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
Dr. Husam Al-Najjar is a Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He serves as the Course Director for the Bachelor of Information Systems (BIS) program. With expertise in geospatial technology and machine learning, Dr. Al-Najjar focuses on predicting and mitigating natural hazards and environmental issues to contribute to a sustainable digital earth. Dr. Al-Najjar earned his PhD from the University of Technology Sydney. Before joining academia, he worked in project management and has received numerous prestigious awards, scholarships, and grants throughout his career. Dr. Al-Najjar's research primarily centers on the application of machine learning techniques to address complex environmental challenges. His work spans geospatial AI, natural hazard prediction (particularly landslides and bushfires), and sustainable development. He has developed innovative approaches that integrate physical models with machine learning algorithms to improve prediction accuracy in data-scarce environments. His research also extends to remote sensing applications, urban planning, and smart city technologies, with a strong emphasis on practical solutions for real-world problems. Analysis of Dr. Al-Najjar's recent publications reveals a strong focus on applying explainable AI techniques to natural hazard prediction, particularly landslides. His work consistently bridges the gap between theoretical machine learning approaches and practical geospatial applications. He has made significant contributions to integrating physical models with AI, developing methods for handling imbalanced data through generative adversarial networks, and improving feature selection for remote sensing applications in environmental monitoring. Best Paper Award at the ISPRS Geospatial Week in Enschede, the Netherlands As an educator, Dr. Al-Najjar is actively involved in mentoring and teaching. He serves as Course Director for the Bachelor of Information Systems program and teaches courses in GIS, Information Systems, IS development methodologies, Design & Innovation, and Project Management. He welcomes prospective PhD candidates interested in his research areas and emphasizes the importance of detailed research proposals that demonstrate novelty and significance. His teaching philosophy focuses on fostering an engaging and inclusive learning environment that promotes student success and well-being. Dr. Al-Najjar is affiliated with 'The Trustworthy Digital Society' concentration at UTS and serves as a referee and holds editorial roles in respected journals. His work contributes to the development of geospatial AI frameworks that support decision-making in environmental management and disaster preparedness.
Heather Miller is a tenure-track Assistant Professor in the Software and Societal Systems Department within Carnegie Mellon University's School of Computer Science. Her academic journey includes prior roles as an Assistant Clinical Professor at Northeastern University's College of Computer and Information Science and as Executive Director of the Scala Center at EPFL. Miller's research centers on distributed and concurrent computation through the lens of programming languages, with particular emphasis on data-centric systems, big data processing, and edge computing. A defining theme throughout her work is composability - enabling construction of complex distributed systems through composition of components that are correct by construction. Her projects span distributable closures, flexible serialization techniques, futures and promises for asynchronous programming, and deterministic concurrent dataflow models. Her recent publications demonstrate strong trends in applying programming language theory to practical distributed systems challenges, with increasing focus on WebAssembly instrumentation, microservice resilience, and language model pipelines. This evolution reflects her commitment to bridging theoretical foundations with real-world system requirements. Dahl-Nygaard Junior Prize (2023) Mentorship forms a significant component of Miller's academic work. She actively supervises multiple PhD, MS, and undergraduate researchers at CMU, including Christopher Meiklejohn, Matthew Weidner, Huairui Qui, Ria Pradeep, and Luke Dramko. Her service contributions span numerous top-tier conferences including PLDI, SPLASH, ECOOP, and ICSE where she has served as committee member, chair, and keynote speaker. Miller co-founded the Curry On conference to foster industry-academia dialogue, hosting successful editions in Prague, Rome, Barcelona, Amsterdam, and London. She leads research groups focused on distributed programming models and maintains strong industry connections through Two Sigma, where she holds an affiliation. Her work consistently emphasizes practical open-source implementations, primarily within the Scala ecosystem where she's been a core contributor since 2011.
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.
