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Amal AhmedView profile

Professor

• Compiler Verification
• Language Interoperability
• Type Systems
• +7 more

Amal Ahmed is a Professor at Khoury College of Computer Sciences, Northeastern University, where she leads the SILC (Secure Interoperability, Languages, and Compilers) research group. She holds a Ph.D. in Computer Science from Princeton University. Her research focuses on programming languages, compilers, and software security, with emphasis on compiler verification, language interoperability, type systems, gradual typing, and secure compilation. She teaches courses including CS7470 (Seminar in PL: Logical Relations) and CS7400 (Intensive Principles of Programming Languages). Research Focus Dr. Ahmed's work bridges theoretical foundations and practical implementation, exploring: semantics and type systems for imperative/probabilistic languages; safe multi-language interoperability; compiler correctness; gradual typing; and provenance. Her recent publications demonstrate strong emphasis on secure compilation techniques, WebAssembly interoperability, and formal verification of compiler transformations. Professional Recognition NSF CAREER Award recipient Keynote speaker at major conferences including OOPSLA 2023, LICS 2022, and ESOP 2018 Program chair for POPL 2023 and OOPSLA 2022 Editorial board member for Journal of Functional Programming and Mathematical Structures in Computer Science Research Team & Students Dr. Ahmed leads an active research group with 7 PhD students and 1 postdoc. She has graduated 4 PhD students who now hold academic positions at University of Michigan, UBC, and Northeastern. Her mentorship extends to numerous MS students and undergraduates, with several receiving NSF GRFP awards.

Joachim FalkView profile

Researcher

• Electronic System Level Design
• Hardware-Software Co-Design
• Data-Flow Graphs
• +6 more

Joachim Falk is a researcher at the Department of Hardware-Software Co-Design (Computer Science 12) at Friedrich Alexander University Erlangen-Nuremberg. With over 20 years of experience since joining the department in 2004, he specializes in electronic system level design, dataflow programming, and hardware-software co-design, contributing significantly to the field through publications, teaching, and research leadership. His educational background includes: Doctorate degree (Dr.-Ing.) in Computer Science from FAU (2014) Diploma degree in electrical engineering (data processing) from Georg-Simon-Ohm University of Applied Science Nuremberg (2002) Falk's research focuses on Electronic System Level Design, Hardware/Software Code Generation for Data-Flow Graphs, Compiler Optimizations, and Parallel Architectures. His work bridges theoretical computer science with practical embedded systems implementation, particularly through the SystemC framework and his contributions to the SysteMoC language. He actively explores energy-efficient computing approaches for dataflow networks and embedded systems, with recent work emphasizing self-powering networks, clock and power gating techniques, and multi-reader buffer implementations for heterogeneous architectures. His recent publication trends reveal a consistent focus on optimizing dataflow networks for energy efficiency and performance. Key themes include self-powering dataflow networks, innovative clock and power management techniques, buffer management strategies for heterogeneous many-core systems, and invasive computing approaches. His research demonstrates a strong connection between theoretical models and practical implementation challenges in embedded systems design. Dr. Falk teaches courses including 'Entwicklung interaktiver eingebetteter Systeme' (Development of Interactive Embedded Systems) for Winter Semester 2024/2025 and 'SystemC' for Summer Semester 2024, supervising multiple theses on security modeling at the electronic system level and sleep/wake-up strategies for hardware implementations of dataflow networks. His research is conducted within the framework of projects like SysteMoC (representation of computational models in SystemC) and SystemCoDesigner (design space exploration for embedded systems).

Alexander CarôtView profile

Professor

• Networked Music Performance
• Real-Time Audio Streaming
• Telecommunication Systems
• +3 more

Prof. Dr. Alexander Carôt is a Professor of Media Informatics at Anhalt University of Applied Sciences, leading the Faculty of Computer Science and Languages as its Dean. His work bridges physics, computer science, and music, with a focus on networked music performance and low-latency audio systems. He developed the SoundJack software, enabling remote collaborative music-making, and contributed to projects like FAST-MUSIC and 5GUK trials for distributed music sessions. His research spans telemedicine applications, real-time streaming optimization, and quantum effects in telecommunications. Education: Ingenieursdiplom (2004) Doctorate in Engineering (2009) Research Interests: Prof. Carôt explores interdisciplinary fields combining technology and music. Key areas include: Real-time audio networking for live performances 5G-enabled tele-music systems Medical tele-rehabilitation using music feedback Low-latency multimedia streaming protocols Grants & Projects: Led initiatives like the FAST-MUSIC project and 5G trials, focusing on distributed music collaboration. Active in EU-funded research on telematic systems and networked audio. Labs/Teams: Directs the SoundJack development team and collaborates with industry partners on 5G infrastructure for arts and healthcare.

