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João F. HenriquesView profile

Research Fellow

• Machine Learning
• Deep Learning
• Computer Vision
• +7 more

Dr. João F. Henriques is a Research Fellow at the Royal Academy of Engineering and a core member of the Visual Geometry Group (VGG) at the University of Oxford. His work spans the intersection of machine learning , deep learning , and computer vision , with notable contributions to visual tracking , 3D reconstruction , and robotics . He actively mentors DPhil students and collaborates across disciplines including AI safety , NeRFs , and optimisation . Current Students: Marian Longa, Tim Franzmeyer, Dominik Kloepfer, Yash Bhalgat, Shivani Mall, Lorenza Prospero, Mark Eid Graduated Students: Xu Ji, Mandela Patrick, Shu Ishida, Andreea Oncescu Research Trends from his recent work include advances in 3D scene reconstruction (e.g., Flash3D, GST), robotic adaptation (Rapid Motor Adaptation), and multimodal learning (Text2Loc, SCENES). His publications frequently address theoretical guarantees in unsupervised detection and reinforcement learning for POMDP environments. Scientific Recognition includes: Research Fellow, Royal Academy of Engineering CVPR Best Paper Finalist (2012) for Kernelized Correlation Filters (KCF) SIGBOVIK 2020 Most Timely Paper Award for Deep Industrial Espionage He also develops open-source tools like OverBoard , a Python dashboard for deep learning experiment monitoring, and advocates for preregistration workshops to improve machine learning research transparency.

• Data science
• Machine learning
• Business analytics
• +10 more

Masayuki Goto is a Professor in the Department of Industrial Systems Engineering, School of Creative Science and Engineering at Waseda University, Japan, where he has served since 2011. He earned his Doctor of Engineering from Waseda University and leads research integrating statistical science, machine learning, information theory and management engineering to solve business-analytics, marketing, AI ethics and industrial optimisation problems. Education: Doctor of Engineering, Waseda University Research Interests: His work spans data science, machine learning, business analytics, statistical learning theory, generative AI, deep neural networks, natural language processing, network analysis and information theory, with recent emphasis on trustworthy AI and synthetic data generation. Publication Trends: Over 2024-2025 his group has published extensively on deep learning for tabular data, vision-language models, recommender systems, causal inference and ethical AI, demonstrating a shift toward generative-AI-driven business analytics and interpretable models. Scientific Awards: Best Paper Award, CIE51 2024 Outstanding Paper Award, APIEMS 2023 Best Paper Award, APIEMS 2022 Best Paper Award, 20th ANQ Congress 2022 2022 PC Conference Best Paper Award Best Paper Award, JASMIN 2021 Best Paper Award, 19th ANQ Congress 2021 Encouragement Award, AAMSA 2021 IDR User Forum 2020 Enterprise & DBSJ Special Awards Best Paper Award, APIEMS 2019 Best Paper Award, ANQ Congress 2018 World CIST'18 Best Paper Award Best Paper Award, ANQ Congress 2017 JSPS Grant Review Commendation 2016 JIMA Distinguished Research Award 2015 Best Paper Award, Journal of JIMA 2015 IPSJ National Convention Best Paper Awards (2015 & 2012) Advising & Grants: He has mentored a large cohort of graduate students evidenced by co-authorship on over 100 recent papers. He has served as PI on numerous JSPS KAKENHI grants and industry projects focused on data-driven management, AI marketing and ethical AI frameworks. Labs & Teams: He heads the Goto Laboratory within the Waseda Institute for Advanced Study, leading interdisciplinary projects on business AI, data-ethics education and industrial optimisation.

• Multi-criteria decision analysis
• Operations research
• Fuzzy sets and fuzzy numbers
• +7 more