Ali Ghanbari is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. His research focuses on software engineering, programming languages, and data science, with an emphasis on automated program repair, deep learning, and mutation analysis. He received his Ph.D. in Software Engineering from the University of Texas at Dallas and his M.Sc. and B.Sc. from Amirkabir University of Technology in Tehran, Iran. Education: Ph.D. Software Engineering, University of Texas at Dallas M.Sc. Software Engineering, Amirkabir University of Technology B.Sc. Software Engineering, Amirkabir University of Technology Research Interests: Dr. Ghanbari's work spans automated program repair, deep neural network analysis, and mutation-based fault localization. He explores techniques to enhance software quality through methods like patch correctness assessment, object similarity-based prioritization, and optimization of mutation testing frameworks. His contributions include frameworks such as PRF and tools like Shibboleth for hybrid patch evaluation. Publications Trends: His recent work highlights advancements in accelerating mutation analysis, improving deep learning models via modular decomposition, and refining automated repair techniques. Notable contributions include Rocq for goal clone detection and MeMu for faster mutation analysis. Awards & Grants: No specific awards or grants mentioned in the provided materials. Advising & Labs: While no advisees are listed, his research group likely focuses on program repair and deep learning applications. His work is supported by datasets like Defexts, which provides reproducible real-world bugs for JVM languages.
Shiny Verghese is a Lecturer in Computer Science at Teeside University, affiliated with the Faculty of Computing, Engineering and Science and the Computing, Cybersecurity, Mathematics and Informatics Research and Innovation Group. She holds a PhD in Human-Computer Interaction from Teeside University (2019), focusing on design parameters for online psychometrics. Her research spans human-computer interaction, online questionnaire design, and posture monitoring using machine learning. She leads the 2023-2028 project "A Smart Sensing System for Continuous Sitting Posture Recognition and Monitoring using Machine Learning and a Mobile App" , funded in part by the Kuwait Foundation of Advancement of Sciences. Teaching focuses include programming languages, object-oriented concepts, algorithms, and event-driven programming. Her work bridges HCI with educational technology, emphasizing innovative pedagogy and creative problem-solving. Recent collaboration on posture monitoring systems demonstrates expertise in sensor technology and health informatics. Key contributions include a novel online research tool for psychometric questionnaires and a comprehensive review in Sensors (2024) on smart posture monitoring systems. She collaborates with researchers like Prof. Kulon and Dr. Odesola, advancing applications of machine learning in health and education.
Zhenchang Xing is a Research Professor at CSIRO's Data61 and holds a Hans Fischer Senior Fellowship at TUM-IAS. With a Ph.D. in Computer Science from the University of Alberta (2008), he previously served as Associate Professor at Australian National University and Assistant Professor at Nanyang Technological University. His research focuses on software engineering for AI systems and human-centered computing. Current projects include: Automated Software-Hardware Co-Design for AI Systems Software Supply Chain Security frameworks Data Bill of Materials (DataBOM) for verifiable data ecosystems Software Testing Knowledge Graph development Professor Xing has received 10 Distinguished Paper Awards from ACM and IEEE, including the 2005 Most Influential Paper Award for UMLDiff. His work combines software engineering with responsible AI development.
