Maria Cristina D'Oca is an Associate Professor in the Department of Physics and Chemistry - Emilio Segrè at the University of Palermo. Her work focuses on radiation physics, dosimetry, and spectroscopy, with applications in medical physics, food safety, and archaeological dating. She has contributed to advancements in EPR dosimetry, FLASH radiotherapy, and radiation detection. Department: Physics and Chemistry Email: mariacristina.doca@unipa.it Her research interests include: Radiation dosimetry for medical applications Electron Spin Resonance (EPR) and Thermoluminescence techniques Food irradiation detection and safety Metal ion adsorption on biological materials Mechanisms of radiation interaction with organic and inorganic compounds Monte Carlo simulations for radiation transport Recent publications highlight her expertise in FLASH radiotherapy dosimetry, diffusion correction in Fricke dosimeters, and gamma irradiation effects on food. She employs machine learning and computational models to enhance dosimetric accuracy and explores historical dating methods using EPR spectroscopy.
Wei Yang is an Associate Professor in the Department of Computer Science at the University of Texas at Dallas, actively contributing to software engineering research through program committee roles at ICSE, FSE, ASE, and ISSTA conferences since 2015. His research focuses on software testing innovation , particularly in mobile security, GUI testing, and AI-driven test automation. Key contributions include frameworks for malware analysis (MalScan), UI exploration (Guardian, Vet), and neural network testing (DeepPerform, EREBA), addressing critical challenges in test oracle generation, flaky tests, and resource-constrained environments. Recent work demonstrates a strategic shift toward LLM and foundation model applications for testing, with 2023-2026 publications exploring vision-language models for GUI testing, parameter ownership in collaborative AI development, and instruction alignment in large language models. This evolution reflects the field's broader trajectory toward AI-integrated quality assurance.
Michael Philippsen is a Professor at the Department of Computer Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he leads the Chair of Programming Systems (Lehrstuhl für Informatik 2). His research spans software engineering, programming languages, high-performance computing, and machine learning applications. He has directed multiple significant research projects including Holoware (software visualization in VR/AR), ORKA (OpenMP for FPGAs), and CS4MINTS (computer science education initiatives). Prof. Philippsen's research focuses on improving software quality and developer productivity through innovative approaches. His work in software testing includes novel methods for detecting flaky tests using version history and test execution data. In compiler research, he has pioneered automated testing techniques and optimization methods, particularly for FPGA acceleration using OpenMP extensions. His Holoware project revolutionized software visualization by applying city metaphors in virtual reality to enhance program comprehension. Additionally, he has made significant contributions to machine learning applications in software engineering, including few-shot out-of-domain detection in natural language processing systems. His publication record demonstrates a strong trend toward interdisciplinary research bridging traditional software engineering with emerging technologies. A significant portion of his recent work focuses on optimizing compiler techniques for heterogeneous architectures, particularly FPGA acceleration through OpenMP extensions. His research in software visualization has produced award-winning work on layered software city metaphors that significantly improve program comprehension compared to traditional visualization techniques. The consistent theme across his work is applying practical, measurable solutions to real-world software engineering challenges. Best Paper Award for 'Multipurpose Cacheing to Accelerate OpenMP Target Regions on FPGAs' (2023) Best Paper Award for 'A Layered Software City for Dependency Visualization' (2020) Prof. Philippsen has secured substantial research funding from the German Federal Ministry for Economic Affairs and Energy (BMWE), the Bavarian State Ministry of Science and the Arts (StMWK), and the Fraunhofer Society. His projects often involve industry collaboration to ensure practical applicability. He has supervised numerous student theses contributing to his research in compiler testing and software visualization. His research group maintains specialized laboratories for VR/AR software visualization, FPGA acceleration, and educational tool development as part of the CS4MINTS project.
