جست‌وجوی استاد و پژوهشگر

• Combinatorial Optimization
• Operations Research
• Stochastic Programming
• +۵ مورد دیگر

Hanyu Gu is a Senior Lecturer in the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS), part of the Faculty of Science. He holds a PhD in Power Engineering and Automation from Shanghai Jiao Tong University (1999) and has extensive industry experience in telecommunications, airline optimization, and mining. His research focuses on combinatorial optimization, decomposition methods, stochastic programming, and machine learning applications. Notable awards include second place in the 2020 ROADEF competition. He collaborates with institutions like the UTS Transportation Research Centre and has contributed to projects such as optimisation engines for airline management and underground mining algorithms. Current research explores hybrid algorithms, Bayesian optimisation, and scheduling under uncertainty. Education: Bachelor in Industrial Automation, Shanghai Jiao Tong University (1994) Master in Control Theory and Application, Shanghai Jiao Tong University (1997) PhD in Power Engineering and Automation, Shanghai Jiao Tong University (1999) Industry Experience: ZTE (1999–2001): Senior Wireless Communication Engineer CTI, Melbourne (2007–2011): Airline Management Optimisation Researcher NICTA (2011–2013): Underground Mining Optimisation Researcher Grants: ARC Linkage Project LP0883855 (2008–2012): Developed optimisation tools for transportation crewing, valued at $840,000. Research interests span decomposition methods for large-scale problems (e.g., airline scheduling), stochastic programming for resource sharing, and hybridisation of mathematical programming with constraint programming. Recent work includes Bayesian optimisation for knapsack problems and relax-and-solve algorithms for project scheduling. His articles frequently address optimisation in logistics, healthcare, and transportation, emphasizing practical industry applications and algorithmic innovation. Awards: Second place in the ROADEF 2020 competition for maintenance planning solutions. Advising & Grants: Supervises Masters and PhD students in operations research and optimisation. Collaborates with Ausgrid, UGL, and ANC on optimisation projects (e.g., employee training timetabling, logistics). Active in the Optimisation Group of UTS Transportation Research Centre, he bridges academic research with real-world challenges in scheduling, logistics, and resource management. Ongoing efforts include advancing metaheuristics and integrating machine learning with traditional optimisation techniques.

Benjamin Cerfontaineمشاهده پروفایل

دانشیار

• Offshore Renewable Energy
• Geotechnical Engineering
• Numerical Modelling
• +۳ مورد دیگر

Dr Benjamin Cerfontaine is an Associate Professor in Geotechnical Engineering at the University of Southampton. His research focuses on geotechnical solutions for offshore renewable energy infrastructure, particularly floating and bottom-fixed offshore wind turbines. He specializes in numerical and physical modeling techniques such as finite elements, DEM, and centrifuge testing, aiming to uncover fundamental mechanisms governing foundation behavior. Education: PhD in Geotechnical Engineering from the University of Liège (2014), followed by postdoctoral research at the University of Dundee (2017–2020). Research Interests: Offshore renewable energy systems, constitutive modeling of geomaterials, and innovative anchoring solutions. Projects: Leads the ROBOCONE and TAILWIND projects, focusing on robotic ground characterization and sustainable anchor designs. Key achievements include the Geotechnical Research Medal (2023) and the Bright Spark Lecture Award (2023). He supervises three PhD students and actively collaborates with industry partners on offshore energy infrastructure. Dr. Cerfontaine is part of the Infrastructure Group and the Southampton Marine and Maritime Institute, contributing to interdisciplinary research on ocean energy and coastal communities.

