Prof. Dr.-Ing. Hakan Kayal serves as University Professor for Aerospace Engineering at the University of Würzburg, holding the Chair of Computer Science VIII (Space Technology) and chairing the Interdisciplinary Research Center for Extraterrestrial Studies (IFEX). His leadership bridges computer science and space systems engineering within the university's Institute of Computer Science. Research focuses on three synergistic domains: nanosatellite development for extraterrestrial missions (including the SONATE-2 6U platform demonstrating AI-driven onboard processing), scientific investigation of Unidentified Anomalous Phenomena (UAP) through the university's collaboration with the Federal Aviation Office, and spacecraft autonomy systems enabling higher mission independence. Current projects include the NEAlight mission (extended to develop the Apophis Interceptor concept for the 2029 asteroid flyby), VaMEx3-MarsSymphony for Mars exploration, and JMU Space Observatory initiatives. Publication trends reveal strong emphasis on asteroid defense strategies (particularly for Apophis), CubeSat-based UAP detection methodologies, and real-time AI processing in constrained space environments. His team actively engages students through ADS-B tracking, Meteosat App development, and Moon Base 2030 projects, while recent recognition includes co-authoring a landmark UAP review in Progress in Aerospace Sciences with 33 international scientists.
Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.
Krishna Gummadi is a Scientific Director and Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Germany, where he leads the Networked Systems Research Group. He also holds a professorship at the University of Saarland, demonstrating his dual commitment to research and academic instruction in computer science. His educational background includes: Ph.D. in Computer Science and Engineering from the University of Washington (2005) B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Madras (2000) Gummadi's research spans networked and distributed computer systems with a current focus on social computing systems. His work addresses critical challenges in algorithmic fairness, privacy in social media, trustworthiness of online identities, and information dissemination in social networks. He approaches these problems through interdisciplinary methods combining user-centric studies, data-centric analysis, and systems-centric design to create practical solutions that enhance fairness, transparency, and user control in online platforms. His methodology integrates large-scale observational studies, computational modeling, and system implementation to tackle complex human-computer interaction challenges at societal scale. His recent publications reveal a strong emphasis on fairness in algorithmic decision making, with significant contributions to quantifying and addressing discrimination in machine learning systems. His work bridges computer science, social science, and ethics, creating frameworks for fair classification, understanding media bias, and developing privacy-preserving techniques that maintain functionality while protecting user data. The research demonstrates a progression from technical system design to addressing societal implications of computing systems. Among his notable scientific achievements: ERC Advanced Grant in 2017 for 'Foundations for Fair Social Computing' Test of Time Awards at ACM SIGCOMM and AAAI ICWSM Casper Bowden Privacy Enhancing Technologies (PET) and CNIL-INRIA Privacy Runners-Up Awards IW3C2 WWW Best Paper Honorable Mention Multiple Best Paper awards across prestigious conferences Gummadi has advised numerous PhD students and postdoctoral researchers who have gone on to prominent positions in academia and industry. His ERC Advanced Grant has supported extensive research into fair social computing, while his leadership in major conferences (including serving as General Chair for ICWSM 2016 and Program Chair for WWW 2015) has shaped research directions in the field. His teaching portfolio includes courses on Distributed Systems, Human-Centered Machine Learning, and Social Media Analysis. He leads the Networked Systems Research Group at MPI-SWS, which has developed several publicly available systems including tools for fair classification, privacy risk assessment, trust evaluation in social media, and information diet management. The group's work bridges theoretical advances with practical implementations that address real-world challenges in social computing, with numerous software releases and datasets made available to the research community.
