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Tao ZhangView profile

Professor

• AI for Software Engineering
• Software Security
• Mining Software Repositories

Dr. Tao Zhang is a Full Professor at the School of Computer Science and Engineering, Macau University of Science and Technology (MUST), Macau SAR. He serves as an Associate Editor for IEEE Transactions on Software Engineering (TSE), IEEE Transactions on Reliability (TRel), and the Journal of Systems and Software (JSS), and is an Editorial Board Member for Empirical Software Engineering (EMSE) and Science of Computer Programming (SCP). His educational background includes: Ph.D. in Computer Science from the University of Seoul B.S. in Automation and M.Eng in Software Engineering from Northeastern University, China Postdoctoral Research Fellow at Hong Kong Polytechnic University Dr. Zhang's research primarily focuses on three interconnected areas that represent the cutting edge of modern software engineering: AI for Software Engineering : Utilizing neural language models and large language models to create automated software engineering tools that help developers produce high-quality software. His work includes evaluating whether pretrained language models truly understand software engineering tasks and developing universal representations for bug reports. Software Security : Employing static analysis, AI technologies, and formal methods to detect malware, vulnerabilities, and privacy leaks in mobile apps and smart contracts. His research spans Android malware detection, smart contract vulnerability analysis, and state manipulation attacks in blockchain systems. Mining Software Repositories : Applying information retrieval and machine learning to extract meaningful insights from software artifacts to improve development efficiency. This includes work on app review analysis, change request localization, and code similarity metrics. His publications demonstrate significant impact across the software engineering community, with over 100 high-quality papers in top venues including ICSE, ESEC/FSE, ASE, TSE, TOSEM, EMSE, JSS, TIFS, and TDSC. Dr. Zhang has received numerous honors and recognitions: Distinguished Member, China Computer Federation (CCF), September 2025 Top Reviewer Award 2023, Journal of Systems and Software (JSS), April 2024 Distinguished Reviewer in 2023, ACM Transactions on Software Engineering and Methodology (TOSEM), February 2024 Senior Member of ACM (October 2020) and IEEE (February 2020) Best Paper Award, 16th Korea Conference on Software Engineering (KCSE), February 2014 As an academic leader, Dr. Zhang serves/served as General or Program Chair for numerous conferences including APSEC 2025, Internetware 2024, SANER 2023, and DSA 2021. He mentors a vibrant research group with multiple postdocs, PhD students, and master's students working on innovative projects in intelligent software engineering and security. His lab actively recruits highly motivated students interested in Data Mining, Artificial Intelligence, Software Security, and Software Engineering. Dr. Zhang leads the "Intelligent Software Data Analysis and Software Security" research team at MUST, which focuses on leveraging AI technologies to solve critical challenges in software development and security. The team maintains strong collaborations with international researchers and regularly publishes in top-tier venues.

Paolo ArcainiView profile

Professor

• Quantum Software Engineering
• Autonomous Systems Testing
• Search-Based Software Testing
• +7 more

Paolo Arcaini is a Project Associate Professor at the National Institute of Informatics in Japan, where he has been employed since April 2019. Previously, he served as a Project Assistant Professor at the same institution from March 2018 to March 2019. Prior to that, he was an Assistant Professor at the Department of Distributed and Dependable Systems of Charles University from March 2015 to February 2018. His academic journey includes postdoctoral fellowships at the University of Bergamo and CNR-IDPA, followed by completing his PhD in Computer Science at the University of Milan. PhD in Computer Science at the University of Milan (January 2010 - December 2012) Postdoctoral fellow at University of Bergamo (October 2013 - February 2015) Postdoctoral fellow at CNR-IDPA (January 2013 - September 2013) Professor Arcaini's research focuses on the intersection of quantum computing and software engineering, particularly in quantum software testing and verification. He has pioneered approaches to applying search-based techniques to quantum software development, including mutation testing and error mitigation for quantum programs. His work also extends to autonomous systems testing, where he develops methods for scenario generation, safety assessment, and falsification of autonomous driving systems. His research in formal methods for cyber-physical systems combines signal temporal logic with search-based techniques to verify complex system behaviors. A significant portion of his work addresses the challenges of testing AI-enabled systems, particularly deep neural networks in safety-critical contexts. Analysis of Professor Arcaini's recent publications reveals a strong trend toward quantum software engineering, with numerous papers on testing methodologies for quantum programs, error mitigation techniques, and quantum-inspired optimization for software testing. His work demonstrates a consistent focus on applying search-based techniques to challenging domains, with increasing attention to safety-critical applications of AI and quantum computing. The interdisciplinary nature of his research connects formal methods, machine learning, and software engineering to address verification challenges in emerging technologies. Best paper award on testing AI system at IEEE International Conference on Artificial Intelligence Testing (AITest 2020) Distinguished paper award at Symposium on Search-Based Software Engineering (SSBSE 2021) Best paper award at Symposium on Search-Based Software Engineering (SSBSE 2020) Professor Arcaini is highly active in the software engineering research community, serving on program committees for numerous top conferences including ASE, ICSE, ISSTA, and ESEC/FSE. His work on quantum software engineering has positioned him as a leader in this emerging field, with multiple tool demonstrations and journal-first papers that bridge theoretical advances with practical applications. His research on autonomous systems testing has resulted in several tools for scenario generation and safety assessment that are being adopted by the automotive industry. While specific grant information isn't detailed in the provided materials, his extensive publication record across multiple high-impact venues suggests substantial research funding support. Professor Arcaini leads the MMM research group (as indicated by his personal website at http://group-mmm.org/~arcaini/), which focuses on model-based methods for software engineering. His team appears to specialize in applying formal methods and search-based techniques to emerging domains like quantum computing and autonomous systems. The group maintains active collaborations with researchers worldwide, as evidenced by the international co-authorship of his publications across European and Asian institutions.