Sven Scheweمشاهده پروفایل

استاد

• Automata Theory
• Game Theory
• Formal Verification
• +۱۲ مورد دیگر

Sven Schewe is a Professor in the Department of Computer Science at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He leads the AI Section and is a founding member and former leader of the Verification Group. He also has secondary affiliations with the Algorithms, Complexity Theory and Optimisation Group and the Institute for Risk and Uncertainty. Research Interests: His research centers on automata theory and game theory, particularly their applications in the verification and synthesis of reactive and safety-critical systems. He investigates infinite-duration games, automata over infinite words and trees, and develops algorithms and tools for automated verification, synthesis, and learning of optimal control strategies. His work extends to reinforcement learning with formal guarantees, cyber-physical systems, and AI safety. Recent Research Trends: His recent publications demonstrate a strong integration of formal methods with machine learning, particularly in adversarial training, neural network robustness, and model-free reinforcement learning under omega-regular objectives. He also applies formal reasoning to interdisciplinary domains such as chemical space exploration and materials science. Scientific Awards: Finalist for the ERCIM Cor Baayen Award 2010 Dr. Eduard Martin Preis 2009 GI Dissertation Award 2008 Advising and Grants: He actively supervises numerous PhD students and postdoctoral researchers. He is Principal Investigator (PI) or Co-Investigator (CI) on multiple major grants, including EPSRC Programme Grants, Royal Society Fellowships, and Horizon Europe projects. His funded research spans topics such as game theory, verification, synthesis, reinforcement learning, and risk analysis. He has hosted visiting researchers and collaborated internationally with institutions in Germany, France, India, Taiwan, and the US. Labs and Teams: He co-founded and led the Verification Group and previously led the AI Section at the University of Liverpool. These groups focus on formal methods, automata, games, and their applications in AI and safety-critical systems.

Holger H. Hoosمشاهده پروفایل

استاد

• Human-centred AI
• AI for Good
• AI for All
• +۶ مورد دیگر

Holger H. Hoos is the Alexander von Humboldt Professor of AI at RWTH Aachen University (Germany), Professor of Machine Learning at Universiteit Leiden (Netherlands), and Adjunct Professor of Computer Science at the University of British Columbia (Canada). He also serves as Faculty Associate at the Peter Wall Institute for Advanced Studies, UBC. Affiliations: RWTH Aachen University, Universiteit Leiden, University of British Columbia Research interests include methodological and technological advances in human-centred AI, AI for Good, and AI for All. His work focuses on improving AI efficiency and robustness, automated algorithm design (notably Programming by Optimisation and AutoML ), and interdisciplinary applications of AI. He leads the AI Center at RWTH Aachen. Scientific leadership includes roles as president of the European Association for AI (EurAI), co-founder of CLAIRE (now CAIRNE), and past president of the Canadian Association for Artificial Intelligence (CAIAC). His book on Stochastic Local Search (with Thomas Stützle) remains a foundational text in the field. Awards and recognition include: Alexander von Humboldt Professorship (2021) Fellow of ACM, AAAI, and EurAI German AI Innovation Prize (2021) for CLAIRE and ELLIS

Salman Nazirمشاهده پروفایل

استاد

• Human Factors
• Training Methods
• Virtual Reality Training Simulators
• +۹ مورد دیگر

Salman Nazir is Professor at the University of South-Eastern Norway (USN) , Faculty of Technology, Natural Sciences and Maritime Sciences, Department of Maritime Operations. He heads the Training and Assessment Research Group (TARG) and is Scientific Leader of the national Centre of Excellence in Maritime Simulator Training and Assessment (COAST). Since 2019 he has held the rank of Professor, after serving as Associate Professor from 2015 and earlier post-doctoral and lecturer roles in Norway, Italy, South Korea and Pakistan. Education PhD in Industrial Chemistry and Chemical Engineering ( cum laude ), Politecnico di Milano, Italy, 2011–2013 MSc in Chemical Engineering (Process System Engineering), Hanyang University, South Korea, 2007–2009 BSc in Chemical Engineering, Bahauddin Zakariya University, Pakistan, 2002–2006 Research Interests Prof. Nazir’s work sits at the intersection of Human Factors, Safety and Simulation Technology . He investigates how immersive virtual- and augmented-reality simulators, novel training syllabi and evidence-based performance indices can enhance operator competence and safety in complex maritime and process-industry systems. Concepts such as Distributed Situation Awareness , multi-criteria decision making , accident analysis and learning process optimisation are central to his multidisciplinary agenda, which actively involves cognitive scientists, computer scientists, maritime practitioners and industrial stakeholders. Research Trends & Article Overview Across more than 80 peer-reviewed outputs, a clear trajectory emerges: early focus on process-industry training simulators and KPI development evolved into maritime-centric studies on simulator fidelity, VR-based education, and human-automation interaction in autonomous shipping. Recent work (2019–2021) emphasises systematic reviews and comparative European studies, validating VR-headset efficacy, performance-assessment frameworks, and sociotechnical implications of increased automation. Honours & Awards COAST designated one of 12 national Centres of Excellence in Education (SFU) by DIKU, Norway Coordinator/Leader, EU Horizon 2020 project ENHANCE (multi-million NOK) 400 000 NOK MARKOM 2020 Workshop grant PhD cum laude , Politecnico di Milano Young-researcher grants, Politecnico di Milano (2011 & 2013) Merit scholarships, Hanyang University & Korean Government Advising & Grant Portfolio Prof. Nazir currently supervises 6 master students and 1 PhD candidate in “Automated Performance Assessment in Maritime Operations”, while co-supervising additional PhD students at Liverpool John Moores University. He has successfully graduated 3 master and 6 bachelor students . External funding includes EU Horizon 2020, Norwegian SFU scheme, MARKOM 2020, Maritime Technology and Innovation (MTDI) and multiple Italian national grants. Labs & Collaborative Networks He leads TARG at USN, acts as Scientific Leader of COAST , and collaborates with leading international scholars such as Prof. Zaili Yang (Liverpool John Moores), Prof. Annette Kluge (University of Duisburg-Essen), Prof. Davide Manca (Politecnico di Milano) and Prof. Paulo Carvalho (UFRJ, Brazil). These partnerships span computer science, cognitive psychology, maritime logistics and safety engineering, ensuring a truly interdisciplinary research ecosystem.

