdays from 10:00-10:30, requiring advance registration via email or the OPAL learning platform. Professor Waldmann's research spans theoretical computer science with practical applications, focusing on programming paradigms and languages, compiler construction, and computational complexity across different calculation models. His expertise includes declarative programming approaches (functional and constraint programming), automata theory, and formal languages. More recently, he has applied his theoretical knowledge to develop auto-grading systems for e-learning environments, bridging foundational computer science with educational technology. His teaching philosophy emphasizes mathematical foundations in computer science education, as reflected in his reference to G.H. Hardy's "A Mathematician's Apology," where he notes that "pure mathematics is distinctly more useful than applied" because "mathematical technique is taught mainly through pure mathematics." As Dean of Computer Science, he plays a pivotal leadership role in shaping academic programs and ensuring their alignment with both theoretical rigor and industry relevance at HTWK Leipzig.

• Answer Set Programming
• Artificial Intelligence
• Logic Programming
• +4 more

Torsten Schaub is a Professor at the Institute of Computer Science , University of Potsdam. His research focuses on Answer Set Programming (ASP) , constraint solving, temporal reasoning, and combinatorial optimization, with applications in multi-agent pathfinding, product configuration, and course timetabling. Key contributions include ASP-based tools for industrial-scale optimization problems, metric temporal logic implementations, and frameworks for dynamic equilibrium logic. Recent work explores efficient design space exploration, stream reasoning, and multi-shot ASP solving for complex domains. His publications emphasize hybrid ASP systems , integrating constraints and temporal logic, with co-authors across Europe and Asia. He actively develops tools like clingo and Clingraph for practical ASP applications in logistics, bioinformatics, and robotics. The articles reveal a trend toward multi-agent systems (e.g., pathfinding algorithms) and temporal extensions in ASP, combining formal logic with real-world problem-solving. Sub-fields include constraint satisfaction, logical abduction, and declarative modeling for optimization tasks.

Siegfried NijssenView profile

Assistant Professor

• Data Mining
• Pattern Mining
• Exploratory Data Analysis
• +5 more

Siegfried Nijssen is an Assistant Professor of Data Mining and Artificial Intelligence at the Catholic University of Louvain (UCLouvain) in Belgium, working within the ICTEAM research institute's Artificial Intelligence and Algorithms group. He has been at UCLouvain since 2016, previously serving as an Assistant Professor at University Leiden (2012-2016) and completing postdoctoral work at KU Leuven (2006-2015). He earned his PhD in Computer Science from University Leiden in 2006. His research focuses on making data analysis simpler through intersections between pattern mining, exploratory data analysis, and programming paradigms in Artificial Intelligence, particularly constraint programming and probabilistic programming. He has developed techniques for analyzing diverse data types including graphs, networks, and multi-relational data. His work bridges theoretical foundations with practical applications in decision tree learning, probabilistic networks, and source code analysis. Nijssen's recent publications demonstrate a strong focus on optimal decision trees, constraint-based pattern mining, and applications in bioinformatics and education. His research shows consistent evolution from foundational graph mining work (including the Gaston algorithm developed in 2004) to current work integrating machine learning with constraint programming for interpretable AI solutions. As an educator, he teaches courses including Mining Patterns in Data, Databases, and Artificial Intelligence and Machine Learning seminars at UCLouvain. He has advised numerous PhD students and postdocs, primarily in collaboration with Pierre Schaus, with former students like Tias Guns now holding professorships.

Elena ZuccaView profile

Associate Professor

• Programming Languages
• Type Systems
• Formal Semantics
• +6 more

Elena Zucca is an Associate Professor in the Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS) at the University of Genoa, Italy. She has a strong research focus on programming languages, particularly in the areas of type systems, formal semantics, and coinduction. Her work bridges theoretical foundations with practical implementations, as evidenced by her publications on topics like Featherweight Java, corecursion, and soundness proofs for programming language features. Her research interests span the theoretical and practical aspects of programming languages. She investigates the formal semantics of programming constructs, particularly focusing on the boundaries between finite and infinite computations. Her work on coinduction and corecursion explores how to safely handle infinite data structures and computations. She has made significant contributions to understanding soundness in programming language features, especially in relation to resource-aware semantics. Her research often combines theoretical foundations with practical implementations, as seen in her work on Featherweight Java extensions and effect systems. Analysis of her recent publications reveals a consistent focus on advanced type systems and semantics. Her work explores the theoretical boundaries of programming language features while maintaining practical relevance. A recurring theme is the relationship between finite and infinite computations, with particular attention to soundness guarantees. Her research has evolved from foundational work on lambda calculus and object-oriented programming toward more specialized topics like graded types, coeffects, and resource-aware semantics. Elena Zucca has been actively involved in the programming languages research community, serving on program committees for major conferences including SPLASH, ECOOP, and POPL. She has mentored students through doctoral courses on Declarative Programming and (Co)Induction. Her research has been supported through participation in various academic programs, including the SEPL research program mentioned in her course materials. She teaches a diverse range of courses at the University of Genoa, from undergraduate to doctoral levels. Her teaching portfolio includes Automata Theory and Computability, Algorithms Analysis and Design, Principles and Paradigms of Programming Languages, and specialized doctoral courses on Declarative Programming and (Co)Induction. Her courses often reflect her research interests, covering theoretical foundations of programming languages, formal methods, and advanced programming paradigms.