Daniel LohmannView profile

Professor

• Operating system construction
• Embedded systems
• System software for dependable real-time systems
• +6 more

Daniel Lohmann is a Full Professor and Fachgebietsleiter (Head of Department) at the Department of Operating Systems and Middleware within the College of Engineering and Computer Science at Leibniz Universität Hannover . His research focuses on operating system construction , embedded systems , and dependable real-time systems , with a strong emphasis on generative approaches , software product lines , and hardware-RTOS co-design .

• Multi-stable Perception
• Perceptual Inference
• Cognitive Neuroscience
• +7 more

Dr. Alexander Pastukhov is an Assistant Professor at the Department of General Psychology and Methodology, University of Bamberg. His research focuses on multi-stable perception , perceptual inference , and the interplay between attention and microsaccades . He develops open-source tools like saccadr , eyelinkReader , and bistablehistory for analyzing eye movements and perceptual dynamics. Research Interests : Multi-level mechanisms of perception and memory History effects in bistable perception Attentional allocation via microsaccade analysis Bayesian modeling of perceptual decision-making Scientific Awards : 2013 'Best of the Year' award from the Psychonomic Society for work on sensory memory and adaptation in structure-from-motion displays His recent software development includes probabilistic programming tools for 2D/3D transformations and eye-tracking analysis. He also contributes to applied data analysis teaching materials using R and Python. Collaborative projects with industry partners (e.g., BMW) and institutions (e.g., Fraunhofer, Orange Labs) highlight his research transfer efforts.

Jiachi ChenView profile

Associate Professor

• Software Security
• Program Analysis
• Smart Contracts
• +4 more

Jiachi Chen is an Associate Professor at the School of Software Engineering, Sun Yat-sen University, China. He received his Ph.D. from Monash University, Australia, and has established himself as a leading researcher in blockchain security and Web3 technologies. His educational background includes: Ph.D. in Information Technology from Monash University (2019-2022) Research Assistant at Hong Kong Polytechnic University (2016-2018) Dr. Chen's research spans software security, program analysis, smart contracts, and Web3 technologies. His work bridges traditional software engineering with blockchain, focusing on security vulnerabilities, large language models for code generation, and empirical studies of smart contract development. Recent publications show increasing integration of AI techniques to address blockchain security challenges. His publication trends reveal deep expertise in smart contract security, with numerous papers on vulnerability detection and code analysis. Recent work has expanded into LLM applications for software engineering, particularly examining code generation quality and security implications in Web3 contexts. Dr. Chen has received significant recognition: Multiple ACM SIGSOFT Distinguished Paper Awards (ICSE 2025, ISSTA 2024, Internetware 2024) Best Paper Award at IEEE CSCloud/EdgeCom 2023 Best Paper Award at IEEE INFOCOM 2018 Best Paper Award at ISPEC 2017 He serves on program committees for top software engineering conferences (ICSE, FSE, ASE, ISSTA) and is actively recruiting students in Web3/Blockchain/Smart Contract research areas. His work appears consistently in premier venues including IEEE Transactions on Software Engineering and ACM conferences, demonstrating sustained research impact.

Xiang ChenView profile

Associate Professor

• Empirical Software Engineering
• Mining Software Repositories
• Software Testing and Maintenance
• +5 more

Xiang Chen is an Associate Professor at the Department of Software Engineering, School of Artificial Intelligence and Computer Science, Nantong University, China. He received his B.Sc. degree from Xi'an Jiaotong University in 2002 and his M.Sc. and Ph.D. degrees in computer software and theory from Nanjing University in 2008 and 2011 respectively. He is an editorial board member of Information and Software Technology and serves as a program committee member for prestigious conferences including FSE 2026 and ASE 2025. Chen is also a senior member of the China Computer Federation (CCF) and active in various academic committees. Chen's research focuses on empirical software engineering, mining software repositories, and software testing and maintenance, with particular emphasis on applying AI techniques to software engineering problems. His work spans large language models for software engineering, security vulnerability analysis, code change representation, and regression testing. He has published over 110 papers in top-tier journals and conferences including IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology. His recent publications demonstrate a strong trend toward integrating AI techniques, particularly large language models, with traditional software engineering practices. The research spans code generation evaluation, deep learning framework testing, vulnerability detection, and automated program repair, showing a consistent focus on improving software quality through innovative testing and analysis techniques. ACM SIGSOFT Distinguished Paper Award (ICSE 2021) ACM SIGSOFT Distinguished Paper Award (ICPC 2023) Top 1% CNKI Highly Cited Scholar (2024) Top 2% Scientist by Stanford University (2023-2025) NASAC 2019 Prototype Competition First Prize Chen has successfully advised numerous graduate and undergraduate students who have gone on to prestigious institutions including Nanjing University, Tsinghua University, and Zhejiang University. Many of his students have won national programming competitions and received scholarships. His research group, smartSE, actively works on projects funded by the Natural Science Foundation of China and various provincial research programs. Chen also serves as a reviewer for top journals including IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology.