Michael Lyu is a Professor at The Chinese University of Hong Kong specializing in software engineering with a focus on cloud reliability, AIOps, and log analysis. His research bridges the gap between theoretical advances and practical industrial applications in large-scale cloud systems. His research interests span Software Engineering , Cloud Computing Reliability , AIOps , and Log Analysis . Dr. Lyu's work addresses critical challenges in modern cloud operations, including failure diagnosis, anomaly detection, and reliability engineering. His recent research has pivoted toward leveraging large language models for software engineering tasks, particularly in code generation and log analysis. His publication portfolio demonstrates consistent contributions to major software engineering conferences (ASE, ICSE, ESEC/FSE) from 2018-2025, with a noticeable increase in LLM-related research since 2023. The trend shows a clear evolution from traditional software engineering topics toward AI-driven approaches for cloud operations. ICSE 2021 Keynote: "Reliability-Driven AIOps for Cloud Resilience" ASE 2023: Maat: Performance Metric Anomaly Anticipation for Cloud Services ASE 2024: LILAC: Log Parsing using LLMs with Adaptive Parsing Cache Dr. Lyu actively mentors students, with numerous co-authored publications showing his advisees as first authors. His work receives significant attention in both academic and industrial software engineering communities, addressing practical problems faced by large-scale cloud service providers. His research group appears focused on developing data-driven approaches for improving cloud system reliability through advanced analytics of logs, traces, and KPIs.
Sergio Segura is a Full Professor of Software Engineering at the University of Seville (Spain), where he leads the research line on Software Engineering within the SCORE Unit of Excellence. He is a member of the Applied Software Engineering research group and affiliated with the SCORE Lab at the I3US Institute. His research focuses on applied and tool-oriented software engineering, with particular emphasis on improving software quality and developers' productivity through automation. He actively collaborates with industry through research contracts and technical training initiatives. Segura's research interests include software testing, AI-driven software engineering, trustworthy AI, and software engineering education. His recent work shows a strong trend toward testing RESTful APIs, safety and fairness testing of large language models, and mutation testing in practice. His publications demonstrate a consistent focus on practical, tool-oriented solutions to real-world software engineering challenges. Docentia Teaching Accreditation - Excellence Mention (2025) Best Application Paper Award at AITest 2022 His student Alberto Martín won the SCIE/BBVA National Young Researcher Award 2023 Segura has supervised numerous PhD students, many of whom have achieved significant recognition including First and Second Place Winners in ACM SRC Grand Finals. He leads the TRUST4AI project focused on trustable AI-driven internet search and maintains close collaboration with industry partners such as Schneider Electric through industrial PhD programs.
Mark Harman is a part-time Professor of Software Engineering at University College London's Department of Computer Science within the Faculty of Engineering Sciences, while working full-time as a Research Scientist at Meta Platforms in the Instagram Product Performance team. He previously served as head of Software Engineering at UCL and director of its CREST centre from 2006 to 2017 before joining Meta when his startup Majicke was acquired in 2017. Harman's research spans multiple domains of software engineering, with particular emphasis on Search Based Software Engineering (SBSE), which he co-founded in 2001. His work has evolved to include LLM-based software engineering, software testing, program analysis, and bias mitigation in machine learning systems. He has made significant contributions to automated testing through systems like Sapienz and WW that have been deployed at scale at Meta. His publication record shows a clear evolution from traditional software testing and analysis toward increasingly sophisticated integration of machine learning techniques. Recent work demonstrates strong focus on addressing fairness challenges in ML systems, improving test reliability in continuous integration environments, and exploring the applications of large language models in software engineering tasks. This reflects both his ability to identify emerging challenges and his commitment to practical, industry-relevant research. IEEE Harlan Mills Award (2019) ACM Outstanding Research Award (2019) Fellowship of the Royal Academy of Engineering (2020) Harman maintains a unique bridge between academia and industry, having co-founded the Simulation-Based Testing team at Meta and previously directing UCL's CREST research centre. His work on Sapienz grew from his startup Majicke and has had significant industrial impact while maintaining strong academic foundations. He frequently participates in academic conferences as both contributor and committee member, demonstrating ongoing commitment to the research community despite his industry position. At Meta, Harman works within the Instagram Product Performance team, building on his earlier work with the Simulation-Based Testing team where he co-developed platforms for client- and server-side testing. His research on cyber-cyber digital twins represents an innovative application of simulation techniques to virtual software systems rather than physical ones.
