Robert Bruce Findler is a Professor of Computer Science at Northwestern University, specializing in programming languages and software engineering. He serves as a core developer of the Racket programming language and has contributed extensively to language design, macro systems, and gradual typing. Affiliation: Department of Electrical Engineering and Computer Science, McCormick School of Engineering Research Interests: Programming Languages (PL), Domain-Specific Languages (DSLs), Macro Systems, Gradual Typing, Contracts His work spans both theoretical and practical domains, including the development of Racket's Redex framework for semantics engineering and innovative approaches to contract systems in gradual typing. He has been actively involved in the PL community through committee memberships and program organization. Recent research focuses on macro systems (Rhombus), contract optimization (Collapsible Contracts), and language interoperability (The Functional, the Imperative, and the Sudoku). His GitHub contributions reflect ongoing development in Racket and related tools. Key Collaborations: Racket development ecosystem, PLDI/POPL/ICFP/SPLASH conferences Committee Roles: ICFP Programme Committee, REBLS Program Committee, POPLmark Retrospective Panelist
Falko Dressler is a Full Professor and Chair for Telecommunication Networks at the School of Electrical Engineering and Computer Science, Technische Universität Berlin. He holds a Ph.D. and M.Sc. in Computer Science from Friedrich-Alexander University of Erlangen-Nuremberg (1998-2003). His research focuses on next-generation wireless systems , distributed machine learning , edge computing , and applications in Internet of Things (IoT) , cyber-physical systems , and internet of bio-nano-things . Editorial roles: IEEE Trans. on Mobile Computing, Elsevier Computer Communications, IEEE/ACM Trans. on Networking Conference leadership: IEEE INFOCOM, ACM MobiSys, IEEE VNC Textbooks: Self-Organization in Sensor and Actor Networks (Wiley), Vehicular Networking (Cambridge) Recent publications highlight trends in Edge Computing Resilience and 6G Network Architecture , with a strong emphasis on Molecular Communication , Terahertz Band Synchronization , and Federated Learning in vehicular environments. Scientific contributions include multiple IEEE Fellow , ACM Fellow , and VDE ITG Prize 2023 recognitions. Advisory and professional activities include membership in the German National Academy of Science and Engineering (acatech), IEEE COMSOC Conference Council, and ACM SIGMOBILE Executive Committee. His work spans cooperative driving, ultra-low power sensor networks, and security in nano-communication systems.
Kihong Heo is an Associate Professor in the School of Computing and Graduate School of Information Security at KAIST (Korea Advanced Institute of Science and Technology) in South Korea. His academic career includes serving as an Assistant Professor at KAIST from 2017-2019 before being promoted to Associate Professor in 2020, following his postdoctoral research at the University of Pennsylvania. He earned both his Ph.D. and B.S. in Computer Science & Engineering from Seoul National University. Dr. Heo's research focuses on developing program reasoning systems for safe and reliable software, with specific interests in AI-based program analysis systems for detecting deep semantic software bugs, general-purpose program simplification systems for secure and efficient software, and scalable program synthesis systems for automatic software generation and repair. His work bridges the gap between programming languages, program analysis, and machine learning techniques to create next-generation programming systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning techniques with traditional program analysis methods, with significant contributions in compiler validation, software security, fault localization, and program debloating. His research has practical impact, with some of his work incorporated into Facebook's Infer static analyzer. ACM SIGSOFT Distinguished Paper Award, FSE 2025 Amazon Research Award, 2024 The Soo-Young Lee Teaching Innovation Award, KAIST, 2024 Prize for Excellence in Teaching, KAIST, 2024 Best Artifact Award, ICSE 2022 ACM SIGPLAN Distinguished Paper Award, PLDI 2019 ACM SIGSOFT Distinguished Paper Award, ICSE 2019 Dr. Heo actively mentors graduate students, currently advising several Ph.D. candidates including Yeonhee Ryou, Taeeun Kim, and Sujin Jang, as well as master's students. He has served on program committees for major software engineering and programming language conferences including PLDI, ICSE, POPL, and SPLASH, demonstrating his active role in the academic community. His laboratory, the Programming Systems Laboratory at KAIST, focuses on creating innovative programming systems that leverage both semantic-based program analysis and AI techniques.