Cuiyun Gao is a Full Professor and PhD Supervisor at the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen. She has established herself as a prominent researcher in the intersection of artificial intelligence and software engineering. Her educational background includes a PhD from the Chinese University of Hong Kong (completed in 2018), followed by postdoctoral work at CUHK and a Research Fellowship at Nanyang Technological University. She also had a visiting period at University College London supervised by Prof. Mark Harman and Prof. Federica Sarro. Dr. Gao's research primarily focuses on Software Repository Mining, Natural Language Processing, Code Analysis, Large Language Models, Source Code Understanding, User Review Analysis, Vulnerability Detection, and Mobile Advertising Analysis . Her work bridges the gap between traditional software engineering practices and modern AI techniques, particularly in the context of code intelligence and software maintenance. Her recent publications (2024-2025) demonstrate a strong emphasis on Large Language Models for code-related tasks, including code generation, optimization, vulnerability detection, and software engineering applications. Her research shows a clear trend toward addressing practical challenges in integrating LLMs into the software development lifecycle while maintaining code quality and security. Scientific Awards: Distinguished Paper Award at ASE 2023 Best Paper Award of the Track at ICSE 2024 Distinguished Paper Award at ICSE 2024 Dr. Gao actively supervises multiple PhD and Master's students, contributing to the next generation of software engineering researchers. She has served on numerous conference committees including FSE, ISSTA, ICSE, ASE, and SANER. Her research has received significant attention in the software engineering community, with multiple papers published in top-tier venues like FSE, ICSE, ASE, and TSE. Her lab appears to be actively engaged in both theoretical research and practical applications, particularly in the context of WeChat and other industry collaborations, demonstrating strong industry-academia connections.
Xin Peng is a Professor and Deputy Dean at the School of Computer Science, Fudan University, China. He leads the CodeWisdom research team focusing on intelligent software engineering techniques for development, maintenance, and operation of software systems. His educational background includes a PhD in Computer Science (2001-2006) and Bachelor's degree in Computer Science (1997-2001), both from Fudan University. He progressed through the academic ranks from Assistant Professor (2006-2010) to Associate Professor (2010-2015) and finally to Professor (2015-present). Professor Peng's research interests span Software Analytics, Intelligent Software Development, Microservice systems, and AIOps. His work leverages AI technologies including deep learning and knowledge graphs to develop intelligent software engineering techniques. A significant portion of his recent work focuses on applying Large Language Models to various software engineering tasks, including vulnerability detection, API usage analysis, and test automation. His publication record shows a clear trend toward increasingly sophisticated applications of AI in software engineering, with recent work heavily featuring LLMs for tasks ranging from vulnerability patch porting to resource leak detection. The research spans multiple domains including microservice systems, automotive software, and Web of Things security. Best Paper Award of ICSM 2011 ACM SIGSOFT Distinguished Paper Award of ASE 2018 and 2021 IEEE TCSE Distinguished Paper Award of ICSME 2018, 2019, and 2020 IEEE Transactions on Software Engineering Best Paper award for 2018 Professor Peng serves in numerous leadership roles including Deputy Director of CCF Technical Committee on Software Engineering, Co-Editor-in-Chief of Journal of Software: Evolution and Process, and Associate Editor for ACM Transactions on Software Engineering and Methodology. He has been actively involved in program committees for major software engineering conferences including ICSE, ASE, ESEC/FSE, and ICSME. He leads the CodeWisdom research team at Fudan University, which has developed several benchmark systems including TrainTicket for microservice research. The team's work bridges academic research with industrial applications, particularly in microservice systems analysis and intelligent software development tools.
Paul R. Genssler is a Dr.-Ing. researcher at the Chair of AI Processor Design (AI-Pro) within the Technical University of Munich (TUM), actively advancing hardware solutions for artificial intelligence under Prof. Hussam Amrouch. His work bridges computer engineering and emerging technologies, focusing on overcoming fundamental limitations in conventional computing architectures through brain-inspired paradigms. His research spans critical domains in next-generation computing: Hyperdimensional Computing for robust pattern recognition and bioinformatics applications Neuromorphic and In-Memory Computing architectures for energy efficiency Reliability engineering for emerging memory technologies (FeFET, etc.) Quantum computing support systems including cryogenic embedded electronics Machine learning-driven transistor aging prediction and mitigation Analysis of his 15 most recent publications (2023-2024) reveals a dominant trend toward hyperdimensional computing as a unifying framework for addressing reliability challenges in emerging technologies. His work consistently integrates in-memory computing techniques to bypass von Neumann bottlenecks while targeting real-world applications like genome matching and unsupervised learning. A significant portion focuses on error-resilient implementations for unreliable nanoscale devices, demonstrating exceptional cross-stack expertise from transistor physics to algorithm design. As a core member of TUM's AI Processor Design group affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI), Genssler collaborates extensively on projects spanning cryogenic quantum control systems, FPGA-based AI resilience, and monolithic 3D integration. The team operates at the intersection of semiconductor physics, computer architecture, and machine learning, with strong industry connections evident through publications at DATE, ASP-DAC, and ICCAD.