Fang LiuView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence for Software Engineering
• Program Understanding
• +3 more

Fang Liu is an Assistant Professor at the School of Computer Science & Engineering, Beihang University, China. She has made significant contributions to the field of software engineering, particularly in the intersection of artificial intelligence and software development practices. Dr. Liu received her Ph.D. in Computer Science from Peking University (Sep. 2017 - Jul. 2022), supervised by Prof. Zhi Jin and Prof. Ge Li. Prior to that, she earned her B.S. in Computer Science from Chongqing University (Sep. 2013 - Jul. 2017). Her research interests focus on AI for Software Engineering, including program understanding and generation, program repair, and applications of Large Language Models to software development tasks. Dr. Liu teaches Compiler Technology as a compulsory course at Beihang University (Fall 2024) and Programming in Cangjie Language as an elective course (Spring 2025). Her recent publications demonstrate a clear trend toward leveraging large language models for various software engineering tasks, with a particular emphasis on code editing, program repair, and code translation. Her research spans both theoretical advancements in model architectures and practical applications to real-world software development challenges, with numerous publications in top-tier venues like ASE, ICSE, and FSE. Distinguished Paper Award at ICPC'20 for "A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning" Dr. Liu actively participates in the software engineering research community as a program committee member for major conferences including ASE, FSE, and ICSE. Her work has significant implications for improving developer productivity, software quality, and the integration of AI technologies into the software development lifecycle.

Yuning DingView profile

Researcher

• Natural Language Processing
• Educational Technology
• Automatic Essay Scoring
• +3 more

Yuning Ding is a PhD Student and Research Assistant in the junior research group 'EduNLP' at the Research Center CATALPA (Center of Advanced Technology for Assisted Learning and Predictive Analytics), FernUniversität in Hagen, since January 2022. Her work focuses on Natural Language Processing applications for educational technology, specifically developing systems for automatic essay scoring and generating formative feedback for learners and summative feedback for teachers. Her educational background includes: M.Sc. in Applied Cognitive and Media Science with Specialization in Cognition & Artificial Intelligence at University of Duisburg-Essen (2017-2019) B.Sc. in Applied Cognitive and Media Science at University of Duisburg-Essen (2014-2017) B.A. in Communications at University of International Relations, Beijing (2009-2013) Ding's research centers on leveraging NLP to enhance writing education through AI-driven assessment and feedback systems. Her work bridges computational linguistics and pedagogy, with particular emphasis on argument mining, cohesion analysis, and cross-lingual content scoring. She investigates how transformer models and multi-task learning can improve the reliability and educational value of automated writing evaluation, while addressing critical issues like fairness and adversarial vulnerability in scoring systems. Her research demonstrates how NLP can provide actionable insights for both students and educators in writing development. Analysis of her publication trends reveals a strategic progression from foundational work on content scoring and error analysis toward sophisticated integrated systems. Recent work emphasizes multimodal feedback generation, argument-cohesion integration, and cross-lingual transfer, with increasing focus on real-world implementation challenges including fairness, robustness, and user experience. Her research spans multiple languages and educational contexts, reflecting a commitment to globally applicable educational technology. Within CATALPA, Ding actively collaborates across disciplines through the center's vibrant knowledge-sharing culture. She participates in project presentations and colloquia that facilitate cross-pollination of ideas between computer science, linguistics, and educational theory. Her work on the DARIUS corpus and FEAT-writing system demonstrates tangible contributions to educational resource development and interactive learning environments.