Matteo Brunelli is Associate Professor of “Mathematical Methods of Economics and Actuarial and Financial Sciences” at the University of Trento , Department of Industrial Engineering, and Adjunct Professor (docent) at Lappeenranta University of Technology , Finland. He is nationally habilitated as Full Professor in Italy and has held long-term visiting positions at Berkeley, Turku, Auckland, JAIST and Binghamton. Education: Ph.D. (Doctor of Science) in Information Technologies, Åbo Akademi University, Finland, 2011 – graded Eximia cum laude approbatur M.Sc. in Economics, University of Trento, 2007 – grade 110/110 cum laude B.Sc. in Economics, University of Trento, 2005 Research focus: Brunelli’s work sits at the intersection of multi-criteria decision analysis , operations research and computational optimisation . He develops axiomatic foundations and algorithms for pairwise comparison matrices , consistency indices , the best-worst method and fuzzy preference relations , and applies them to energy planning, sustainable inventory, maintenance scheduling, 3-D printer selection, and blockchain governance. His 2023-2025 articles reveal intensified interest in uncertainty modelling (Dempster-Shafer theory), bi-objective optimisation of inventory and maintenance, and group decision protocols that integrate probabilistic or active-learning components, demonstrating both methodological depth and practical relevance. Scientific awards & grants: Academy of Finland Postdoctoral Researcher grant (€254 670, 2014-2017) Claudio Dematté Research Grant (€19 000, 2008) Teacher of the Year Award, Aalto University (2013 – both Spring & Autumn semesters) Bernard Roy Award 2021 for outstanding contribution to Multiple Criteria Decision Aiding (under-40 category) Supervision & funding: While specific doctoral students are not listed, Brunelli currently supervises graduate theses at Trento and has continuously held competitive national grants. His Academy of Finland project “Consistency of valued preference relations for decision analytics methods” financed three years of full-time research and international collaboration. Editorial & community roles: He serves on the editorial boards of International Journal of General Systems and Mathematical and Computational Applications , and acts as area editor for Journal of Multi-Criteria Decision Analysis , positioning him among the key gatekeepers of the MCDA community.

Pierre FlenerView profile

Professor

• Artificial Intelligence
• Constraint Programming
• Discrete Optimization
• +5 more

Pierre Flener is a Professor at the Department of Information Technology, Division of Computing Science at Uppsala University. He leads the Optimisation Group and is a member of the Centre for Interdisciplinary Mathematics. His work focuses on constraint programming and discrete optimization, addressing complex scheduling, routing, and resource allocation challenges. Flener is an Officer of the Order of Merit of Luxembourg and co-founder of NordConsNet, the Nordic Network for Constraint Programming researchers. Research Interests: Flener’s research spans constraint programming, combinatorial optimization, and algorithm design. He develops models and tools for automated decision-making in domains like air traffic management, sensor networks, and industrial robotics. His work emphasizes practical applications, leveraging constraint satisfaction techniques to solve real-world puzzles such as vehicle routing and personnel allocation. Key Contributions: Flener has authored over 100 publications on constraint solving, symmetry breaking, and CP-based approaches to industrial problems. Notable projects include airspace sectorization optimization, energy-efficient sensor networks, and financial portfolio design. He has led initiatives like Auto-Tabling for MiniZinc and collaborated on CP applications in bioinformatics and image processing. Labs & Teams: He heads the Optimisation Group at Uppsala, fostering research in CP and its applications. NordConsNet, co-founded by Flener, connects Nordic researchers and practitioners in constraint technology.

• Business Process Management
• Strategy
• Technology Management
• +12 more

Dr Jeff Chamberlain is a Senior Lecturer at Deakin Business School, Faculty of Business and Law, Deakin University, where he also serves as Deputy Director of the MBA program. With over two decades of industry experience at the Australian Taxation Office (ATO), he transitioned into academia, bringing a pragmatic, industry-informed approach to teaching and research. His work centers on the 'triple helix' model linking strategy, business processes, and technology. Doctor of Philosophy, Deakin University Master of Electronic Commerce, Deakin University Graduate Diploma of Electronic Commerce, Deakin University Graduate Certificate of Electronic Commerce, Deakin University Graduate Certificate of Higher Education, Deakin University Bachelor of Business, Deakin University CPA (Certified Practising Accountant), CPA Australia His research focuses on business process management, digital transformation in SMEs, enterprise systems (especially ERP), and organizational learning. He explores how SMEs strategize for technological change and the role of awareness and learning in enterprise transformation. Recent work includes a study on a Global Advisory Program (GAP) for innovative SMEs. The recent articles reflect a strong trajectory in e-government, digital services, and cross-cultural project management, with a pivot toward educational innovation and flexible learning models. His work bridges public sector digital transformation and academic pedagogy, emphasizing practical application. Vice Chancellor's Award for Teaching Excellence, 2017 Fellow, CPA Australia (FCPA), 2016 Fellow, Institute of Managers and Leaders (FIML), 2016 Dean's Lecturing Legend Award, 2012 Australia Day Achievement Award, 2002 Jeff has supervised numerous student capstone projects, led industry-university partnerships, and secured internal grants such as the BPM Deakin project. He has been involved in executive education with DeakinCo, delivering transformation leadership programs to Tata Aerospace. He also serves on the board of Eastern Innovation Business Centre, supporting sustainable startups. He leads the 'BPMDeakin' virtual community and has developed innovative teaching models like 'Start Anytime' and 'Cloud First'. His teaching units include MBA705 Enterprise Optimisation and Transformation, emphasizing real-world applicability and digital agility.