Alexandra Kirsch is an Assistant Professor in the Media Informatics Department at the University of Tübingen's Faculty of Informatics. She held the Carl von Linde Junior Fellowship at the Technical University of Munich (TUM) Institute for Advanced Study (TUM-IAS) from 2010. Previously, she was a senior research scientist at TUM's Intelligent Autonomous Systems Group and led the independent Junior Research Group “Planning for Adaptive Robot Assistance” within the Excellence Cluster CoTeSys (Cognition for Technical Systems). Education: Diploma in Computer Science from TUM, 2003 Doctoral degree from TUM, completed between 2003-2007 Research Interests: Kirsch focuses on developing control mechanisms for autonomous robots using artificial intelligence, aiming to create systems that collaborate closely and transparently with humans. Her work emphasizes models of world dynamics, robot action effects, and human behavior. She created the Robot Learning Language (RoLL) to automate model acquisition and update processes during robot operations. Collaborations with psychologists and neuroscientists explore joint human-robot planning tasks and model development for seamless interaction. Scientific Awards: Member of the Bayerische Akademie der Wissenschaften Förderkolleg (2012) Award by Comet Computer GmbH for excellent graduation results (2003) Advising & Grants: Managed interdisciplinary research projects during her junior fellowship at TUM. Previously worked as a management consultant at Booz & Co., 2007-2008. Her grants include the Carl von Linde Fellowship and support for the Junior Research Group. Labs/Teams: Active in the Planning for Adaptive Robot Assistance group (CoTeSys) and collaborates with the Cognitive Technology focus group at TUM-IAS. Engages in cross-disciplinary teams involving neuroscience and psychology for human-robot interaction studies.
Jianwen Su is a Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB), where he has been since 1990. He holds a Ph.D. in Computer Science from the University of Southern California and B.S./M.S. degrees from Fudan University in China. His research focuses on databases, formal verification, web services, business process management (BPM), and workflow systems. He has contributed to data-centric workflow modeling, artifact-based BPM frameworks, and tools like the Web Service Analysis Tool (WSAT). Adjunct professorships at Peking, Fudan, and Donghua Universities in China. Key roles: General co-chair of ICSOC 2013, PC chair of PODS 2009, and general chair of SIGMOD 2001. Recipient of the 2000 Outstanding Faculty Award (UCSB College of Engineering) and IBM Faculty Awards (2007, 2008). Research spans database query languages, incremental query evaluation, spatial databases, and formal verification techniques for software systems. Current emphasis is on data modeling for workflows and BPM systems.
Bradley Schmerl serves as a Principal Systems Scientist in the Software and Societal Systems Department (S3D) within Carnegie Mellon University's School of Computer Science. His research advances software engineering practices for modern challenges in distributed heterogeneous systems, self-adaptation, and cyber-physical integration. He leads the ABLE research group and actively mentors students in the Masters in Software Engineering program while teaching core courses like Software Architecture and Software Engineering Practicum. Dr. Schmerl's work addresses critical challenges in composing continuously evolving software systems, including components from untrusted third parties and on-the-fly recomposition for environmental changes. His research develops reusable, analyzable tools for software composition with emphasis on model-based adaptation, uncertainty management, and cross-language integration. Key projects include Rainbow for runtime architecture reflection, Acme for formal architectural foundations, and Cyber-physical Systems research linking software models with physical dynamics. Analysis of his 2023-2025 publications reveals intensifying focus on robotics software architecture (particularly ROS-based systems), explainable AI for architectural tradeoff analysis, and configuration management in adaptive systems. Trends show growing integration of machine learning for auto-tuning, empirical studies of misconfigurations, and dimensionality reduction techniques for visualizing design spaces—consistently bridging theoretical rigor with practical tool development for real-world applications. Scientific Awards: No specific awards were documented in the source materials. Dr. Schmerl serves as Practice Area Lead and mentor in CMU's Masters in Software Engineering program, guiding client projects including Rainbow UI for self-adaptive framework interfaces, CoBot UI for telepresence robots, and Educational Telepresence Tasking Language development. His research receives support through ABLE group projects funded by grants targeting software architecture foundations, adaptation mechanisms, and cyber-physical system validation. As a core member of the ABLE research group, he directs investigations into architecture-based self-adaptation with active projects spanning Rainbow (runtime architecture models for dynamic adaptation), Acme (formal architectural styles and tools), and Cyber-physical Systems (software-physical model integration). The group also maintains legacy work in End-User Architecting, Architecture Evolution, and service-oriented platforms for intelligence analysis through SORASCS.