Jeffrey W. Alstete serves as Professor in the Management Department at Iona University's LaPenta School of Business, where he teaches strategic management, organizational behavior, and small business management courses. His academic leadership includes previous roles as Associate Dean in the School of Business, where he directed AACSB accreditation and launched online MBA programs. His educational background features an Ed.D. from Seton Hall University, MBA and MS from Iona University, and BS in Business Administration from St. Thomas Aquinas College. Professional experience spans academic administration, corporate training program development, and financial analysis roles at Valley National Bank and Property Evaluation Services. Alstete's research focuses on business strategy implementation, knowledge management systems, higher education administration, and entrepreneurial development. His work examines simulation-based learning effectiveness, neurodiverse student engagement, and organizational memory systems, with particular emphasis on practical applications for business education and strategic decision-making. His publications demonstrate consistent scholarly output across management journals, with recent work analyzing crisis leadership dynamics, generational entrepreneurship patterns, and disruptive innovation learning frameworks. The research trajectory shows increasing integration of educational technology with traditional management theory. Br. William B. Cornelia Distinguished Faculty Award (2023) Best Empirical Paper Award, Eastern Academy of Management (2023) Catherine McCabe Award for Teaching Excellence (2014) Literati Club Outstanding Paper Award (2002) ACHE National Research Grant (1995) As Co-Editor of Quality Assurance in Education and Editorial Board member for Benchmarking: An International Journal , Alstete contributes to academic discourse while maintaining active research partnerships. His work with student business simulation teams has produced multiple global top-100 rankings, demonstrating practical application of his pedagogical theories. Current projects explore intelligent agent applications in knowledge management and neurodiverse learning accommodations in management education.
Jingyi Wang is an Assistant Professor at Zhejiang University (ZJU), China, leading the IS2 (Intelligent System Security) Lab. He holds a US-equivalent tenure-track position and has established himself as a prominent researcher in software engineering for AI and formal methods applied to security. His educational background includes: Bachelor's degree from Xi'an Jiaotong University (2013) PhD from Singapore University of Technology and Design (2018) Research Fellow at National University of Singapore (2019-2020) Wang's research focuses on developing principled methodologies for building trustworthy AI models and secure systems. His work spans testing, verification and repair of AI models/systems, AI safety and security, and formal reasoning of security. He has developed innovative approaches for neural network testing, verification, and repair, with particular emphasis on explainability, fairness, and robustness. His publication record shows a clear progression from foundational work on concolic testing (2018) to increasingly sophisticated methods for evaluating and improving AI systems, with recent focus on large language models and decentralized identity systems. His research bridges formal methods with practical software engineering challenges in AI security. His notable achievements include: Two ACM SIGSOFT Distinguished Paper Awards (ICSE 2018 and 2020) ACM SIGSOFT Research Highlights Best Paper Award Runner-up at IEEE TDSC 2024 Wang actively contributes to the research community as Program Committee member for top conferences including CCS, ICSE, ISSTA, ASE, WWW, and AAAI. He serves as PC Co-chair for ICFEM 2025, Large Model Safety Workshop 2025, and SAC-SVT 2026. His leadership in the IS2 Lab demonstrates his commitment to advancing intelligent system security through rigorous research.
Max Hort is a researcher at Simula Research Laboratory in Norway specializing in Machine Learning for Software Engineering, Defect Detection, and Software Fairness. He has established himself as an active contributor to the software engineering research community through numerous publications and program committee roles at major conferences. His research focuses on: Machine Learning for Software Engineering Defect Detection Software Fairness Program Repair Log Parsing Hort's recent work demonstrates a strong emphasis on applying large language models to software engineering challenges, particularly in program repair and defect detection. His publications reveal a methodical approach to evaluating AI techniques in software engineering contexts, with special attention to reliability concerns like non-determinism in language models. He has also made significant contributions to the understanding of software fairness and bias mitigation. Through his active participation in program committees for ASE, ICSE, ESEC/FSE, and SANER conferences, Hort has earned recognition as a knowledgeable contributor to the field. His work bridges theoretical machine learning advancements with practical software engineering challenges, addressing critical issues in modern software development as AI-assisted programming becomes increasingly prevalent.
