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Jinqiu YangView profile

Associate Professor

• software reliability
• quality assurance of machine learning systems
• autonomous vehicles
• +5 more

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. Her research focuses on improving software reliability and quality assurance, particularly in the context of machine learning systems and autonomous vehicles. She leads active research projects in software testing, automated program repair, and mining software repositories, with strong connections to both academic and industrial applications. Her research interests span software reliability, quality assurance of machine learning systems including autonomous vehicles, software testing, automated program repair, text analytics of software artifacts, and mining software repositories. She has developed novel approaches for testing deep learning libraries, evaluating robustness in autonomous driving systems, and tracking the evolution of static code warnings. Her work bridges traditional software engineering with emerging challenges in AI systems, addressing critical issues of reliability and safety in complex software environments. Yang's recent publications (2021-2025) demonstrate a clear trajectory toward AI/ML system reliability, with increasing focus on autonomous vehicles, concept drift detection, and security aspects of large language models. Her work spans both theoretical foundations and practical applications, often involving empirical studies of real-world systems and development of practical tools to address identified challenges. ACM SIGSOFT Distinguished Paper Award Dr. Yang actively mentors graduate students and is currently recruiting Master's and PhD candidates. She has secured significant research funding including NSERC Discovery Grants (2019-2025), Gina Cody Research and Innovation Fellowship (2024-2026), and participation in the NSERC CREATE Program SE4AI (2021-2026). Her research is supported by multiple grants including NOVA – FRQNT-NSERC PROGRAM (2024-2027) and Volt-Age Seed Grant (2024-2026). She leads research in the O-RISA Lab at Concordia University, focusing on reliability and security aspects of intelligent software systems. Her team collaborates with industry partners including IBM, where she previously worked at IBM Watson Research Lab and IBM CAS, bringing practical experience to her academic research.

Jingling XueView profile

Professor

• Programming Languages
• Compilers
• Program Analysis
• +5 more

Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.

Daye NamView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence
• Human-Computer Interaction
• +3 more

Daye Nam is an Assistant Professor in the Department of Informatics at the University of California, Irvine, where they design, build, and evaluate AI tools for developers using natural language processing techniques. Their work sits at the intersection of software engineering, artificial intelligence, and human-computer interaction, with a strong focus on creating useful and usable tools that make software development more accessible, efficient, and enjoyable. Education PhD in Software Engineering, Carnegie Mellon University (2018-2024) MS in Computer Science, University of Southern California (2016-2018) BS in Computer Science, Yonsei University (2012-2016) Research Interests Dr. Nam's research focuses on designing, building, and evaluating AI tools for programmers at all levels, with an emphasis on making these tools both useful and usable. Their work spans several key areas including machine learning for software engineering (ML4SE), developer experience, and human-AI interaction. They employ a user-centered approach that involves conducting empirical studies to understand programmers' needs, building and training machine learning models based on those insights, creating tools for programmers, and evaluating them using human-computer interaction methods. Their research has particular relevance to AI-powered developer tools, API documentation and discovery, and educational applications of AI for programming students. Publications and Research Trends Dr. Nam's recent publications demonstrate a clear trajectory toward understanding and improving how developers interact with AI systems. Their work increasingly focuses on empirical studies of developer-AI interaction, particularly with large language models for code generation and understanding. There's a strong emphasis on understanding trust in AI systems among developers, measuring the actual impact of AI on development speed, and designing tools that balance automation with user control. Their research methodology often combines log analysis, user studies, and the development of novel AI-powered tools that address specific developer pain points. Scientific Awards and Honors Best Tool Paper Award at ASE ACM Student Research Competition 2nd Place SIGSOFT CAPS Student Travel Award for FSE ACM SIGSOFT NSF Travel Award NSF Travel Award for ICSE SIGSOFT Best Research Award from University of Southern California Teaching and Service Dr. Nam teaches SWE 233: Intelligent User Interfaces at UC Irvine, guiding students through the design and evaluation of AI-powered interfaces for software development. They have previously served as a Teaching Assistant and Co-Instructor for Foundations of Software Engineering at Carnegie Mellon University. In terms of service, they've been on program committees for major software engineering conferences including ICSE, ASE, and FSE, and have reviewed papers for journals like TOSEM and Empirical Software Engineering. They've also been active in student support programs, organizing and mentoring for graduate applicant support initiatives.