Tim MenziesView profile

Professor

• Search-Based Software Engineering
• Software Analytics
• Software Product Lines
• +4 more

Tim Menzies is a Full Professor at North Carolina State University with a diverse professional background that includes previous careers as a nurse, rocket scientist, taxi driver, and journalist. His primary research focuses on Search-Based Software Engineering (SBSE), software analytics, software product lines, Mining Software Repositories, and data mining and machine learning applications in software engineering. Dr. Menzies has maintained an active research profile with publications spanning from 2018 through 2026, serving on program committees and organizing tracks at major conferences including ASE, ICSE, and ESEC/FSE. His work consistently bridges theoretical research with practical applications, emphasizing solutions that are both technically sound and implementable in real-world settings. His research interests include: Search-Based Software Engineering (SBSE) Software analytics and data-driven approaches Software product lines and configuration Mining Software Repositories Machine learning applications in software engineering Fairness in software engineering tools Interpretable and explainable AI for software engineering His recent publications (2023-2026) reveal a strong trend toward making complex software analytics more accessible and understandable while maintaining performance. He has pioneered approaches that simplify models without sacrificing effectiveness, directly addressing the 'black box' problem that often hinders adoption of machine learning in practice. Dr. Menzies is known for his critical perspective on software engineering research, challenging assumptions in the field, particularly regarding deep learning applications. His work emphasizes practical, evidence-based approaches over theoretical elegance alone, reflecting his commitment to improving the research-practice connection in software engineering.

Thomas VogelView profile

Researcher

• Software Engineering
• Search-Based Software Engineering
• Model-Driven Engineering
• +5 more

Thomas Vogel is a postdoctoral researcher in the Software Engineering Group at Humboldt-Universität zu Berlin, where he teaches courses including Adaptive Systems, Software Engineering, and Methods and Models of System Design. Previously, from October 2021 to September 2022, he served as stand-in professor for Empirical Software Engineering at Paderborn University. His academic journey includes a Ph.D. with summa cum laude from the Hasso Plattner Institute at University of Potsdam (2018) and undergraduate studies in Information Systems at University of Bamberg with distinction. Dr. Vogel's research focuses on the intersection of software engineering with self-adaptive and autonomous systems. His work bridges search-based and model-driven software engineering with control theory to create robust adaptive systems. He has made significant contributions to quality assurance of self-adaptive software, particularly in the areas of uncertainty reduction, runtime verification, and formal synthesis of controllers. His research has practical applications in mobile robot navigation, server infrastructure management, and scientific computing domains. His publications reveal a strong emphasis on formal methods for adaptive systems, with a notable trend toward integrating machine learning techniques with traditional control theory approaches. Recent work demonstrates increased focus on industrial applicability of self-adaptation research, uncertainty management, and evaluation methodologies for adaptive systems. His 2015 paper "Software Engineering meets Control Theory" received the SEAMS 10-Year Most Influential Paper Award in 2025, highlighting the lasting impact of his contributions to the field. Among his notable recognitions are the Facebook Testing and Verification Research Award, the Karsten Schwan Best Paper Award at ICAC 2017, and the Best Paper Award at SEAMS 2024 for "Formal Synthesis of Uncertainty Reduction Controllers." He has served on program committees for major conferences including ASE, ICSE, ICST, and SEAMS, and was Program Committee Co-Chair for the Research Track at SEAMS 2025. Dr. Vogel actively mentors PhD students, with recent successful defenses by Arut Prakash Kaleeswaran ("Explanation of the Model Checker Verification Results") and Sona Ghahremani ("Incremental Self-Adaptation of Dynamic Architectures Attaining Optimality and Scalability"). His research has been supported by the Deutsche Forschungsgemeinschaft (DFG), including the "Controlling Search-Based Test Generation and Program Repair" project. He is also involved in the collaborative research center "FONDA: Foundations of Workflows for Large-Scale Scientific Data Analysis."