• Operating Systems
• Distributed Systems
• Reliable Systems
• +4 more

Christian Dietrich is a Professor for Reliable Distributed Systems at Technical University of Braunschweig since 2024, where he heads the Reliable System Software research group within the Institute of Operating Systems and Computer Networks, part of the Faculty of Electrical Engineering, Information Technology, and Physics. He previously held positions at Leibniz Universität Hannover where he completed his distinguished doctoral research. Professor Dietrich's research focuses on operating systems, distributed systems, and reliable systems with particular expertise in memory management, real-time systems, and embedded systems. His work spans both theoretical foundations and practical implementations, with significant contributions to system software reliability and efficiency. His research interests include operating system design, memory management techniques, real-time computing, embedded systems, fault tolerance, and compiler optimizations for system software. Dietrich's recent publications demonstrate a strong focus on memory management innovations (particularly for persistent memory), virtualization techniques, and reliability mechanisms for distributed systems. His work frequently bridges the gap between theoretical computer science and practical system implementation, with many contributions finding their way into production systems. The research shows increasing emphasis on hardware-software co-design and adapting operating systems to modern hardware capabilities. His scientific achievements have been recognized with numerous prestigious awards: USENIX ATC Distinguished Artifact Award (2023) for LLFree USENIX ATC Best Paper Award (2017) for cHash RTAS Best Paper Award (2015) for dOSEK Multiple Outstanding Paper Awards at ECRTS, OSPERT, and ISORC Wissenschaftspreis Hannover (2020, awarded in 2024) Professor Dietrich has secured significant research funding including multiple DFG projects (DI 2840/1-1, DI 2840/2-1) and has been involved in collaborative projects with industry partners. His teaching portfolio includes advanced courses on operating systems, programming languages, and compilers, for which he received teaching excellence awards. He leads an active research group focused on reliable system software with ongoing projects including ParPerOS (Parallel Persistency OS) and ATLAS (Adaptable Thread-Level Address Spaces). The Reliable System Software group maintains strong connections with both academic and industrial partners, contributing to open-source projects and collaborating on cutting-edge research in system software reliability. The group's work has practical impact, with some findings leading to more than 100 accepted patches in the Linux mainline kernel.

Mario NolteView profile

Researcher

• Green Information Systems
• Conceptual Modeling
• Process Modeling
• +6 more

Mario Nolte, M.Sc. is a research associate and external doctoral student at the University of Duisburg-Essen , affiliated with the Research Group for Business Information Systems and Business Modeling . His work bridges conceptual modeling, enterprise modeling, and sustainability research, focusing on transparency in digital systems and algorithmic fairness. Alumni of University of Münster (B.Sc. in Business Information Systems) and Tel Aviv University (MBA exchange). Former coordinator of the international academic network IS:link , funded by Stiftung Mercator . Research interests include: Green Information Systems for sustainability Conceptual, process, and enterprise modeling Philosophy of science applied to digital systems Algorithmic transparency and data privacy Key publication trends highlight multi-level modeling architectures, domain-specific languages for life-cycle assessment, and frameworks for algorithmic fairness in public administration. His supervised theses explore topics like Explainable AI , Decentralized Apps Usability , and Privacy Paradox in Digital Systems . He actively contributes to academic discourse through collaborations at conferences like European Conference on Information Systems and International Workshop on Multi-Level Modeling , while advising students on digital transformation, data governance, and ethical implications of AI.

• Microarchitectural Side-Channel Attacks
• System Security
• CPU Security
• +6 more

Dr. Michael Schwarz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany, where he leads the RootSec research group. Since 2020 he has been investigating microarchitectural side-channel attacks, system security, CPU security, transient-execution vulnerabilities, and corresponding defense mechanisms. He previously served as a post-doctoral researcher (2019–2020) and PhD student (2016–2019) at Graz University of Technology, after earning two master’s degrees in computer science and software engineering with a security focus. Education PhD, Graz University of Technology, 2019 — “Software-based Side-Channel Attacks and Defenses in Restricted Environments” (Advisor: Daniel Gruss) MSc, Computer Science, Graz University of Technology MSc, Software Engineering, Graz University of Technology Research Interests Dr. Schwarz’s work centers on microarchitectural side-channel attacks and system security , spanning: CPU security and transient-execution vulnerabilities (Meltdown, Spectre, Fallout, LVI, ZombieLoad, ÆPIC Leak, CacheWarp) Detection, mitigation, and formal guarantees against microarchitectural leakage Hardware–software co-design for secure operating systems and trusted execution environments Reverse engineering of CPU internals, cache architectures, and prefetchers Browser and web-platform security, including sandbox bypasses and fingerprinting Compiler security and constant-time programming enforcement Scientific Awards & Recognition 2022 Busy Beaver Award for “Foundations of Cybersecurity II” 2021 Busy Beaver Award for “Side-Channel Attacks and Defenses” 2020 EuroSys Roger Needham PhD Award 2019 IEEE S&P Distinguished Paper Award (Spectre) 2019 NSA Best Scientific Cybersecurity Paper Competition Honorable Mention (Meltdown) 2019 Open Exploit Award (Meltdown & Spectre) 2018 CSAW Best Paper Award (Meltdown) 2018 Pwnie Awards: Best Privilege Escalation Bug & Most Innovative Research 2023 USENIX Security Noteworthy Reviewer Award 2023 WOOT Best Paper Award (CustomProcessingUnit) 2022 Pwnie Award for Best Desktop Bug (ÆPIC) 2022 AI 2000 Security & Privacy Honorable Mention 2021 CCSW Best Paper Award & CSAW Best Paper 3rd Place Advising & Collaborations Dr. Schwarz currently mentors numerous PhD students and post-docs within the RootSec group. His collaborations extend across CISPA, Graz University of Technology, and an international network visible through extensive multi-author publications at top-tier venues including USENIX Security, IEEE S&P, ACM CCS, NDSS, ESORICS, DIMVA, ASPLOS, WWW, FC, and specialized microarchitecture security conferences. Labs & Teams He heads the RootSec Research Group at CISPA, whose core mission is to uncover, analyze, and mitigate microarchitectural and system-level security threats. The group maintains close links with industry partners, open-source communities, and policy makers to ensure that research findings are rapidly translated into practical defenses deployed in modern operating systems and processors.