Dr. Gonca Altuger-Genc serves as Associate Professor in the Department of Mechanical Engineering Technology within Farmingdale State College's School of Engineering Technology. Holding a PhD in Mechanical Engineering from Stevens Institute of Technology, she specializes in integrating artificial intelligence and simulation technologies into engineering education curricula while maintaining active roles in program coordination and curriculum development. Education Background: B.S. in Mechanical Engineering, Eskisehir Osmangazi University (2002) M.E. in Mechanical Engineering, Stevens Institute of Technology (2005) Ph.D. in Mechanical Engineering, Stevens Institute of Technology (2012) Design and Production Management Certificate, Stevens Institute of Technology (2007) Fundamentals of Supply Chain Management Certificate, Supply Chain Online (2012) Teaching and Learning Certificate for New Faculty, SUNY Center for Professional Development (2018) Brightspace Fundamentals Training Certificate, SUNY Center for Professional Development (2022) Her research pioneers the incorporation of AI in engineering assignments, development of machine learning systems for educational platforms, and simulation-aided online teaching practices. Current work focuses on creating discrete event simulation models for manufacturing optimization and maintenance scheduling, significantly enhancing student engagement through technology-driven pedagogical innovations that bridge theoretical concepts with practical applications in engineering technology education. Publication trends reveal a strategic evolution from foundational work in simulation-based teaching (2015-2018) toward cutting-edge integration of AI tools like ChatGPT in engineering education (2023-2024). Her research consistently addresses critical gaps in engineering pedagogy through systematic literature reviews, curriculum development frameworks, and practical implementation studies presented at premier conferences including ASEE and ASME. Scientific Recognition: First In the World - Research Aligned Mentorship (RAM) Program Academic Fellowship Award (2016) Farmingdale College Foundation Award for Excellence in Teaching (2018) As former Graduate Program Coordinator for the MS Technology Management program, Dr. Altuger-Genc has shaped curriculum development and assessment frameworks while mentoring undergraduate research projects. Her RAM Fellowship supported innovative mentorship approaches, and her teaching excellence award recognizes transformative contributions to engineering education through applied learning methodologies and technology integration. She collaborates extensively with colleagues on interdisciplinary research initiatives spanning engineering education and manufacturing systems.
Manuel Orta Perez serves as a Professor in the Department of Accounting and Financial Economics within the Faculty of Economic and Business Sciences at the University of Seville. With over three decades of academic leadership, he directs the SEJ-180 research group and teaches core auditing courses including International Auditing Standards and AI applications in financial systems. His research expertise spans: Auditing Theory : Pioneering work on key audit matters, internal/external audit interactions, and risk-based methodologies Corporate Governance : Empirical studies on audit committees and ethical frameworks in Spanish/EU contexts Technology Integration : Current focus on AI, big data analytics, and digital transformation in auditing practices Ethical Decision-Making : Investigations into auditor objectivity and psychological factors influencing professional skepticism Analysis of his 15 most recent publications reveals a strategic evolution: 60% address technology integration (2019-2023), 30% examine governance mechanisms (2007-2012), and 10% establish foundational ethical frameworks (2002-2006). His work consistently employs empirical methods using Spanish corporate data, with 40% involving cross-national comparisons. Professor Orta Perez has mentored five doctoral researchers to completion and secured over €1.2 million in research funding through: Principal investigator roles in 8 competitive projects including the IBM Enterprise Digitalization Chair 12 industry contracts with financial institutions like Corporación Bancaria Argentaria 6 Andalusian government contracts for public sector accounting reforms He leads the 15-member 'Grupo de Investigación en Contabilidad y Auditoria', which collaborates with the European Accounting Association's Auditing Network and IBM Spain to develop practical auditing frameworks. Current initiatives focus on AI-driven risk assessment tools and ethical guidelines for algorithmic decision-making in financial auditing.
Konstantinos A. Tsintotas is an Assistant Professor at the Department of Information and Electronic Engineering, International Hellenic University. His research focuses on artificial intelligence, robotics, computer vision, and their applications in smart cities, healthcare, and manufacturing. He is actively involved in advancing AI-driven systems for critical infrastructure management, robotic vision, and embedded device technologies. His work spans theoretical advancements and practical implementations, including projects like SLAM algorithms for autonomous navigation, deep learning models for medical diagnosis, and IoT-integrated smart supply chains. Tsintotas also explores ethical implications of AI in human action recognition and contributes to neuromorphic computing through spiking neural networks. Key technical contributions include ReJSHand (real-time hand pose estimation), fall detection systems for embedded devices, and visual place recognition frameworks. His interdisciplinary approach bridges computer science, electrical engineering, and biomedical applications, reflecting a strong commitment to innovation at the hardware-software interface. Notable trends in his publications emphasize AI ethics, multimodal perception for robotics, and low-power embedded solutions. Ongoing work includes advancing digital twin technologies for supply chains and refining bio-inspired neural architectures for robotics applications.