• Theoretical Computer Science
• Formal Program Verification
• Parametrised Algorithms
• +۴ مورد دیگر

Amir Kafshdar Goharshady is an Associate Professor of Computer Science at the University of Oxford and a Tutorial Fellow at St Catherine's College. His research focuses on theoretical computer science, including formal program verification, parametrised algorithms, compiler optimisation, and blockchain technology. He leads the ALPACAS research group, which explores algorithms, logic, program analysis, cryptocurrencies, and smart contracts. Education: PhD in Theoretical Computer Science from IST Austria (supervised by Prof. Krishnendu Chatterjee), MSc in Computer Science (Systems) from Georgia Tech, and undergraduate studies in Maths and Computing at the Universities of London and Yazd. Prior to Oxford, he was an Assistant Professor at HKUST and maintains a visiting role at IIT Bombay. Research Interests: Formal methods for program verification Algorithmic efficiency in compiler optimisation Decentralized systems and blockchain applications Game-theoretic approaches to secure protocols Advising and Grants: Supervises over 20 PhD and Master’s students, with a rigorous selection process emphasizing problem-solving skills. Leads an international research group with multiple grants and collaborations. Former roles include organizing high school Olympiads and founding the Sundar STEM School in Pakistan. Labs/Teams: ALPACAS group, known for its work on smart contract optimization, blockchain security, and parametrised algorithm design. Regularly hosts visiting researchers such as Petr Novotný and Thomas Henzinger.

Dan Steinbergمشاهده پروفایل

عضو هیئت علمی

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +۷ مورد دیگر

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

Deborah Do-Rosario-Benrosمشاهده پروفایل

مدرس ارشد

• Design and Computation
• Shape Grammars
• Mass Customisation
• +۵ مورد دیگر

Dr. Deborah Do-Rosario-Benros is a Senior Lecturer in Technical Studies and Representation at the University of East London (UEL), serving as a Cluster Leader in the Department of Architecture & Visual Arts within the School of Architecture, Computing and Engineering. She is a UK-registered architect (ARB/RIBA) with advanced research expertise in computational design methodologies. Educational Background: Doctorate from University College London (The Bartlett School) Master's in Architecture from the University of Lisbon Visiting student at Massachusetts Institute of Technology (MIT) Her research focuses on Design and Computation , Shape Grammars , and Mass Customisation in architecture. Recent work explores 3D printing , robotic manufacturing , and modular design for sustainable housing solutions. Research Publications Trends: Her 2024-2023 articles analyze computational frameworks like multilingual shape grammars, apply robotic construction to micro-housing, critically review 3D printing in large-scale construction, and investigate modular systems using bent metal pipes and space-filling solids. Collaborative co-design with AI and participation in international competitions like the Orange County Sustainability Decathlon are recurring themes. Professional Experience: Prior roles include teaching at the University of Lisbon, visiting faculty at Cornell University, and professional practice at architectural firms Foster and Partners and Arup. She has presented at major conferences including SIGraDi, ZEMCH, and SEEDS. Labs and Teams: As Cluster Leader at UEL, she coordinates research efforts in architecture and computational design, collaborating with institutions like MIT and Cornell.