Anker Degn JensenView profile

Professor

• Chemical Reaction Engineering
• Catalysis
• Particle Technology
• +10 more

Anker Degn Jensen is a Professor in the Department of Chemical and Biochemical Engineering at the Technical University of Denmark (DTU), where he is affiliated with the CHEC Research Centre. His research spans chemical reaction engineering, catalysis, and particle technology with applications in energy and sustainability. His research interests include chemical reaction engineering , catalysis , combustion , gasification of solid fuels , flue gas cleaning (especially NOx and Hg removal), production of liquid fuels , and fluidized bed processes for coating and agglomeration in white biotechnology. His work significantly contributes to UN Sustainable Development Goals related to clean energy and climate action. The recent publications highlight a strong trend in bio-oil upgrading , ammonia synthesis , plasma-assisted methane conversion , and adsorption modeling . These works reflect a deep engagement with sustainable fuel production, catalytic process optimization, and fundamental surface reaction mechanisms. Conversion of Furfural as a Bio-Oil Model Compound An Adsorption Isotherm That Includes Interactions Plasma-Assisted Non-Oxidative Coupling of Methane Optimisation of a Haber-Bosch Synthesis Loop Production of Phenolic Compounds from Argan Shell Waste Anker Degn Jensen is actively supervising multiple PhD students and leading research projects in hydrogen production, CO2 and H2O electrochemical reduction, bio-oil hydrotreating, and catalytic upgrading of biomass. He is involved in over 100 projects, including active grants on Conversion of hydrocarbons to hydrogen , Modeling electrochemical CO2 reduction , and Catalytic upgrading of pyrolysis oil . He is associated with the CHEC Research Centre at DTU, a hub for chemical engineering and catalysis research. His team collaborates on advanced reactor designs and catalytic processes for renewable fuels and chemicals.

• Mathematical Finance
• Probability Theory
• Stochastic Analysis
• +5 more

Sigrid Källblad Nordin is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Department of Mathematics (Division of Probability, Mathematical Physics, and Statistics). Her research focuses on Mathematical Finance, Probability Theory, and Stochastic Analysis, with an emphasis on measure-valued processes, martingale optimal transport, and model uncertainty. She holds a DPhil from the University of Oxford (2014). Her work bridges theoretical advancements in stochastic control, optimization, and financial applications. Recent research includes Bayesian optimal adaptive control, robust option pricing, and dynamically consistent investment strategies under uncertainty. She teaches courses such as Financial Mathematics and Financial Derivatives, and supervises PhD students Linn Engström and Chaorui Wang. Publications span journals like Annals of Applied Probability , Finance and Stochastics , and SIAM Journal on Control and Optimization , reflecting contributions to optimal transport, stochastic processes, and financial modeling. She is currently hiring a new PhD student and welcomes inquiries about master thesis supervision.

Shu-Jung Sunny YangView profile

Professor

• Supply Chain Resilience
• Sustainable Operations
• Data-Driven Operations
• +3 more

Shu-Jung Sunny Yang is a Professor and Chair in Operations Management at the School for Business and Society, University of York. He has held faculty positions at institutions including the University of Melbourne, University of Essex, and National Taiwan University, alongside administrative roles such as department head and research centre director. He earned his PhD in Management from the Australian Graduate School of Management (AGSM) at the University of Sydney and University of New South Wales. His research focuses on Supply Chain Resilience, Sustainable Operations, and Data-Driven Operations, combining formal theory, data science, and operational research. Recent work includes modeling supply chain collaboration for medical mask distribution during disruptions and exploring altruistic venturing through community-based approaches. His book Building Resilience: Consistent Re-rationalisation in Digital Transformation and Business Inheritance won the 2022 Golden Book Award in Taiwan. Awards: 2022 Golden Book Award (Taiwan) 2004 Sasakawa Young Leader Fellow (Japan) 2019 Ta-Yu Wu Memorial Award (Taiwan) Yang leads the Human futures in the digital transformation interdisciplinary cluster. His teaching includes Operations Management and Supply Chain Management. He currently serves as a guest editor for Transportation Research Part E and holds editorial roles at Journal of General Management and Journal of Management and Systems . Research grants include the British Academy-funded project on Optimising Product Line Design in Professional Service Operations . He actively collaborates with industry and academia, emphasizing practical applications of operations strategy.