Piera Rossetto is a Researcher at the Department of Asian and North African Studies, Ca' Foscari University of Venice. Her work bridges Jewish studies, migration, memory, and creative methodologies, with a focus on postcolonial North African and Middle Eastern Jewish communities, especially from Libya. She leads major research projects and teaches Hebrew language and social research methods. PhD in Languages and Civilizations of Asia and the Mediterranean Africa, Ca' Foscari University and EHESS Toulouse (2015) MA in South and Western Asia: Languages, Cultures, and Institutions, Ca' Foscari University (2011) Her research explores memory, identity, and belonging through narrative cartography and co-creative methods. She investigates Jewish Mediterranean migrations, particularly of Libyan Jews, using oral histories, literary sources, and digital tools. Her work emphasizes marginalized voices and interdisciplinary, artistic approaches to research. The recent articles highlight a strong trend in narrative and creative cartography, Jewish postcolonial memory, and interdisciplinary research-creation. Themes include the poetics of identity, mapping invisible migrations, and digital documentation of diasporic memories. Her publications span monographs, journal articles, book chapters, and multimedia outputs. Scientific awards and fellowships include: Rita Levi Montalcini Program for Young Researchers (Principal Investigator) FWF Hertha Firnberg Postdoctoral Fellowship Fondazione Rothschild Doctoral Fellowship Fondazione CDEC Research Fellow She has secured competitive grants, including the FWF-funded "Europe's (In)Visible Jewish Migrants" and the ongoing Rita Levi Montalcini project on gender, aging, and migration. She has advised collaborative, interdisciplinary research teams and supervised creative digital projects. Her work often involves partnerships across Europe and digital humanities platforms. She leads the research project "Genere, invecchiamento e migrazioni: memorie e mascolinità ebraiche del Nord Africa e del Medio Oriente" and previously led an FWF-funded project at the University of Graz. She also contributes to digital initiatives like the World Jewish Congress’s MENA Jewish legacy project and co-developed interactive visualizations and audio-documentaries.
Tom Verhoeff is an Assistant Professor at the Faculty of Mathematics and Computing Science of Eindhoven University of Technology (TU/e) , working within the Software Engineering & Technology group. His research focuses on Model-Driven Engineering (MDE) , Domain-Specific Languages (DSLs) , and the intersection of mathematics, computing, and the arts . He teaches courses in data analytics, programming, algorithms, theoretical computer science , and logic . Verhoeff earned both his MSc and PhD in Technical Science (Mathematics and Computer Science) from TU/e. He is actively involved in promoting mathematics and informatics through initiatives like the annual Bridges conference , and serves as board member and treasurer of the Dutch Mathematics Olympiad , as well as chair of the Koos Verhoeff MathArt foundation . He has also held roles as guest lecturer in Lithuania and Finals Director for the ACM International Collegiate Programming Contest . Research Interests: Verhoeff’s work spans Model-Driven Engineering , domain-specific language development , and 3D geometric modeling . His scholarship often explores symmetry, recursion, and mathematical visualization , particularly through computational art and algorithmic puzzles . Recent publications highlight 3D rotation methods , knot theory , and mathematical art using lattice paths and geometric transformations . Scientific Awards: ACM ICPC European Founders Award (2004) IOI Distinguished Service Award (2007) Second Place in the 2022 Wolfram Computational Art Contest Notable Collaborations and Affiliations: He is affiliated with the Esprit Working Group on Asynchronous Circuit Design (ACiD-WG) , WIRE (TUE Mathematics Alumni) , ACM (Senior Member) , CSTA , IEEE Computer Society , and Royal Dutch Mathematical Society (KWG) .