Lionel C. Briand is a Professor of Software Engineering with shared appointments at the University of Luxembourg's SnT Centre for Security, Reliability, and Trust and the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a Canada Research Chair (Tier 1) in Intelligent Software Dependability and Compliance and serves as Director of Lero, Ireland's national software research center. His academic leadership spans over 25 years of collaborative research with industry partners across automotive, aerospace, energy, financial, and legal domains. Professor Briand's research focuses on software verification and validation, trustworthy AI systems, model-driven engineering, and empirical software engineering methodologies. His work bridges theoretical foundations with industrial applications, particularly in cyber-physical systems where machine learning components interact with safety-critical control systems. He has pioneered techniques for testing AI-enabled systems, GDPR compliance automation, and mutation analysis for space systems. His publication portfolio demonstrates consistent innovation in software testing, with recent emphasis on large language models for test generation, automated compliance checking, and safety analysis of deep neural networks. Key trends include black-box testing methodologies, metamorphic testing for security, and search-based approaches for DNN validation. IEEE Fellow and ACM Fellow IEEE Computer Society Harlan Mills Award (2012) ACM SIGSOFT Outstanding Research Award (2022) IEEE Reliability Society Engineer-of-the-Year Award (2013) ERC Advanced Grant recipient (2016) Fellow of the Academy of Science, Royal Society of Canada (2023) ICSE 2011 Most Influential Paper Award As Director of Lero and holder of a Canada Research Chair, Professor Briand leads major research initiatives including an ERC Advanced Grant on cyber-physical system modeling and testing. His industrial collaborations generate substantial grant funding, particularly in automotive safety validation and regulatory compliance automation. He mentors numerous researchers through his dual appointments and serves on program committees for top software engineering conferences. Professor Briand directs research activities at the SnT Centre's SVV department, focusing on software verification and validation. His team develops practical tools like MASS for space system mutation analysis and COREQQA for compliance requirements understanding, with strong industry adoption in automotive and aerospace sectors.
Jie M. Zhang is an Assistant Professor in the Department of Informatics at King's College London, specializing in the intersection of software engineering and artificial intelligence. Her research focuses on two main directions: AI for Software Engineering (leveraging AI technologies to automate software tasks) and Software Engineering for AI (applying SE principles to enhance AI system trustworthiness). Her educational background includes a PhD in Computer Science from Peking University, where she was supervised by Professors Lu Zhang and Dan Hao. Prior to joining King's College London, she was a Research Fellow at University College London working with Professor Mark Harman and Professor Federica Sarro. Dr. Zhang's research interests center on software testing, machine learning trustworthiness, fairness testing, bias mitigation in AI systems, and program analysis. Her work particularly examines how large language models can be utilized for code generation, test case creation, and program repair, while also developing techniques to detect and fix issues within AI models. Her recent publications demonstrate strong trends in evaluating and enhancing the trustworthiness of AI-generated code, with specific emphasis on fairness testing across various domains including autonomous driving systems, machine translation, and decision-making software. Her research increasingly focuses on the efficiency of generated code and detecting hallucinations in large language models. 2025 ACM Sigsoft Early Career Researcher Award for pioneering contributions to software engineering for AI IEEE TSE 2024 Best Paper Award for 'Stealthy Backdoor Attack for Code Models' FSE 2025 Distinguished Paper Award Royal Society International Exchange Grant recipient NMES Enterprise & Engagement Partnerships Fund recipient Dr. Zhang has served in numerous leadership roles across major software engineering conferences including as General Chair for AIware 2025, Area Chair for ASE 2025, and Steering Committee Member for ICST. She has advised multiple PhD students and received significant research funding for her work on LLMs and software engineering. Her research group collaborates with industry partners including Huawei and Facebook, and she leads projects such as ITEA GENIUS and ITEA GreenCode. She is actively involved with King's College London research hubs including the Trusted Autonomous Systems Hub, Security Hub, and Software Systems group, where her work contributes to developing trustworthy AI systems across multiple domains.