• Computational Design
• Parametric Design
• Digital Fabrication
• +۵ مورد دیگر

Eduardo Costa serves as a Senior Lecturer in Computational Architecture at the University of the West of England's College of Arts, Technology and Environment, where he bridges computational methodologies with architectural design and fabrication. His academic credentials include a PhD in Architecture specializing in Digital Design and recognition as a Fellow of the Higher Education Academy (FHEA). Research centers on parametric and procedural modeling for automating architectural shape generation, analysis, and optimisation. Costa integrates advanced digital fabrication techniques—including stone CNC milling, ceramic 3D printing, and concrete reconfigurable forming—with design workflows, while developing user-friendly software tools to implement these processes. Key research collaborations include: Bath University Cambridge University University of Dundee Pennsylvania State University University of Lisbon No scientific awards were documented in the source material. Information regarding student advising, research grants, and laboratory facilities was either not provided or insufficiently detailed for inclusion.

• Digital education
• Sociology of technology
• Education policy
• +۱۱ مورد دیگر

Professor Sandra Leaton Gray is a distinguished academic at University College London (UCL), where she serves as Professor of Education Futures at the UCL Institute of Education (IOE) within the Curriculum, Pedagogy & Assessment department. She also holds significant governance roles as a member of the UCL Council, the main governing body of the university, and as a Senior Member of Wolfson College, University of Cambridge. Her academic career spans multiple institutions including the University of East Anglia and the University of Cambridge, with a consistent focus on the intersection of education, technology, and social policy. Professor Leaton Gray's educational background includes: Doctor of Philosophy from University of Cambridge (2004) Master of Philosophy from University of Cambridge (1999) ATQ07 - Holder of a PGCE in higher education, secondary education, further education, lifelong learning from Roehampton University (1993) Her research interests center on the sociology of technology and its impact on children and young people, with particular emphasis on AI ethics in educational contexts. She explores education futures, the future of assessment, educational testing, and curriculum reform through a critical lens that considers digital ethics, children's biometrics, and the knowledge economy. Her work addresses pressing contemporary issues including the recovery from the COVID-19 pandemic's impact on education systems, with a strong commitment to addressing educational inequality and promoting democratic values in technologically mediated learning environments. Professor Leaton Gray approaches these topics through the frameworks of sociology, policy analysis, and educational futures thinking, always with attention to the lived experiences of learners in digital contexts. Professor Leaton Gray's extensive publication record reveals a clear trajectory of increasing focus on digital technologies in education, particularly AI and biometrics, while maintaining her foundational interest in educational sociology and policy. Her recent work demonstrates a sophisticated understanding of how technological systems intersect with power structures in educational assessment and how these dynamics impact equity and access. The growing emphasis on AI in her recent publications reflects both the rapid development of educational technologies and her strategic positioning at the forefront of critical analysis of these innovations. Professor Leaton Gray has received numerous professional recognitions: Fellow of the College of Teachers (since 2007) Fellow of the Incorporated Society of Musicians (since 1993) Fellow of the London College of Music (since 1990) Principal Fellow of the Higher Education Academy (since 2005) Senior Member of Wolfson College, University of Cambridge (since 2006) As an advisor and consultant, Professor Leaton Gray has significantly influenced education policy at national and international levels, working with organizations including the UN, UK Government, EU Commission, OECD, Royal Colleges of Medicine, British Dental Association, International Baccalaureate Organisation, BBC, Channel 4, Pearson, and the Biometrics Institute. Her current role as Researcher in Residence at the University of Passau, Germany (2024-2025) leading the project 'Education Futures: Visions of Artificial Intelligence, Biometrics and Digital Surveillance within Bavarian Schools' demonstrates her ongoing commitment to shaping educational practices through research. She has also directed significant projects including the 'My Life Online' research project, the 'Future of the Artificially Intelligent Examination,' and the 'Optimising 2020 secondary school transition processes during the Covid-19 pandemic.' Professor Leaton Gray is actively involved in several important governance and advisory roles that extend her influence beyond traditional academic boundaries. She serves as a member of the UK Government's College of Experts in the Ministry of Culture, Media and Sport (DCMS), the UK Ministry of Science, Innovation and Technology, the Privacy Expert Group of the Biometrics Institute, the UK Cabinet Office's International Best Practice Advisory Group, and as an expert advisor to the UK Government's Centre for Data Ethics and Innovation. Her participation in the OECD's ONE AI working group further demonstrates her international standing in the field of educational technology policy.