Yingfei XiongView profile

Associate Professor

• Software Engineering
• Programming Languages
• Program Synthesis
• +4 more

Yingfei Xiong is an Associate Professor at Peking University specializing in Software Engineering and Programming Languages. His research focuses on program synthesis, automated program repair, and software analysis techniques with extensive contributions to top-tier conferences including ASE, ICSE, PLDI, and SPLASH/OOPSLA. Dr. Xiong's primary research interests include: Program Synthesis and Inductive Programming Automated Program Repair and Bug Fixing Static and Dynamic Program Analysis Machine Learning Applications in Software Engineering Compiler and Language Implementation Techniques Software Testing and Verification His publication trends show a strong shift toward neural approaches in program repair, with significant integration of large language models in synthesis tasks since 2023. His work bridges theoretical foundations with practical applications, evidenced by industrial collaborations such as the Alibaba case study on static taint analysis. Recent contributions include GrammarT5 for code generation, Tare for type-aware neural repair, and Equality Saturation techniques guided by LLMs. At Peking University, Dr. Xiong is affiliated with the School of Electronics Engineering and Computer Science (SEI) as indicated by his institutional website domain. He actively contributes to the academic community through program committee service for major software engineering conferences and mentoring student researchers.

Shun ShiramatsuView profile

Professor

• Natural Language Processing
• Knowledge Graph
• Civic Tech
• +3 more

Shun Shiramatsu is a Professor at the Graduate School of Engineering, Nagoya Institute of Technology, where he also serves as Director of the Center for Community Creation Education and Research. Since August 2024, he has held a concurrent position as Specially Appointed Professor in the Civic Tech and Design Science Creation Endowed Research Division at the Center for Spatial Information Science, The University of Tokyo. In April 2024, he established Socianotter Inc. to implement his laboratory's research results in society. Dr. Shiramatsu has been actively involved in civic tech activities with Code for Nagoya since 2014. Dr. Shiramatsu earned his Master's degree in Information Science from Tokyo University of Science in 2003 and his PhD in Informatics from Kyoto University in 2008. His academic journey began as a JST CREST Research Assistant at AIST in 2003, followed by positions as JSPS Research Fellow (DC2) in 2007, JSPS Research Fellow (PD) in 2008, Assistant Professor at Nagoya Institute of Technology in 2009, Associate Professor and head of the Shiramatsu Laboratory in 2015, and Professor in 2021. His research focuses on civic tech support and consensus building using natural language processing and knowledge graphs. Dr. Shiramatsu's work bridges the gap between advanced AI technologies and practical community applications, particularly in supporting democratic processes and citizen engagement. His recent research has increasingly incorporated large language models to enhance discussion structuring, information recommendation, and consensus building in both physical and digital spaces. He has developed systems for structuring oral discussions, estimating optimal timing for information recommendations, and creating coaching agents for job-hunting students. Dr. Shiramatsu's publication record shows a clear evolution from foundational work in web technologies and knowledge representation toward increasingly sophisticated applications of AI for civic engagement. His recent work heavily features large language models applied to community building, discussion support, and consensus formation, demonstrating how cutting-edge AI can address real-world social challenges through practical implementations. Excellence Award in the Dataset category at the LOD Challenge Japan 2013 Excellence Award in the Application category at the LOD Challenge Japan 2018 Grand Prize at the Kyoto University Summer Design School 2022 Best Student Paper Award at the 2nd International Workshop on Democracy and AI (2023) Outstanding Performance Award at the Linked Open Data Challenge Japan 2018 Dr. Shiramatsu has served as principal investigator on multiple research projects funded by JST, NEDO, and the Japan Society for the Promotion of Science. His research has been applied in various civic tech initiatives, including the development of systems for public collaboration, consensus building, and regional revitalization. Through Socianotter Inc., he is translating academic research into practical solutions that directly benefit communities. His leadership extends to chairing the Japanese Society for Artificial Intelligence's Citizen Co-Creation Intelligence Research Group (SIG-CCI) and serving as Secretary for the IEICE Special Research Committee on 'Agreement and Co-creation'.