Daniel Müller-Gritschneder is an Adjunct Teaching Professor (Privatdozent) at the Technical University of Munich (TUM), affiliated with the Chair of Electronic Design Automation. He leads the 'Electronic System Level' research group, focusing on embedded systems, TinyML, virtual prototyping, and hardware resilience. He temporarily served as head of the Chair of Real-Time Systems (2019–2020) and holds a senior membership in IEEE. His research spans: TinyML : Optimizing neural network inference for microcontrollers. Virtual Prototyping : Fast simulation for embedded software development (e.g., ETISS simulator). Runtime Verification : Hardware monitoring for safety-critical systems. Fault Tolerance : Cross-layer resilience against soft errors. Design Automation : NoC synthesis and RISC-V toolchain optimization. His publications emphasize RISC-V-based systems, TinyML deployment, fault injection, and embedded AI. Recent works show trends toward compiler-assisted security, thermal management, and automated design-space exploration for edge devices. Awards: Best Paper Award (SiPS 2019) Habilitation Award (Bund der Freunde der TUM, 2019) 2nd Best Paper (SMACD'15) Best Paper nominations at DAC'07, DATE'10, Analog'10, NOCS'13 He advises researchers in the Electronic System Level group and contributes to EU projects (e.g., Scale4Edge). His lab develops tools like ETISS, MLonMCU, and Seal5 for RISC-V and TinyML ecosystems.
Ruben Martins is an Assistant Professor at Carnegie Mellon University's School of Computer Science and serves as the program director of the Master of Science in Computer Science (MSCS) . His research focuses on the intersection of constraint programming, program synthesis, analysis, and verification, with recent work aiming to make formal methods tools more accessible through automated reasoning. Ruben earned his Ph.D. with honors from the Technical University of Lisbon, Portugal (2013) , followed by postdoctoral research at the University of Oxford (2014-2015) and UT Austin (2015-2017) . Research Interests : Ruben's work bridges constraint programming and program synthesis , with applications in software verification , optimization , and automated reasoning . He has developed award-winning tools like Open-WBO , a modular MaxSAT solver that has won gold medals in international competitions. His publications span top-tier venues such as POPL , PLDI , FSE , SAT , and CP , often addressing real-world challenges from program analysis to network security. Scientific Awards include: Distinguished Paper Award at PLDI 2018 Distinguished Paper Award at FSE 2021 Distinguished Paper Award at SAT 2022 Gold medals for Open-WBO in MaxSAT competitions Teaching & Advising : Ruben mentors Ph.D., Master’s, and undergraduate students in research projects related to program synthesis, formal methods, and constraint solving. He teaches courses such as Bug Catching: Automated Program Verification and Advanced Topics in Logic: Automated Reasoning and Satisfiability , emphasizing hands-on experience with tools like Why3. His advising spans topics from AI-driven program repair to network protocol verification , fostering collaboration across disciplines.