Max WillseyView profile

Assistant Professor

• Program Optimization
• Programming Languages
• Databases
• +5 more

Max Willsey is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, since 2024. He specializes in program optimization, leveraging techniques from programming languages, databases, and systems to develop robust and accessible compiler frameworks. His research focuses on equality saturation, E-Graphs, and the integration of Datalog with compiler optimizations. He has contributed to advancements in unifying algorithmic approaches, enabling faster and more extensible program analysis. Teaching: CS 164 (Programming Languages and Compilers, Spring 2025), CS 265 (Compiler Optimization, Fall 2024), and CS 294-260 (Declarative Program Analysis and Optimization, Spring 2024). Research Highlights: Development of the egg and egglog projects, co-organizing the EGRAPHS workshop, and leading the EGRAPHS Community for e-graphs researchers. His recent articles highlight trends in unifying traditional hash joins with worst-case optimal joins, applying equality saturation to diverse domains like Datalog and tensor graph optimization, and advancing E-Graphs for program synthesis and formal verification. Scientific Awards: SIGMOD Record Research Highlight, 2024 MIT PL Review Selection, 2024 Distinguished Paper, OOPSLA 2021 and POPL 2021 NSF Graduate Research Fellowship Honorable Mention, 2018 Qualcomm Innovation Fellow, 2019 Service: Committee Member, PLDI 2025, POPL 2025, ASPLOS 2025 Co-organizer, EGRAPHS 2024 and 2023 workshops Interviewer, UC Berkeley Graduate Admissions Committee, 2024

Ingo RohlfingView profile

Professor

• Causal Inference
• Qualitative Comparative Analysis
• Process Tracing
• +4 more

Professor Ingo Rohlfing is a Professor for Methods of Empirical Social Research at the University of Passau, specializing in advanced methodological approaches in social science research. His work focuses on bridging qualitative and quantitative methodologies to enhance causal inference and research transparency. Current position: Professor for Methods of Empirical Social Research, University of Passau Contact: ingo.rohlfing@uni-passau.de, +498515092720 Professional website: https://ingorohlfing.wordpress.com/ Rohlfing's primary research interests center on causal inference, qualitative and multimethod research, and research transparency and credibility. He has made significant contributions to Qualitative Comparative Analysis (QCA), process tracing, and Bayesian approaches to qualitative research. His work emphasizes methodological rigor and the integration of diverse methodological traditions to address complex social phenomena. His recent publications demonstrate a consistent focus on methodological innovation, particularly in integrating Bayesian statistics with qualitative methods. Rohlfing's 2025 article with Lion Behrens on integrating Bayesian regression analysis with Bayesian process tracing represents a cutting-edge contribution to mixed-methods design. His blog content shows ongoing engagement with critical issues in research transparency, open science, and the practical application of advanced methodological approaches. Rohlfing is actively involved in methodological training, offering courses such as the 5-day remote course 'Introduction to Qualitative Comparative Analysis' through the ICPSR Summer Program and an on-demand seminar on process tracing. His teaching reflects his commitment to making sophisticated methodological approaches accessible to social science researchers. Regular instructor at the ICPSR Summer Program Creator of self-paced methodological training materials Active participant in the DORA (Declaration on Research Assessment) movement