Dimitris FassouliotisView profile

Professor

• Particle Physics
• High-Energy Physics
• Collider Experiments
• +7 more

Prof. Dimitris Fassouliotis is a Professor in the Department of Physics at the National and Kapodistrian University of Athens. He specializes in Experimental Elementary Particle Physics, with a focus on collider experiments at CERN. He has been a member of the DELPHI collaboration at LEP and the ATLAS collaboration at LHC since 1998. His research includes precise Standard Model (SM) validation, Higgs boson discovery and property measurements, and BSM searches. He has contributed to the construction of the ATLAS muon spectrometer and developed gas micro-pattern detectors. Education: Bachelor's degree in Electronic Engineering (National Technical University of Athens, 1989), PhD in Experimental Particle Physics (National Technical University of Athens, 1994), and didactics qualification (PA.TE.S, 1995). His postdoctoral work at INFN Trieste and NCSR Demokritos focused on LEP results, including W mass measurements and gauge boson coupling studies. Research interests span collider-based studies of SM and BSM physics, detector development, and precision measurements of Higgs properties. He has supervised 4 PhD, 7 MSc, and 21 undergraduate theses. His recent work includes Higgs boson cross-section analyses, searches for heavy BSM bosons, and R&D on resistive Micromegas detectors for LHC upgrades. Lab affiliations include the ATLAS collaboration, where he contributed to detector construction, software development, and physics analysis. His work emphasizes experimental techniques and data-driven discoveries in high-energy physics.

Jimmy LinView profile

Professor

• Information Retrieval
• Natural Language Processing
• Data Management
• +2 more

Jimmy Lin is a Professor and David R. Cheriton Chair at the University of Waterloo's David R. Cheriton School of Computer Science. His research focuses on information retrieval, natural language processing, and data management, with applications in large-scale data analysis and AI systems. Previously at the University of Maryland, he earned his PhD from MIT in 2004. PhD in Electrical Engineering and Computer Science, MIT (2004) MS, Mechanical Engineering, Penn State University BSc, Mechanical Engineering, Addis Ababa University His research integrates information retrieval, NLP, and data management to build tools for understanding large datasets. Current interests include neural retrieval models, cross-lingual systems, and efficient search algorithms. Recent publications focus on retrieval-augmented generation (RAG), model efficiency, multilingual NLP, and evaluation methodologies. Trends include hybrid dense-sparse retrieval systems, robustness improvements, and practical toolkits like Anserini and Tevatron. ACM Fellow SIGIR Academy ACL Fellow SAE Ralph R. Teetor Educational Award (2014) NSF CAREER Award (2011) He advises 36+ PhD/Master's students and leads multiple industry collaborations (Primal, RSVP, Twitter). His lab develops open-source tools for retrieval and NLP, focusing on scalable and multilingual applications.

Nitin NaikView profile

Senior Lecturer

• Cybersecurity
• Artificial Intelligence
• Data Science
• +3 more

Nitin Naik is a Senior Lecturer in Software Engineering & Cybersecurity at Aston University's School of Computer Science and Digital Technologies, part of the College of Engineering and Physical Sciences. He holds a PhD in Computer Science from Aberystwyth University and has diverse qualifications including an MBA in Finance, PGCTHE in Higher Education Teaching, and multiple degrees from Devi Ahilya University. His research focuses on cybersecurity, artificial intelligence, data science, and IoT. He teaches courses like CS21DE (Data Encryption) and CS22DS (Distributed Systems). Research emphasizes threat modeling (STRIDE, DREAD, etc.), attack tree analysis with fuzzy logic, federated learning, and malware detection using fuzzy hashing/YARA rules. He has published 125+ works including analyses of OT system defenses, privacy-preserving techniques in tourism data fusion, and AI chatbot applications. Professional memberships include SFHEA and IEEE Computational Intelligence Society. He supervises PhD students in areas like secure LLMs, quantum search-based malware detection, and AI-based cyberattack modeling. His work integrates computational intelligence with cybersecurity solutions, including honeypot systems and blockchain-based identity management.