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +7 more

Prof. Dr. Mario Fritz is a Professor at Saarland University and faculty member at CISPA Helmholtz Center for Information Security. He serves as a Fellow at the European Laboratory for Learning and Intelligent Systems (ELLIS). His research focuses on Trustworthy Information Processing at the intersection of AI & Machine Learning with Security & Privacy. Mario Fritz leads numerous significant research initiatives including the European Large Open Multi-Modal Foundation Models for Robust Generalization (ELLIOT), European Lighthouse on Secure and Safe AI (ELSA), and multiple projects on privacy-preserving AI applications in healthcare. His work spans security and privacy aspects of large language models, foundation models, and medical AI applications. He has established himself as a leading researcher in trustworthy AI through his extensive publication record and leadership in major collaborative projects. His recent research (2025 publications) demonstrates a strong focus on the security and safety challenges of large language models, including model stealing attacks, causal reasoning capabilities, sampling methods, and privacy risks. His work bridges theoretical foundations with practical applications across multiple domains, particularly in healthcare and cybersecurity. Mario Fritz actively mentors PhD students and Post-Docs, seeking new researchers to join his group. He has been involved in numerous grants and collaborative projects, including those funded by BMBF and the Helmholtz Association, demonstrating his ability to secure substantial research funding and lead large interdisciplinary teams.

Pierre MaierView profile

Researcher

• Multi-Level Software Languages
• Software Language Engineering
• Conceptual Modeling
• +4 more

Pierre Maier is a Researcher at the University of Duisburg-Essen , affiliated with the Faculty of Computer Science and the Information Systems and Integrated Information Systems department. His research explores the intersection of information systems and software languages, with a focus on multi-level modeling (MLM), natural language generation (NLG), and the application of large language models (LLMs) in organizational problem-solving. Maier's work investigates automation techniques to enhance the usability of multi-level software languages and addresses challenges in transitioning from traditional two-level languages. He supervises theses on topics like Machine Learning-Assisted Domain Modeling , LLM-Driven Semantic Matching , and Flexible Modeling , reflecting his interest in bridging conceptual modeling with AI advancements. His recent publications analyze the integration of generative language models with UML, the evolution of low-code platforms, and the role of multi-level modeling in improving software artifacts. Maier teaches courses in Object-Oriented Modeling , Robotic Process Automation , and Data Integration , combining technical rigor with practical applications for enterprise environments.

• Computational Biology
• Systems Biology
• Mathematical Modeling
• +2 more

Prof. Dr. Jan Hasenauer holds the Hertz Professorship at the University of Bonn since August 2024, with dual affiliations to the Life & Medical Sciences Institute (LIMES) and the Hausdorff Center for Mathematics (HCM) . His work bridges computational biology , systems biology , and mathematical modeling , focusing on methods for integrating heterogeneous biological data , model-based hypothesis testing , and experimental design optimization . Develops computational frameworks for analyzing complex biological systems Active in multi-scale modeling of cellular and organismal processes Key contributor to universal differential equations and Bayesian inference tools Research Trends from 2024-2025 publications show expertise in: Integrating clinical data with multi-omics for disease modeling Designing privacy-preserving analysis tools for clinical trials Developing software standards like PEtab for reproducible modeling Applying machine learning to epidemiological forecasting The group maintains strong connections with the Transdisciplinary Research Area 'Modelling' and collaborates across mathematics , life sciences , and public health . Recent ERC Starting Grant funding (€1.5M) supports their innovative computational approaches.