Detlev MarpeView profile

Researcher

• Video Coding
• Image Compression
• Signal Processing
• +3 more

Detlev Marpe is a leading researcher at the Fraunhofer Heinrich Hertz Institute (HHI), serving as Head of the Video Coding & Analytics Department and Head of the Image & Video Coding Group. His work focuses on advancing video compression standards, including HEVC (H.265) and its extensions. He has contributed significantly to tools like entropy coding, transform coding, and scalable video coding. His research emphasizes efficient compression techniques, such as adaptive context models and wavelet-based methods, with applications in multimedia communication and low-delay video encoding. Affiliations: Fraunhofer Institute for Telecommunications HHI, Berlin, Germany Roles: Department Head, Research Group Leader, and Adjunct Lecturer at TU Berlin (2013/14) Research Interests: Video coding standards (HEVC, H.264/AVC), entropy coding (CABAC), wavelet-based compression, scalable video coding (SVC), multiview video coding (MVC), and rate-distortion optimization. His work bridges theoretical advancements with practical implementations, addressing challenges in compression efficiency, scalability, and real-time applications. Publications & Awards: Over 200 publications in top-tier journals and conferences, including IEEE Transactions and SPIE. Notable awards include the Chester Sall Best Paper Award and multiple Best Paper Awards from IEEE journals. His contributions to video coding standards have been adopted in global specifications like MPEG and ITU-T. Grants & Labs: Involved in major research projects on HEVC extensions, 3D video coding, and low-delay applications. Collaborates with industry partners and academic institutions globally. His team at HHI develops reference software and test models for emerging standards.

• Computer Networking
• Software-Defined Networking (SDN)
• Distributed Systems
• +3 more

Mario Baldi is a researcher affiliated with the Polytechnic University of Turin, Italy , with significant contributions to computer networking , distributed systems , and software-defined networking . Key research themes: network function virtualization , programmable dataplanes , time-driven scheduling , and traffic analysis . Recent work focuses on RDMA-enabled compute offloading (2023) and DNN inference in network data planes (2023). Longstanding expertise in multicast routing , voice/data packet efficiency , and XML-based protocol parsing (2000–2006). Collaboration network includes Yoram Ofek , Fulvio Risso , and Han Hee Song , with 99+ publications spanning 1994–2023.

• High-Performance Computing
• Parallel Programming
• Domain-Specific Languages
• +3 more

Wenguang Chen is a researcher affiliated with Tsinghua University and Pengcheng Laboratory , specializing in computer science and high-performance computing . His work bridges theoretical advancements with practical applications in domain-specific languages , parallel programming , and machine learning . Research Interests include: Development of modular DSLs for numerical methods (e.g., Mat2Stencil) Performance optimization in distributed and parallel systems Compiler frameworks for privacy-preserving AI (e.g., FHE-based neural network inference) Graph algorithms scaling to trillion-edge datasets Applications of Rust in memory-safe pointer analysis Recent Publications span 2014–2025, focusing on: Parallelization strategies for supercomputing Compiler automation tools Extreme-scale data processing Performance variance diagnosis in production environments

Brigitte PientkaView profile

Professor

• Programming Languages
• Verification
• Type Theory
• +6 more

Brigitte Pientka is a Professor at McGill University's School of Computer Science , where she leads the Computation and Logic group . She earned her PhD from Carnegie Mellon University in 2003 and previously studied at the University of Edinburgh and Technical University of Darmstadt. Education PhD in Computer Science, Carnegie Mellon University (2003) University of Edinburgh Technical University of Darmstadt Research Interests Her work focuses on the theoretical and practical foundations for building reliable software systems, combining logic, type theory, and verification with system-building. Key areas include: Type Theory and Dependent Types Logical Frameworks and Mechanized Metatheory Session-Typed Concurrency and Linear Logic Metaprogramming and Contextual Type Systems Theorem Proving and Formal Verification Functional Programming and Language-Based Security Professional Roles She has served as: PC Chair for ICFP'24 and CPP'24 General Chair for POPL'20 Executive Editor of Logical Methods in Computer Science Steering Committee Member for LICS, POPL, and ESOP Scientific Awards Dr. Pientka has received: Test of Time Award at PPDP'18 Humboldt Fellowship for research at MPI-SWS, Germany Labs & Teams She actively develops the Beluga programming language , a tool for mechanizing meta-theory proofs and type-driven program manipulation.