• Neural Computation
• Spiking Neural Networks
• Auditory System Processing
• +5 more

Dr. Dan Goodman is a Senior Lecturer in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. He leads the Neural Reckoning Group, focusing on uncovering principles of neural computation using precisely timed spikes. His work bridges computational neuroscience and artificial intelligence, emphasizing spiking neural networks and sensory processing, particularly in auditory systems. Key tools include the Brian simulator, a widely used spiking neural network framework. Research affiliations include the Artificial Intelligence Network, Centre for Neurotechnology, and Georgina Mace Centre for the Living Planet. His interests span neural computation, neuromorphic engineering, and interdisciplinary applications of neuroscience. Recent work explores multisensory fusion, neural modularity under resource constraints, and adaptive temporal coding. Publications highlight advancements in spiking network models, sound localization, and neural simulation techniques. Collaborations emphasize open science and online conference formats, as seen in initiatives like Neuromatch. No explicit awards or grant details are provided, though his tool development and theoretical contributions suggest significant academic impact.

Tom Van CutsemView profile

Associate Professor

• Distributed Systems
• Software Security
• Blockchain
• +5 more

Tom Van Cutsem is an Associate Professor of computer science at KU Leuven in Belgium, where he is affiliated with the Distributed and Secure Software (DistriNet) research group within the Faculty of Engineering Science and Department of Computer Science. He also maintains an affiliation with Nokia Bell Labs, where he previously served as a Research Department Head with research interests in decentralized systems, IoT, stream processing and machine learning for software engineering. His academic career includes a previous position as Assistant Professor at Vrije Universiteit Brussel from 2010 to 2014. Dr. Van Cutsem holds a PhD in Computer Science from Vrije Universiteit Brussel. His educational background has provided the foundation for his expertise in distributed systems, programming languages, and software security. His primary research interests span Distributed Systems , Software Security , Blockchain , and Programming Language Design . With the DistriNet research group, he pursues work on decentralized systems, IoT, stream processing, and machine learning for software engineering. His research focuses on making distributed systems more secure, reliable, and accessible, with particular attention to blockchain technology and Web3 infrastructure. He is a founding member of IFIP WG 2.16 on Programming Language Design. His recent publications demonstrate a strong focus on blockchain security and infrastructure, with numerous papers on Web3, smart contracts, and decentralized systems from 2023-2025. His work addresses critical challenges in blockchain validation, smart contract security, and Web3 infrastructure, reflecting his commitment to making decentralized systems more secure and accessible to low-resource participants. Several of his current projects focus on secure middleware for smart contracts and scalable infrastructure for self-sovereign applications. Dr. Van Cutsem supervises PhD students including Weihong Wang, with whom he has co-authored multiple recent publications. He is currently leading several research projects including SODISA (Scalable Software Development and Infrastructure for Self-sovereign Applications), Secure Sandboxing for WebAssembly-based Smart Contracts, and Programming languages for privacy-preserving and verifiable computing, with funding extending through 2028-2029. He leads research within the Distributed and Secure Software (DistriNet) group at KU Leuven, which focuses on creating secure and dependable distributed systems. His team works on cutting-edge challenges in blockchain infrastructure, smart contract security, and decentralized application development, with connections to both academic and industrial partners including Nokia Bell Labs.

Robert LoftinView profile

Lecturer

• Ad Hoc Human-AI Cooperation
• Human-AI Alignment
• Human-in-the-loop Machine Learning
• +6 more

Dr. Robert Loftin is a Lecturer in Machine Learning at the University of Sheffield’s School of Computer Science. His research focuses on Ad Hoc Human-AI Cooperation, Human-AI Alignment, and Human-in-the-loop Machine Learning, with expertise in Deep Reinforcement Learning and Multi-Agent Systems. He holds a PhD from North Carolina State University (2019) and a Bachelor’s from Georgia Tech (2011). Postdoctoral work includes roles at Microsoft Research Cambridge and TU Delft, exploring AI applications in gaming and multi-agent cooperation. His research interests span AI ethics, human-robot interaction, and games, with a strong emphasis on practical applications of interactive learning. Recent work includes advancements in entropy-based coordination for multi-agent systems (2023–2025) and theoretical studies on cooperation in socially rational agent populations. He contributes to the Machine Learning research group and has published extensively in top venues like NeurIPS, AAMAS, and AAAI. Publications highlight innovations in curriculum design, exploration strategies, and adaptive feedback mechanisms. His work bridges theoretical foundations and real-world applications, particularly in cooperative and competitive multi-agent environments. Current projects explore safe team coordination and scalable strategies for high-dimensional state spaces.