Professor Matt Garratt is a faculty member at the University of New South Wales (UNSW Canberra), School of Engineering and IT, serving as AI theme lead for the Defence Trailblazer Universities initiative with over $200 million in funding. His primary research focuses on sensing, guidance, and control for autonomous systems within robotics and unmanned aerial vehicles. Garratt's research spans robotics, swarm intelligence, and autonomous systems with emphasis on bio-inspired navigation techniques and adaptive flight control. His work addresses critical challenges including terrain following using vision systems, landing UAVs on moving platforms, and developing self-organizing swarms. He integrates artificial intelligence, computer vision, and machine learning to advance unmanned systems capabilities in complex environments. Analysis of his recent publications reveals strong trends in bio-inspired UAV navigation (particularly honeybee behavior modeling) and swarm robotics applications. His work increasingly incorporates deep learning for perception tasks while addressing real-world challenges like gas plume detection and adversarial robustness in 3D vision systems. The research demonstrates consistent progression toward practical implementation of autonomous systems in dynamic environments. Professor Garratt has secured over $7.7 million in external research funding as Chief Investigator on 33 grants. He actively mentors graduate students with scholarships available for Masters and PhD research in robotics and AI, focusing on: UAV path planning and adaptive control systems Swarm robotics collective motion optimization Bio-inspired autonomous navigation techniques Computer vision for robotic perception He co-founded the UNSW Canberra AIR (AI and Robotics) Group (AIR Lab), which drives research in trusted autonomy, swarm intelligence, and AI integration for defense applications. The lab develops practical solutions for autonomous systems operating in complex, real-world environments while maintaining ethical AI frameworks.
Nadeem Abbas is a Senior Lecturer at the Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Sweden. He earned his PhD in Computer and Information Science from Linnaeus University and has been working with software systems since 2001. His primary research interests include Self-Adaptive Software Systems, Dynamic Software Product Lines, Software Reuse, Requirements Engineering, Software Architecture and Design, and Architectural Analysis and Reasoning. He is actively involved in multiple research groups including: AdaptWise - focusing on foundations and engineering of self-adaptive software systems Engineering Resilient Systems (EReS) Research Lab - investigating system resilience Smart Industry Group (SIG) - an interdisciplinary group focusing on production and product innovation His recent publications show a strong trend in self-adaptive systems with expansion into health inequality research and environmental monitoring applications. His work bridges theoretical software engineering with practical industrial applications, particularly evident in his survey of industry practices in self-adaptation. Nadeem teaches several courses including: 1DV532 - Starting Out with Java 1DV533 - Structured programming with C++ 1DV534 - Object-Oriented Programming with C++ 2DV600 - Foundations of Software Technology 4DV610 - Adaptive Software Systems 2DV604 - Software Architectures 1DV607 - Object-Oriented Analysis and Design using UML He currently supervises multiple research projects related to self-adaptive systems, architectural analysis tools, and health inequality mitigation through digital solutions. His research portfolio demonstrates strong connections between academic research and practical industry applications, particularly in software architecture and adaptation techniques.
Dr. Jennifer Oates is a Senior Personal Tutor and Lead for Wellbeing in the School of Health Sciences at the University of Surrey, where she also holds the role of Senior Teaching Fellow. Her academic career includes previous roles as Lecturer/Senior Lecturer in Mental Health Nursing at King's College London (2016–2022), Specialist Adviser for the Care Quality Commission (2012–2021), and Mental Health Act Reviewer. She holds a PhD in Mental Health from City University of London (2016), complemented by degrees from the University of Leeds, including a BA in English Language & Literature (1997) and an MA in Social Research (2002). Her research focuses on mental health and wellbeing among healthcare professionals and students, mental health ethics, and peer support strategies in higher education. She supervises PhD projects exploring Recovery College collaborations and peer support systems. Dr. Oates is an Associate Editor for the Journal of Interprofessional Care and contributes to editorial boards of mental health publications. She also serves as a Specialist Lay Member of Mental Health Act Tribunals. Her recent work examines wellbeing interventions for NHS staff during the pandemic, curriculum impacts on student wellbeing, and workforce sustainability in forensic mental health settings. She advocates for holistic approaches to mental health education and policy, emphasizing co-production between practitioners and service users.