Joachim BaumeisterView profile

Professor

• Semantic Information Systems
• Knowledge Graphs
• Deep Learning
• +5 more

Joachim Baumeister is a Professor at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. While his primary employment since September 2010 has been at denkbares GmbH, a company specializing in knowledge-based systems, he continues to regularly give lectures at the university. His research focuses on Semantic Information Systems, Knowledge Graphs, Deep Learning applications, Natural Language Processing, and Knowledge-based Configuration for Industry 4.0. Professor Baumeister's work bridges theoretical AI research with practical industry applications, particularly in knowledge-based configuration systems and semantic technologies. His recent publications (2020-2024) reveal a strong emphasis on product configuration systems, semantic knowledge representation, regulatory document processing, and knowledge-based systems. His research has evolved from foundational work on semantic wikis and knowledge engineering to more recent applications involving deep learning and large language models, demonstrating adaptability to emerging technologies while maintaining focus on practical knowledge representation problems. Professor Baumeister's work demonstrates significant contributions to case-based reasoning, knowledge configuration, and semantic technologies, with applications spanning regulatory compliance, industrial configuration systems, and document processing. His current research areas include: Semantic Information Systems and Knowledge Graphs Deep Learning for Image Recognition and Language Understanding Knowledge-based Configuration for Industry 4.0 Natural Language Processing Intelligent Personal Assistants and Chat Bots Though specific students aren't listed in the provided information, Professor Baumeister actively invites students to contact him regarding projects, bachelor theses, and master theses in his areas of expertise. His work at denkbares GmbH focuses on the design, implementation, and evolution of knowledge-based systems and semantic information systems.

Dietmar SeipelView profile

Professor

• Logic Programming
• Deductive Databases
• Knowledge Engineering
• +5 more

Dietmar Seipel is a Professor at the University of Würzburg, affiliated with the Department of Computer Science within the Faculty of Mathematics and Computer Science. He has held this position since November 1995, establishing a distinguished academic career spanning over 25 years with significant contributions to logic-based computer science. Professor Seipel's research focuses on Logic Programming and Deductive Databases, with substantial expertise in Knowledge Engineering and Artificial Intelligence. His scholarly work bridges theoretical foundations with practical applications, particularly in rule-based systems, knowledge representation, and declarative programming paradigms. He has consistently advanced the field through both theoretical developments and practical implementations, creating tools that enable more effective knowledge management and reasoning systems. His publication trajectory demonstrates a clear evolution from foundational work in disjunctive logic programming to contemporary applications in knowledge representation and semantic technologies. Recent research shows continued innovation in integrating logic programming with modern programming languages and systems, including Python and JavaScript implementations. His work spans theoretical contributions to practical tool development, with applications across diverse domains including space systems, medical informatics, and business process management. Professor Seipel has made extensive contributions to the academic literature, with publications appearing consistently from the 1980s through to the present. His work has influenced both theoretical developments in logic programming and practical applications in knowledge-based systems. He has been actively involved in academic community building through conference organization, particularly for events related to declarative programming and knowledge management.

Joanne M. AtleeView profile

Professor

• Software Engineering
• Software Product Lines
• Domain-Specific Languages
• +5 more

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.

Anders MøllerView profile

Professor

• Static Program Analysis
• Dynamic Program Analysis
• JavaScript
• +5 more

Anders Møller is a Professor at the Department of Computer Science , Aarhus University , Denmark. His career spans roles as an author , committee member , and session chair in conferences like SPLASH, OOPSLA, ECOOP, ISSTA, ICSE, and PLDI. Affiliation: Aarhus University Co-founder: Coana Research Focus : Specializing in static and dynamic program analysis for JavaScript, TypeScript, Java, and Node.js applications, his work addresses: Pointer analysis precision in Java Race condition detection in Node.js Library evolution and semantic patching Soundness improvements in static analyzers Type safety in modern languages Concolic execution for web testing Publication Trends : Recent work (2021–2024) emphasizes security-critical static analysis (taint specifications, Node.js security), soundness optimization (approximate interpretation), and program verification (channel-based communication). Earlier work (2013–2018) includes foundational contributions to JavaScript refactoring , Dart type safety , and AJAX race detection . Scientific Recognition : ISSTA 2019 Distinguished Paper Award Leadership Roles : Active in steering committees for SPLASH, ECOOP, and SIGPLAN, with chairs in OOPSLA, ECOOP, and PLDI program committees.

Torsten BrindaView profile

Professor

• Computer Science Education
• Competency Modeling
• Digital Education

Prof. Dr. Torsten Brinda holds the Chair for Didactics of Informatics at the University of Duisburg-Essen's Faculty of Computer Science. His research centers on competency modeling in programming, digital education frameworks, and computer science pedagogy. He serves as chair of the GI department for computer science education and received the IFIP Service Award in 2022. Key research areas include: Modeling of digital competencies for teachers/students Automatic assessment in programming education International curriculum standards for CS education His publications (2015-2022) demonstrate consistent focus on educational informatics, featuring competency modeling studies, global CS education analyses, and contributions to the Dagstuhl Declaration on digital education. Recent work explores integrated digital competency frameworks for teacher training.