• Cybersecurity
• Artificial Intelligence in Education
• Information Systems Security
• +3 more

Dr. Bernard Vukelić is a Professor of Professional Studies at Veleučilište u Zagrebu (Collegium Carolinum), specializing in cybersecurity, AI applications in education, and information systems security. He holds a Doctorate in Sociology (Dr. Sc.) and focuses on topics like digital transformation, phishing detection, and lifelong learning strategies. His research bridges theoretical frameworks with practical applications in office automation, web application security, and AI-driven solutions for educational assessment. Education: PhD in Sociology (Dr. Sc.) His research interests include: Cybersecurity measures for critical infrastructure AI ethics and implementation in higher education Automated office processes using ChatGPT Phishing attack recognition and mitigation Content management system vulnerabilities Indoor positioning system analysis Recent Research Trends: His work from 2024 emphasizes AI's role in office automation and cybersecurity resilience, while 2023 papers address AI-driven examination proctoring and phishing detection methodologies. Earlier studies explored backend web frameworks and biometric algorithms. He teaches courses like Tools for Digitalization of Business and Security of Information Systems , reflecting his practical focus on industry-relevant technical skills.

Miriam LeeserView profile

Professor

• Reconfigurable Computing
• FPGA Accelerators
• Wireless Communications
• +4 more

Miriam Leeser is a Professor and Associate Chair of Research in the Department of Electrical and Computer Engineering at Northeastern University. She leads the Reconfigurable and GPU Computing Lab, focusing on heterogeneous architectures, FPGA accelerators, and their applications in wireless communications, machine learning, and data privacy. Her work emphasizes practical implementations of theoretical advancements, such as cloud-edge computing frameworks and secure multi-party computation using FPGAs. Leeser holds a PhD in Computer Science from the University of Cambridge and a BS in Electrical Engineering from Cornell University. She has been at Northeastern since 1996 and is a Senior Member of both the IEEE and ACM. Notable recognitions include the National Science Foundation Young Investigator Award and her status as a Charter Member of the IEEE Computer Society Distinguished Contributor Program. Her research portfolio includes projects funded by the NSF on topics like resilient cloud infrastructure (CAREFREE), cross-layer wireless coexistence, and FPGA-enabled secure computation. The Reconfigurable Computing Lab develops tools and libraries to simplify accelerator usage, particularly for emerging applications in IoT, medical imaging, and 5G/6G systems. Key Projects: CAREFREE, P4-based FPGA Testbeds, Secure Function Evaluation via FPGAs, MIMO Systems Acceleration Grants: Over $2M in NSF awards for cloud infrastructure and cybersecurity Labs: Reconfigurable and GPU Computing Lab (RCL) Leeser’s publications span FPGA optimization, hardware-software co-design, and privacy-preserving technologies. Recent work highlights include accelerating large language models with token-adaptive quantization and low-latency optical wireless transceivers.

Guido TackView profile

Associate Professor

• Combinatorial Optimization
• Constraint Programming
• Programming Languages
• +3 more

Guido Tack is an Associate Professor in the Department of Data Science & Artificial Intelligence at Monash University's Faculty of Information Technology. He leads development of the MiniZinc constraint modeling language and is a core developer of Gecode, a leading constraint programming library. His research focuses on combinatorial optimization, constraint solver architecture, and industrial applications, extending to programming languages and computational logic. Education: PhD (Dr.-Ing.) in Computer Science from Saarland University (2009), Diplom Informatiker (MSc equivalent) from Saarland University (2003). Professional experience includes postdoctoral roles at NICTA, Saarland University, and KU Leuven before joining Monash in 2012 as a Lecturer and Monash Larkins Fellow. Research interests span constraint programming, optimization algorithms, and their applications. Recent projects include improving the MiniZinc ecosystem, optimizing metering systems for smart water networks, and quantum information technology. He has secured over 13 major research grants, including leadership in the ARC Training Centre for Optimization Technologies. Teaching: Chief Examiner for FIT5170, FIT9135, FIT5216; Lecturer for FIT1047, FIT4010, and others. Editorial Roles: Co-Editor-in-Chief of Constraints journal (2015), active in ACM and Association for Constraint Programming. Awards: 2024 Eureka Prize finalist, 2017 FIT Dean's Award, 2010 Doctoral Research Award. Publications span constraint solving techniques, optimization algorithms, and educational tools. Recent work emphasizes real-time path planning, bi-objective search, and energy-efficient systems. He contributes to both academic conferences and industry-focused projects, bridging theoretical research with practical applications.