Niklas Grabbe is a postdoctoral researcher at the Chair of Ergonomics, Technical University of Munich (TUM), where he leads the research group on "Automated Driving and Mobility Systems" and is establishing a new working group focused on "Modelling complex socio-technical systems" with emphasis on Resilience Engineering and the Functional Resonance Analysis Method (FRAM). His research centers on human factors in automated and teleoperated driving, mobility systems, and behavior modeling. He applies systems engineering principles to analyze safety and usability, particularly using FRAM to study performance variability and resilience in socio-technical systems. His work addresses critical challenges in urban automated driving, teleoperation, and multi-driver interactions through rigorous quantitative modeling and field studies. Analysis of Grabbe's publications reveals a dominant trend in applying FRAM to driving scenarios, with recurring themes of safety enhancement, human-automation interaction, and usability evaluation. His interdisciplinary work bridges transportation psychology, safety engineering, and human-computer interaction, consistently emphasizing systems thinking to solve complex problems in automated vehicle technology. Grabbe contributes to academic instruction through courses like "Modelling Complex Sociotechnical Systems" (Winter 2025/26). He operates within TUM's Chair of Ergonomics infrastructure, utilizing specialized labs including driving simulators and ergonomic mockups to support experimental research in human factors and automated systems.
Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.
Dr. Adrian Fazekas is a Lecturer at the Institute of Highway Engineering, RWTH Aachen University, and collaborates with the Federal Highway Research Institute (BASt). He holds a Dr.-Ing. in Computer Science from RWTH Aachen (2005–2011), specializing in Media Engineering. His professional trajectory includes roles as a Research Assistant at RWTH Aachen and industry experience as a Software Developer at Continental AG. Research interests focus on traffic data acquisition , microscopic traffic flow simulation , and intelligent transportation systems . Key projects include: DROVA: Drone-based traffic analysis for infrastructure optimization ESIMAS: Real-time tunnel safety management Digital Twin Road: Physical-informational mapping of future highways AUTUKAR: Automated tunnel monitoring systems His publications emphasize real-time traffic detection , safety analytics , and data-driven modeling , with recent work exploring thermal-camera nudging systems and weigh-in-motion accuracy. He actively contributes to the Research Association for Roads, Earth and Tunneling (SETAC). No awards or student advising roles are documented.
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.
Tse-Hsun (Peter) Chen is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He serves as Director of the SPEAR lab (Software Performance, Analysis, and Reliability lab), which focuses on improving the quality of large-scale software systems through research in log analysis and AIOps, software performance analysis, software testing, and mining software repositories. His research group maintains extensive collaborations with industry partners including ERA Environmental, Ericsson, Microsoft, and BlackBerry. Dr. Chen received his PhD and MSc in Computer Science from Queen's University and his BSc in Computer Science from the University of British Columbia. Dr. Chen's research addresses critical challenges in modern software engineering, including leveraging Large Language Models to assist developers with development, debugging, and maintenance; helping developers debug production systems by utilizing rich software data; providing optimization suggestions by analyzing user usage data; improving software quality assurances in DevOps environments; and mining software development history for useful developer suggestions. His work spans Software Engineering, Performance Engineering, DevOps & AIOps, Software Testing, and Mining Software Repositories, with a strong emphasis on practical applications that bridge academic research and industrial practice. His recent publications (2024-2025) demonstrate a pronounced shift toward integrating Large Language Models into various aspects of the software engineering lifecycle, particularly in log analysis, fault localization, code generation, and performance testing. This trend reflects the growing importance of AI in software engineering research and practice. Gina Cody Research award (2022) Ranked as one of the most active software engineering researchers worldwide by an independent study published in JSS Dr. Chen has successfully advised numerous PhD and Master's students, many of whom have secured prestigious academic positions. Several of his graduated PhD students now hold tenure-track assistant professor positions at institutions including York University, University of Alberta, DePaul University, and IIT Gandhinagar. His SPEAR lab has developed research tools that have been integrated into industrial practice for ensuring the quality of large-scale enterprise systems. The SPEAR lab, under Dr. Chen's leadership, has established itself as a leading research group in software engineering, with particular expertise in software performance analysis, log analysis, and AI applications for software engineering. The lab maintains strong industry connections and has produced numerous high-impact publications in top-tier software engineering venues including ICSE, FSE, ASE, and TSE.
Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He has been actively contributing to the software engineering research community since 2005, with significant publications and service to major conferences including ASE, ICSE, and ESEC/FSE. His extensive research portfolio spans multiple areas at the intersection of artificial intelligence and software engineering. Dr. Nguyen's research focuses on AI/ML4Code, encompassing Machine Learning, Natural Language Processing for Software Engineering and Software Security. His work specifically addresses Program Analysis, Software Evolution and Mining, Software Security, Software Maintenance, Mining Software Repositories, Version and Configuration Management, and Web Code Analysis and Security. His research has been consistently funded by multiple NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others dating back to CCLI-0737029. His recent publications demonstrate a strong trend toward leveraging large language models for various software engineering tasks including program analysis, bug detection, code completion, and automated program repair. The research spans both theoretical foundations and practical applications, with numerous papers accepted at top-tier conferences across multiple years. His scientific contributions have been recognized with several prestigious awards: ACM SIGSOFT Distinguished Paper Award at FSE 2024 IEEE Computer Society TCSE Distinguished Paper Award at SANER 2022 ACM SIGSOFT Distinguished Paper and ASE Best Paper Award at ASE 2014 ACM SIGSOFT Distinguished Paper Award at ASE 2012 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009 Dr. Nguyen has served in numerous leadership roles including Program Co-Chair for ICSE 2020 Demonstrations, Doctoral Symposium Co-Chair for ESEC/FSE 2021, NIER Track Chair for ASE 2020, and Tutorials Co-Chair for ASE 2024. He has received multiple NSF grants supporting his research in software analysis, mining, and security. His work with the Boa infrastructure for ultra-large-scale code mining has established significant infrastructure for the research community. His laboratory focuses on AI for software engineering, with particular emphasis on program analysis, software security, and mining software repositories. The research group develops techniques that bridge the gap between artificial intelligence and practical software engineering challenges, creating tools that are both theoretically sound and practically applicable to real-world software development.
Prof. Akash Kumar is a Professor at the Chair of Embedded Systems at Ruhr University Bochum, Germany. He previously held professorships at TU Dresden (2015–2024) and the National University of Singapore (NUS; 2011–2015). His research focuses on design automation of embedded systems, reliability optimization, and approximate computing, with a strong emphasis on FPGA and emerging technologies. He leads projects such as Lean-MICS (DFG-funded) and SecuREFET-II, addressing cross-layer reliability and secure circuits. Education: PhD in Multimedia Multiprocessor Systems from Eindhoven University of Technology (TUe) and NUS (2005–2009), Master of Technological Design (Embedded Systems) from NUS (2003–2004), and B.Eng (Computer Engineering) from NUS (1999–2002, First Class Honours). Research interests span embedded systems, reconfigurable architectures, and hardware-software co-design. His work includes optimizing energy efficiency, fault tolerance, and cross-layer approximation techniques. Recent publications highlight advancements in FPGA-based accelerators, machine learning optimizations, and mixed-criticality systems. Active in grants and leadership, Kumar is Principal Investigator on multiple DFG and industry-funded projects, emphasizing collaborative research in distributed computing and approximate architectures. His contributions bridge theory and practice, with applications in edge AI, IoT, and cybersecurity.
Arpan Gujarati is a Sessional Lecturer in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. He teaches graduate and undergraduate courses such as CPSC 538G (Distributed Systems), CPSC 416 (Operating Systems), and CPEN 432 (Real-Time System Design). He holds a PhD from the Max Planck Institute for Software Systems and TU Kaiserslautern, where he was supervised by Björn B. Brandenburg. PhD: Max Planck Institute for Software Systems & TU Kaiserslautern (2020) Undergraduate: Birla Institute of Technology and Science (BITS Pilani) Postdoctoral Researcher: MPI-SWS Research Associate: UBC Software Development Engineer: Citrix R&D, India His research focuses on real-time and distributed systems, with applications in cyber-physical systems, fault tolerance, and machine learning reliability. He investigates scheduling algorithms, reliability analysis, and the integration of learning-enabled components into safety-critical systems. His work combines theoretical analysis with practical system implementations, often involving real-world testbeds and open-source tools. His recent publications span top-tier venues including RTSS, OSDI, ECRTS, DSN, and Middleware, with a strong emphasis on performance predictability, resilience of ML systems, and real-time communication. His work frequently addresses challenges in timing guarantees, fault tolerance, and system reliability in both cloud and embedded environments. SIGBED Paul Caspi Memorial Dissertation Award Best Paper Award at RTSS 2022 Distinguished Artifact Award at OSDI 2020 Best Student Paper Award at Middleware 2017 Outstanding Paper Award at RTCSA 2025 He advises several PhD students and undergraduate researchers at UBC, including Heng Zhao, Aida Aminian, Zainab Saeed Wattoo, and Philip Schowitz. He has led multiple research projects involving robotic arms, NVIDIA Holoscan, FreeRTOS, and distributed key-value stores. His lab work emphasizes reproducibility, open datasets, and practical system building. He has served on program committees for RTSS, RTAS, ECRTS, and Middleware, and contributes to journals such as Real-Time Systems and JSys.