Shin Yoo is a tenured Full Professor in the School of Computing at Korea Advanced Institute of Science and Technology (KAIST), where he leads the Computational Intelligence for Software Engineering (COINSE) research group. He received his PhD from King's College London in 2009 under the supervision of Prof. Mark Harman. Currently, he serves as the General Chair for ASE 2025, which will be held in Seoul, Korea. Professor Yoo earned his PhD in Computer Science from King's College London (2009), following an MSc in Software Engineering with Distinction from the same institution (2006). His academic journey includes positions as Tenured Associate Professor (2021-2025), Associate Professor (2018-2021), and Assistant Professor (2015-2018) at KAIST, as well as Lecturer and Research Associate positions at University College London and King's College London. His research focuses on the intersection of software engineering and artificial intelligence, particularly in search-based software engineering, software testing, automated debugging, SE4AI (Software Engineering for AI), and AI4SE (AI for Software Engineering). Professor Yoo's work bridges theoretical foundations with practical applications, developing innovative techniques for fault localization, test case generation, and debugging using machine learning and genetic programming approaches. His research has significant implications for improving software reliability and development efficiency in both traditional software systems and AI-powered applications. Professor Yoo's recent publications demonstrate a clear trend toward leveraging large language models and deep learning techniques for software engineering tasks. His work spans fault localization, automated debugging, GUI testing, and program analysis, with increasing focus on the challenges and opportunities presented by AI systems. His research shows a consistent evolution from traditional search-based software engineering to AI/ML-enhanced approaches, reflecting the broader trends in the field. ACM SIGEVO HUMIES Silver Medal (2017) for human competitive application of genetic programming to fault localization research IEEE TCSE Most Influential Paper Award (ICST 2024) for work on mutation-based fault localization Professor Yoo has supervised five PhD students to completion, with his former students now holding positions as assistant professors, post-doctoral researchers, and software engineers at institutions including Kyoungpook National University, Max-Planck Institute Security & Privacy, Università della Svizzera Italiana, Roku Korea, and NUS. He currently serves as an associate editor for the Journal of Empirical Software Engineering and ACM Transactions on Software Engineering and Methodology, and has held significant leadership roles in major software engineering conferences including Program Co-chair for SSBSE (2014), ICST (2018), and ICSE NIER track (2020), General Chair for SSBSE (2022), and Testing & Analysis Area Chair for ICSE (2024). As leader of the Computational Intelligence for Software Engineering (COINSE) group at KAIST, Professor Yoo directs research that combines computational intelligence techniques with software engineering challenges. The group focuses on developing novel approaches to software testing, debugging, and analysis using search-based and AI-driven methods. Their work spans both theoretical foundations and practical implementations, with strong connections to industry challenges and applications.
Prof. Dr.-Ing. Marc Reichenbach serves as the Chair of Integrated Systems at the Institute for Applied Microelectronics and Data Technology at the University of Rostock. His office is located at Albert-Einstein-Straße 26, 18059 Rostock, Room 102 (1st floor), with contact information including telephone (0381) 498 7270 and email marc.reichenbach@uni-rostock.de. Professor Reichenbach's research focuses on the intersection of hardware design and artificial intelligence, with particular expertise in memory technologies and computing architectures. His work spans several key areas: Development of specialized computer architectures for deep learning applications Advanced VLSI design and CPU architecture Emerging memory technologies, particularly RRAM (Resistive Random-Access Memory) FPGA-based acceleration systems Hardware implementations for neural networks and AI applications Analysis of Professor Reichenbach's recent publications (2023-2025) reveals a strong focus on memory computing technologies, particularly RRAM-based systems. His work demonstrates expertise across multiple dimensions of computer architecture including ASIC design, FPGA acceleration, and novel memory systems. The publications show a clear trajectory toward implementing AI and machine learning capabilities directly in hardware, with applications ranging from edge computing to satellite systems. A significant portion of his recent work addresses the challenges of implementing neural networks using emerging memory technologies, focusing on efficiency, reliability, and performance optimization. Professor Reichenbach teaches several advanced courses including: Computer architectures for deep learning applications Project seminar Embedded Systems Advanced VLSI Design (Advanced CPU Design) His research group appears to be actively engaged in several cutting-edge projects related to hardware acceleration for AI applications, memory computing, and embedded systems design. The group collaborates on projects involving digital twins for hardware systems, real-time operating systems for heterogeneous architectures, and specialized computing systems for various applications from medical devices to drone technology.
Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on developing software tools and methodologies to enhance programmer productivity and software quality, with expertise in Software Engineering, Programming Languages, and Formal Methods. Education: B.Tech from Indian Institute of Technology, Kanpur M.S. and Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Research Focus: Professor Sen pioneers automated testing techniques including concolic testing and DART (Directed Automated Random Testing). His work bridges formal methods with practical software development, emphasizing bug detection, program synthesis, and AI-driven software analysis tools. Recent innovations include machine learning approaches for code recommendation and fuzzing. Publication Trends: His recent publications (2019-2023) demonstrate strong emphasis on fuzzing techniques, program synthesis, and AI/ML applications in software engineering. Notable domains include smart contract security, automated testing, and developer tooling, with frequent collaborations in top-tier conferences. Awards and Honors: NSF CAREER Award (2008) Sloan Foundation Fellowship (2011) IFIP TC2 Manfred Paul Award (2010) Okawa Foundation Research Grant (2015) Multiple ACM SIGSOFT Distinguished Paper Awards UIUC Distinguished Alumni Educator Award (2014) Leadership: Active program committee member for premier conferences (PLDI, ICSE, ISSTA) and keynote speaker. His research is supported by NSF, Okawa Foundation, and Sloan Foundation.
Alfonso Emilio Gerevini is a prominent researcher in artificial intelligence with over 30 years of continuous academic contributions. His work spans theoretical foundations of automated planning to practical healthcare applications, with recent publications demonstrating significant impact in both traditional AI domains and emerging interdisciplinary areas. His research interests focus on automated planning systems , temporal reasoning , and multi-agent coordination , with recent expansion into healthcare applications using machine learning techniques. Gerevini has made fundamental contributions to planning algorithms, particularly in width-based search, case-based planning, and privacy-preserving multi-agent planning. His work on PDDL (Planning Domain Definition Language) has been influential in standardizing planning representations. Analysis of his 15 most recent publications reveals a strategic evolution from core planning research toward impactful healthcare applications, particularly during the COVID-19 pandemic. While maintaining his expertise in planning algorithms, he has successfully integrated machine learning techniques to address real-world medical challenges including radiology report analysis, prognosis prediction, and lab test interpretation. His work demonstrates exceptional versatility across both theoretical and applied domains of artificial intelligence. Gerevini maintains a robust collaborative network, primarily with Italian researchers including Ivan Serina, Alessandro Saetti, and Luca Putelli. His publications appear consistently in top-tier AI venues including Artificial Intelligence journal, Journal of Artificial Intelligence Research, and AAAI/ICAPS conferences. The collaborative patterns suggest he leads a significant research group focused on advancing planning systems while applying them to critical real-world problems.
Giles Reger is a Senior Lecturer in the Formal Methods Group of the School of Computer Science at the University of Manchester. He completed his BA in Computer Science at the University of Cambridge in 2009, followed by an MSc in Advanced Computer Science at the University of Manchester in 2010 (awarded Highest Achiever of the Year), and earned his PhD from the University of Manchester in 2014 with a thesis titled "Automata based monitoring and mining of execution traces". His research spans several key areas within computer science: Automated Theorem Proving (first-order) Saturation-based techniques Reasoning with theories and quantifiers Finite Model finding Collaborative and Concurrent proof attempts Runtime Monitoring/Verification Temporal specification languages Specification Mining/Inference Dr. Reger leads multiple EPSRC-funded research projects including SCorCH (Secure Code for Capability Hardware), CAPS (Collaborative Architectures for Proof Search), and QuTie (reasoning with Quantifiers and Theories). His work on the Vampire theorem prover and MarQ monitoring tool demonstrates his bridge between theoretical computer science and practical applications. Recent publications show strong focus on runtime verification, theorem proving, and program analysis with applications to security and performance monitoring. Notable awards: Highest Achiever of the Year Award for MSc studies Dr. Reger collaborates extensively with institutions including the University of Oxford, Arm, Amazon Web Services, and CERN (CMS Experiment). As Manchester lead on the SCorCH project, he develops formal analysis tools for security-aware hardware chips. His work on the VyPR framework enables developers to analyze Python program performance through temporal specification languages and monitoring algorithms.