• Empirical Software Engineering
• Requirements Engineering
• Applied Natural Language Processing
• +۷ مورد دیگر

Dr. Chetan Arora is a Senior Lecturer in Software Engineering at Deakin University's School of Information Technology, part of the Faculty of Science Engineering and Built Environment. He holds a PhD from the University of Luxembourg where he received the best PhD thesis award in the ICT domain. His research focuses on applied Artificial Intelligence in Software Engineering, with particular emphasis on Empirical Software Engineering, Requirements Engineering, and Applied Natural Language Processing. PhD in Computer Science from University of Luxembourg Masters in Software Engineering from Technische Universitat Kaiserslautern (Germany) Bachelors in Engineering (CS) from Thapar University (India) Arora's research interests center on the intersection of AI and Software Engineering, particularly how machine learning and natural language processing can enhance software development processes. His work explores requirements engineering, test automation, software trustworthiness, and human-centric software development. He investigates how large language models can be effectively deployed for tasks like test case generation, requirements analysis, and traceability. His recent publications reveal a strong focus on practical applications of AI in software engineering, with numerous studies examining the real-world implementation challenges and benefits. His publication record shows significant activity in top software engineering venues, with a notable emphasis on AI applications in software engineering processes. His recent work demonstrates expertise in retrieval-augmented generation systems, requirements-driven testing, and human-centric software development approaches. The publications collectively highlight his focus on bridging theoretical AI advancements with practical software engineering challenges. Best Ph.D. thesis award in the ICT domain at University of Luxembourg Arora actively supervises doctoral students working on cutting-edge topics including satellite communication systems, extended reality applications, and human-centered AI requirements engineering. His industry collaborations include work with Department of Defence on projects like Contextually Situated Anomaly Detection and Planning and Optimisation of Resources in Defence Satellite Communication Systems. He previously worked at SES Satellites on applied AI for IoT and Satcom, and as an FNR-PPP research fellow at the University of Luxembourg in software quality assurance. His laboratory work focuses on developing practical AI solutions for software engineering challenges, particularly in requirements engineering and test automation. Current projects involve multi-orbit satellite constellation optimization, dynamic radio resource management, and extended reality enabled human-centric requirements engineering.

• Control Engineering
• Aerospace Systems
• Energy Generation Optimization
• +۴ مورد دیگر

Dr Tim Breikin is a Reader in Control Engineering at Sheffield Hallam University's Department of Engineering and Mathematics, part of the College of Business, Technology and Engineering. He began his UK academic career as a Lecturer at the University of Manchester in 2004, following nine years of industrial systems research focusing on modelling, control, and optimization. His research emphasizes advanced control techniques for aerospace and energy systems, including collaborations with industry on low-fuel-consumption aero engines and low-emission gas turbines. He has led significant grants, such as the EPSRC-funded 'real-time modelling for condition monitoring and performance optimisation of gas turbine engines' and contributed to energy demand reduction proposals in paper manufacturing. Recent work spans Ohmic heating technology in food processing, fluid dynamics modelling, and flood prediction systems using machine/deep learning. These projects reflect his expertise in bridging theoretical control engineering with practical industrial and environmental applications.

• Multi-Objective Optimisation
• Nature Inspired Optimisation Algorithms
• Multidisciplinary Design Optimisation Under Uncertainties
• +۷ مورد دیگر

Dr. Edmondo Minisci is a Senior Lecturer in Mechanical and Aerospace Engineering at the University of Strathclyde, part of the Faculty of Engineering. He leads the Intelligent Computational Engineering Laboratory (ICE-Lab) within the Aerospace Centre of Excellence. With over 25 years of experience, his expertise spans model-based analysis and design optimization of complex mechanical/aerospace systems, including aeronautical vehicles, wind turbines, and sustainable energy systems. He actively supervises PhD students in areas like multi-fidelity methods, intelligent control, and sustainable agriculture engineering. Dr. Minisci has received prestigious awards, including the NATO STO “Panel Excellence” Award (2024) and the Sir Arthur C Clarke Award (2015). His research focuses on multidisciplinary design optimization under uncertainties, machine learning applications, and aerospace technologies for sustainable development. Key projects include optimizing high-lift devices, wind turbine performance, and precision agriculture systems. He has contributed to numerous conferences, including organizing the International Conference on Bioinspired Optimisation Methods and serving as an examiner for PhD theses. His work bridges computational intelligence with practical engineering challenges, emphasizing resilience and innovation in aerospace and energy sectors. Research Interests: Multi-Objective Optimization, Nature-Inspired Algorithms, Uncertainty Treatment, Machine Learning, Intelligent Control. Recent Projects: Symbolic Computation for Differential Equation Systems, EPSRC-funded Uncertainty Quantification in Aeroelastic Systems, and Advanced Natural Language Models for Satellite Communications.