Arnulf DeppermannView profile

Professor

• Conversation Analysis
• Interactional Linguistics
• Multimodal Interaction
• +4 more

Arnulf Deppermann is Professor of German Linguistics at the University of Mannheim and Head of the Pragmatics Department at the Leibniz Institute for German Language. He specializes in conversation analysis, multimodal interaction, and the study of spoken German. His research examines how meaning, understanding, and social action are interactively constituted through linguistic and embodied resources. Research interests focus on interactional semantics, multimodal resource coordination, and the temporal organization of social interaction. Key areas include action ascription, the relationship between grammar and interaction, and methodological advances in conversation analysis. His work spans diverse contexts from psychotherapy to theater rehearsals. Recent publications demonstrate strong emphasis on: Interactional semantics and meaning negotiation Multimodal coordination in institutional settings Therapeutic communication patterns German linguistics and conversation analytic methodology Editorial roles include founding co-editor of the online journal 'Gesprächsforschung'. He organizes major conferences including the International Conference on Conversation Analysis (ICCA).

• Electric Vehicles
• Fuzzy Logic
• Blockchain
• +5 more

Shahid Hussain is an Associate Professor at King Abdullah University of Science and Technology (KAUST) in the Computer, Electrical and Mathematical Sciences and Engineering Division. His research spans multiple domains including electric vehicle infrastructure, blockchain technology, and intelligent systems for IoT applications, with a focus on practical implementations of computational intelligence techniques. Dr. Hussain's research interests include: Electric Vehicles and Smart Grid Integration Fuzzy Logic and Intelligent Decision Systems Blockchain Applications for Security and Privacy Machine Learning for IoT and Healthcare Applications Energy Management Systems Smart City Infrastructure Development His recent publications demonstrate a strong focus on applying hybrid computational approaches to solve complex engineering problems, particularly in transportation systems and healthcare applications. Dr. Hussain has published extensively in IEEE journals, with particular emphasis on innovative approaches to electric vehicle charging infrastructure, secure IoT systems, and blockchain-enabled solutions for real-world challenges. Dr. Hussain maintains active collaborations with researchers globally, particularly with Reyazur Rashid Irshad, Young-Chon Kim, and Subhasis Thakur. His work bridges theoretical advancements with practical implementations, as evidenced by his research on fuzzy integer linear programming for EV charging stations and blockchain-enabled security frameworks for medical IoT systems.

Peter BuxmannView profile

Professor

• Artificial Intelligence
• Digital Transformation
• Business Informatics
• +4 more

Prof. Dr. Peter Buxmann is a full professor of business informatics at the Technical University of Darmstadt, holding the Chair in this field. He is an internationally renowned speaker, senior business advisor, and podcaster for the Frankfurter Allgemeine Zeitung. His roles include advisory board memberships for institutions like the Weizenbaum Institute for the Networked Society and Eckelmann AG. His research focuses on AI applications, digital transformation, and future work paradigms, with over 300 publications in top journals and conferences. Education: PhD and habilitation from University of Frankfurt, including a research stay at UC Berkeley's Haas School of Business. Previous positions: Professor at Technical University of Freiberg (2000-2004). Research Interests: Artificial Intelligence (applications in business, healthcare, and ethics), Digital Transformation (organizational and societal impacts), Data-Driven Business Models (platform economics, innovation ecosystems), and Future of Work (human-robot collaboration, agile principles). Keynote Topics: Generative AI and its industry applications Strategic digital transformation frameworks Ethical AI governance Labs/Teams: Leads the HIGHEST innovation center at TU Darmstadt and collaborates with TechQuartier Frankfurt. Founded multiple startups focusing on AI solutions.

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.

Sergio MontenegroView profile

Professor

• Aerospace Information Technology
• Dependable Computing
• Distributed Systems
• +6 more