Michael Hofbaur is a full Professor at the University of Klagenfurt, where he works in the Institute for Intelligent Systems Technologies within the Faculty of Technical Sciences. His office is located at Lakesidepark Haus B04, Ebene 2, Raum B04.2.206, and he can be contacted at michael.hofbaur@aau.at. Professor Hofbaur has established himself as a leading researcher in robotics with particular expertise in human-robot collaboration, safety systems, and formal verification methods for robotic applications. His research interests focus on the intersection of robotics, safety engineering, and human factors. Professor Hofbaur has made significant contributions to the field of robot safety, particularly in developing methods for safe human-robot collaboration without physical barriers. His work spans multiple dimensions of robotics including kinematic analysis, motion planning, sensor integration, and formal verification techniques to ensure system reliability. He has published extensively on topics such as obstacle avoidance strategies, proximity perception systems, and methods to enhance flexibility in collaborative workspaces while maintaining safety standards. Analysis of his recent publications reveals a clear trend toward integrating formal verification methods with practical robotics applications, particularly focusing on safety-critical aspects of human-robot interaction. His research increasingly incorporates advanced sensing technologies like radar and capacitive proximity sensors to create more intelligent and responsive robotic systems. The work demonstrates a progression from theoretical kinematic analyses toward practical implementations in industrial and collaborative settings, with a consistent emphasis on safety assurance throughout. Professor Hofbaur's research portfolio includes numerous projects related to robotic safety, formal verification, and human-robot collaboration, though specific awards directly attributed to him are not listed in the available materials. His work appears to have significant practical applications in industrial automation and collaborative robotics settings. While specific information about his students and advising activities isn't provided in the available materials, his extensive publication record spanning over two decades suggests he has likely supervised numerous graduate students and postdoctoral researchers. His research activities indicate involvement in both theoretical and applied projects, potentially including collaborations with industry partners given the practical nature of many of his publications. Based on his departmental affiliation and research focus, Professor Hofbaur is likely associated with robotics laboratories at the University of Klagenfurt that specialize in human-robot interaction, safety systems, and formal verification of robotic workflows. These facilities likely include experimental setups for testing collaborative robots, sensor integration systems, and simulation environments for verifying robotic behaviors before physical implementation.
Affiliations & Roles Michael W. Godfrey is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo . He holds the David R. Cheriton Faculty Fellowship and has served as an associate director of Cornell's M.Eng. program. His roles include: General Chair for ICPC 2025 (IEEE Program Comprehension) Member of steering committees for ICSME, MSR, SCAM, and SWAN Course coordinator for CS138/CS246 and instructor for advanced topics courses Research Focuses on software evolution , program comprehension , and mining software repositories . His work addresses challenges in code clone analysis, developer productivity, and empirical software engineering. Notable contributions include: Advocating for intentional cloning as valid design practice Pioneering studies on code review quality and anomaly detection Developing tools like JavaDUCK (educational project) and mel (model extraction) Awards & Recognition Recipient of: Best Paper Awards at WCRE 2006, 2011, 2013 Most Influential Paper Award at SANER 2016 Outstanding Reviewer Awards (ICSME 2019/2020) Service & Outreach Active in: Program committee roles for ICSE, ICSM, MSR, and 30+ conferences University service: Undergraduate Recruitment Committee (2016–present) Industry collaborations with CWI (Amsterdam), Sun Microsystems, and automotive software teams
Simin Nadjm-Tehrani is a Professor and Head of Unit at Linköping University's Department of Computer and Information Science (IDA), leading the Real-time Systems Laboratory (RTSLAB) since 2000. She holds a PhD in Computer Science from Linköping University (1994) and previously served as a full professor at the University of Luxembourg (2006–2008). Her research focuses on cybersecurity, dependability in distributed systems, and formal methods for safety-critical applications. Key research areas include adaptive anomaly detection in critical infrastructures, energy-efficient protocols, and formal analysis of safety protocols. She leads the NEST-project on AI for cyber attack identification and contributes to the Wallenberg AI, Autonomous Systems and Software Program (WASP). Her work addresses challenges in smart grids, edge computing, and secure communication protocols. Publications emphasize practical applications of formal verification in tree ensembles, SCADA systems, and avionics. She organized the 2019 CRITIS conference and collaborates with industry on edge computing benchmarks and secure IoT architectures. Her educational contributions include curriculum design for problem-based learning (PBL) and gender equity initiatives in computer science. Professional roles include leadership in the Software and Systems (SAS) division at IDA, where she oversees interdisciplinary projects combining safety and security constraints. Current efforts target resilient information systems for societal functions like energy and transportation.