Jens Palsberg is a Professor and former Department Chair of Computer Science at the University of California, Los Angeles (UCLA), where he currently serves as Director of the UCLA-Amazon Science Hub for Humanity and Artificial Intelligence and co-director of UCLA's quantum research center. He chairs ACM SIGPLAN and is a member of the ACM Council. His research spans programming languages, software engineering, quantum computing, compilers, embedded systems, and information security. Palsberg has authored over 80 technical papers, co-authored the book Object-Oriented Type Systems , and revised Appel's textbook on Modern Compiler Implementation in Java . His recent work shows a significant shift toward quantum computing, including compiler techniques and program analysis for quantum systems. Analysis of his recent publications reveals a clear transition from traditional programming language research to quantum computing, with nearly half of his 2022-2024 publications focusing on quantum topics while maintaining strong work in software engineering and programming languages. His quantum research particularly emphasizes compiler optimization, abstract interpretation, and circuit analysis. ACM SIGPLAN Distinguished Service Award (2012) UCLA teaching award for quantum computing courses (2023) National Science Foundation CAREER and ITR awards Purdue University Faculty Scholar award IBM Faculty Award Okawa Foundation research award Palsberg has served in numerous leadership roles including general chair of POPL, conference chair of LICS, and vice chair of ACM SIGBED. His research has been supported by DARPA, Intel, British Telecom, and the National Science Foundation. He was instrumental in establishing UCLA's Masters degree in quantum science and has mentored numerous students through his legendary proof sessions. He leads a research group of over 30 professors in UCLA's quantum research center and maintains active collaborations across academia and industry, particularly with Amazon through the UCLA-Amazon Science Hub.
Timothy Baldwin is a Professor at the University of Melbourne, School of Computing and Information Systems, with additional affiliation at Mohamed bin Zayed University of Artificial Intelligence in UAE. His research spans natural language processing, large language models, and multilingual AI systems. His research interests focus on the safety, reliability, and ethical aspects of large language models. He investigates bias evaluation and debiasing techniques, uncertainty quantification methods, fact-checking systems, and multilingual model safety. His work addresses critical challenges in making AI systems more transparent, reliable, and culturally aware, with particular attention to low-resource languages and cross-cultural differences. Baldwin's recent publications demonstrate a strong focus on evaluating and improving the safety of language models across diverse linguistic contexts, developing tools for fact verification, and understanding the internal mechanisms of large language models. His research shows increasing emphasis on practical applications with real-world impact, particularly in multilingual settings and safety-critical domains. His scientific contributions include foundational work on multilingual NLP, bias mitigation techniques, and frameworks for evaluating LLM safety across different cultural contexts. His research has been published in top-tier venues including ACL, NAACL, EMNLP, and ICLR. Baldwin actively mentors students and junior researchers, with frequent collaborations with Haonan Li, Xudong Han, and Fajri Koto, among others. His research group appears to focus on practical applications of NLP with strong ethical considerations, particularly regarding model safety and cultural sensitivity.
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.
Florian Leiser is a Professor at the Chair of Information Infrastructures (led by Prof. Dr. Ali Sunyaev) at Technical University of Munich's Heilbronn campus. His research focuses on human-AI collaboration, privacy-preserving algorithms, and explainability in machine learning systems. Current research areas include Hybrid Intelligence, Human-centered Generative AI (LLMs), Federated Learning, and Health Information Systems Recent publications demonstrate expertise in Explainable AI for medical imaging LLM hallucination detection Federated learning architectures Human-in-the-loop systems Healthcare data applications He contributes to teaching through Human-Centered Artifact Design courses Collaborative teaching roles in machine learning Supervising student projects