Sebastiaan Breedveldمشاهده پروفایل

دانشیار

• Radiation Therapy
• Applied Mathematics
• Decision-Making
• +۳ مورد دیگر

Dr. Sebastiaan Breedveld is an Associate Professor in the Department of Radiation Oncology at Erasmus University Medical Center in Rotterdam, The Netherlands. His research focuses on improving radiation therapy through applied mathematics, multi-criteria optimization, and automated treatment planning. He developed the clinically implemented Erasmus-iCycle algorithm and leads projects like TROTS (Radiotherapy Optimisation Test Set). Education includes a PhD in Medical Physics (cum laude) from Erasmus University Rotterdam and an MSc in Applied Mathematics from Delft University of Technology. Research interests center on developing computational methods for real-time, personalized radiotherapy. Key areas include: Large-scale optimization for treatment planning Deep learning for instantaneous dose prediction Multi-criteria decision analysis for balancing treatment trade-offs Proton therapy and VMAT techniques High-performance computing applications Recent publications demonstrate strong focus on AI-driven automation in brachytherapy, IMPT robustness, and FLASH proton therapy. Work consistently addresses clinical translation of mathematical optimization. Significant scientific awards include: NWO Vidi Grant (2021) for INSTORAD project NWO Veni Grant (2016) MCDM Doctoral Dissertation Award (2015) Erasmus MC Efficiency Grant (2015) PhD with honors (2013) Leads research group supervising 2 post-docs, 11 PhD students, and numerous master/bachelor projects. Major grants support work in proton therapy optimization and automated planning systems. Developed TROTS dataset for benchmarking radiotherapy optimization algorithms and collaborates internationally through projects like BiCycle for automated brachytherapy planning.

• Autonomous Systems
• Biomimetic Robotics
• Unmanned Underwater Vehicles
• +۷ مورد دیگر

Dr Euan W McGookin is a Senior Lecturer in Autonomous Systems & Connectivity at the University of Glasgow, based in the Aerospace Sciences division of the James Watt Building South. He coordinates Glasgow-delivered aerospace degree programmes in Singapore and serves on the IFAC Technical Committee on Marine Systems, underlining his sustained engagement with both local and international academic activities. Education: 1st Class Honours Master of Engineering in Avionics, University of Glasgow PhD in Optimisation of Sliding Mode Controllers for Marine Applications, University of Glasgow (1997) Research Interests Dr McGookin’s core expertise lies in the design, simulation, control and physical realisation of autonomous robotic systems. His work spans Autonomous Underwater Vehicles (AUVs) , Unmanned Aerial Vehicles (UAVs) , Planetary & Terrestrial Rovers , and Biomimetic Robotics . He is particularly recognised for applying biologically inspired principles to robotic locomotion, navigation and control. Complementary themes include advanced control methodologies—Sliding Mode Control, H-infinity, Inverse Model Control—optimisation heuristics, guidance & navigation, fault detection & isolation (FDI), and system health monitoring for both terrestrial and space applications. Publication Trends Across 80 publications from 1995 to 2025, his work has evolved from early genetic-algorithm-based controller optimisation for marine vessels to cutting-edge multi-rover mission planning and health monitoring for planetary exploration. Recent outputs (2022–2025) concentrate on micro-rover coordination, friction modelling for planetary soils, reinforcement-learning-driven sensor fusion, and robust health-monitoring architectures, reflecting a strategic pivot toward space robotics while retaining strong roots in control theory and autonomous systems. Scientific Awards & Fellowships Member, IFAC Technical Committee on Marine Systems Grant & Advising Narrative While specific grant values are not disclosed, his continuous funding stream is evidenced by sustained publication output, international conference leadership, and ongoing supervision of postgraduate projects. Dr McGookin advises a steady cohort of PhD and MSc students whose theses align with his research themes—ranging from rover fault diagnosis to biomimetic AUV coordination—thereby fostering the next generation of control and robotics engineers. Laboratory & Team Dr McGookin heads research activities within the James Watt Building South, leveraging interdisciplinary laboratories that integrate simulation suites, rapid-prototyping facilities for AUV and UAV subsystems, and dedicated test rigs for biomimetic propulsion and rover mobility studies. Collaborative networks extend across the University of Glasgow’s Aerospace Engineering group, Singapore Institute of Technology partners, and international consortia such as ESA and IFAC.