Prof. Dr.-Ing. Sergio Montenegro is a Professor of Aerospace Information Technology at Julius-Maximilians-University Würzburg, where he leads the Chair of Computer Science VIII. His academic journey includes a Bachelor's in Computer Science from Universidad del Valle de Guatemala (1978-1982), a Diploma from Technische Universität Berlin (1983-1985), and a Dr.-Ing. from TU Berlin (1989). Prior to joining academia, he held positions as a software developer (1979-1982), research coordinator at Fraunhofer Gesellschaft (1985-2007), and Head of Department at DLR (2007-2010). His research focuses on dependable distributed systems for aerospace applications, including satellite networks, real-time operating systems (RODOS), UAV swarm control, fault-tolerant architectures, and space mission software. Key projects span satellite formation flight (TET, AsteroidFinder), solar sail missions, distributed avionics (VIDANA), and medical IoT systems. Recent publications (2018) demonstrate strong emphasis on distributed spacecraft systems, UAV navigation, fault tolerance, and software engineering for space applications. Trends include miniaturized satellite technologies, decentralized control algorithms, real-time OS verification, and Java-based space systems. He leads research in distributed computing networks and UAV laboratories, supervising projects like VaMEx-LaOLA (Mars exploration) and ultra-wideband positioning systems. Though no awards are documented, he has coordinated over 100 projects including ESA and DLR missions.

• Artificial Intelligence
• Data Analytics
• Machine Learning
• +5 more

Prof. Dr. Christian Schulze is a Professor in the Faculty of Mathematics, Natural Sciences and Data Science at the Technical University of Central Hesse (THM), affiliated with the Competence Center for Information Technology (KITE). His research focuses on Artificial Intelligence and Data Analytics for real-world industry applications. Key projects demonstrate expertise in: LEGADA (2022-2024): Developing AI-driven lead generation systems for B2B sales using self-learning algorithms, funded with €386,330 by Hesse's 'Distr@l' program TLDia (2019-2021): Automating medical diagnosis coding through transfer learning in collaboration with Goethe University Frankfurt PAROT (2016-2018): Creating SaaS recommendation engines for e-commerce personalization ADOMIS (2015-2017): Integrating heterogeneous financial data sources for statistical analysis Prof. Schulze secures significant research funding from the State of Hesse (LOEWE-3 and 'Distr@l' programs) and maintains active industry partnerships with Minds-Medical GmbH, Dastani Consulting GmbH, and Content Software GmbH. His work bridges academic research with practical business solutions in AI deployment.

Tim ChenView profile

Senior Lecturer

• Human-Computer Interaction
• Computer Graphics
• Artificial Intelligence
• +4 more

Dr. Tim Chen is a Senior Lecturer at the School of Computer Science, University of Adelaide, with research focused on augmenting human capabilities through human-centered AI systems. He leads interdisciplinary teams in projects like the Augmenting Ability CRC and the IMAGENDO Project , which received the Eureka Prize and NHMRC Idea Grant. Affiliation: University of Adelaide Collaborators: Prof. Patrick Baudisch (HPI), Prof. Takeo Igarashi (U of Tokyo), Prof CT Lin (UTS), Dr. Li-Yi Wei (Adobe Research) Research Interests: Human-AI interaction paradigms ( AI as Assistant, Collaborator, Oracle ), virtual reality training, contrastive learning, and data visualization. His work bridges AI/ML, computer graphics, and human-computer interaction to empower knowledge workers. Recent Achievements: Two CHI 2025 papers on VR learning, SIGGRAPH 2025 jury committee role, and ARC DP analysis website development. Funding includes NHMRC (~$2M) and AEA Ignite grants. Scientific Awards: Eureka Prize-winning leadership NHMRC Idea Project Grant AEA Ignite Grant (~$500k)

Scott GrissomView profile

Professor

• Computer Science Education
• Active Learning
• Collaborative Learning
• +7 more

Scott Grissom is a Professor at Grand Valley State University’s College of Engineering and Computing , specifically within the Computer Science and Information Systems Department . His work focuses on transforming traditional lecture-based computer science education into interactive, student-centered environments through pedagogies like peer instruction , active learning , and collaborative learning . He has led multi-institutional studies on instructional practices and contributed to national discussions on evidence-based teaching methods in CS classrooms. Dr. Grissom’s research interests revolve around enhancing student engagement via algorithm visualization, digital libraries, and interactive pedagogical tools . His studies on peer instruction (e.g., “A Multi-institutional Study...”) and error analysis in recursive algorithms have significantly influenced CS2 curriculum design. He also explores the integration of mobile application development (e.g., iPhone curriculum adaptations) into traditional CS topics. His publications span journals like ACM Transactions on Computing Education and SIGCSE proceedings , with a focus on student-centered learning , instructional practices , and NSF grant opportunities . He has conducted workshops on funding competitive NSF proposals , emphasizing systematic strategies for converting ideas into successful grants. His collaborations include renowned figures like Renée McCauley , Laurie Murphy , and Leo Porter .