Nguyen DangView profile

Lecturer

• combinatorial optimisation
• algorithms
• machine learning
• +5 more

Nguyen Dang is a Lecturer at the School of Computer Science, University of St Andrews, actively supervising PhD students and teaching AI-related modules including Artificial Intelligence (CS3105), Artificial Intelligence Practice (CS5011), Machine Learning (CS5014), and Uncertainty in Artificial Intelligence (CS5016). He leads the Centre for Interdisciplinary Research in Computational Algebra and maintains an active research profile with numerous publications in top conferences. University of St Andrews, School of Computer Science Lecturer (equivalent to assistant professor) Supervising PhD students including Tai Nguyen Teaching multiple AI and Machine Learning courses Dr. Dang's research focuses on the intersection of machine learning and optimization, particularly automated algorithm configuration and design. His work centers on leveraging machine learning techniques to automate the development of optimization algorithms, with special emphasis on deep reinforcement learning for Dynamic Algorithm Configuration and integrating machine learning into constraint programming. His research has significant applications across various domains, especially in automated constraint modeling. The publications reflect strong activity in combinatorial optimization, algorithm selection, and benchmark instance generation. His recent publications demonstrate consistent output in top venues including Artificial Intelligence Journal, GECCO, FOGA, and CP conferences, with notable achievements including Best Paper Awards at GECCO'2025 and GECCO'2022. The research spans theoretical foundations of parameter control, practical applications in constraint programming, and innovative approaches to algorithm configuration. Best paper award at GECCO'2025 Best paper award at GECCO'2022 Nomination for best paper award at FOGA'2023 Best paper award at GECCO'2017 Dr. Dang holds a Leverhulme Early Career Fellowship (2020-2023) worth £90,000 for his project on constraint-based automated generation of synthetic benchmark instances. He has secured additional funding including EPSRC High Performance Computing grants totaling over 2.2 million CPU hours and a COST Action grant. His research group actively develops tools and frameworks for automated algorithm configuration and benchmark instance generation, with several open-source datasets available on GitHub. He is involved with multiple research groups including the Centre for Interdisciplinary Research in Computational Algebra and collaborates extensively with researchers at University of St Andrews and internationally, including at Université de Paris I Panthéon-Sorbonne where he conducted visiting research.

Hyungwoong AhnView profile

Senior Lecturer

• Adsorption Processes
• CO2 Capture
• Carbon Capture and Storage
• +5 more

Dr. Hyungwoong Ahn is a Senior Lecturer in Chemical Engineering at the University of Edinburgh and an Adjunct Professor at Yonsei University. His expertise lies in adsorption process engineering, particularly for CO2 capture and gas separation. He leads the Carbon Capture Group and coordinates exchange programs for chemical engineering students. Dr. Ahn holds a BSc, MSc, and PhD in Chemical Engineering from Yonsei University. Roles: Senior Lecturer (Edinburgh) & Adjunct Professor (Yonsei) Research Focus: Pressure Swing Adsorption (PSA), CO2 capture technologies, hydrogen purification, and industrial decarbonisation Key Projects: PSA-SPUR technology development, ship-based carbon capture, and collaboration with HD Korea Shipbuilding Awards: KOFST Brain Pool Fellow, IChemE Global Awards finalist, and Honeywell UniSim Design Challenge winner His research integrates equilibrium theory analysis, numerical simulation, and experimental validation to advance carbon capture systems. He has authored over 50 publications (H-index 32) and secured funding from EPSRC, BEIS, KETEP, and others.

• Large deformation FE analysis of soils
• Soil–structure interaction
• Offshore foundation systems
• +7 more

Yuxia Hu is a Professor at the University of Western Australia, affiliated with the School of Engineering (Civil, Environmental and Mining Engineering) and the School of Social Sciences, Planning and Transport Research Centre. Her research focuses on geotechnical engineering, particularly in large deformation FE analysis, offshore foundation systems, and soil-structure interaction. She has contributed to advancements in suction caissons, plate anchors, and computational mechanics, with applications in offshore wind energy and infrastructure stability. Research Interests: Large deformation FE analysis of soils, soil-structure interaction, offshore foundation systems, soil mechanics, and pavement engineering. Awards: Telford Premium, British Geotechnical Association Prize, and Significant Junior/Senior Paper Award. Grants: Leads projects on offshore anchors, carbon capture in pavements, and road maintenance optimization. Her work addresses challenges in geotechnical design and sustainable infrastructure, with a focus on numerical modeling and experimental validation. Collaborations span academia and industry, emphasizing practical solutions for complex soil-structure systems.

• Artificial Intelligence
• Machine Learning
• Game Theory
• +7 more

Prof. Tansu Alpcan is a Professor and Reader in the Department of Electrical and Electronic Engineering at The University of Melbourne, Australia. He holds a PhD from the University of Illinois at Urbana-Champaign (UIUC) and has held academic positions at Technical University Berlin and Deutsche Telekom Laboratories. His research focuses on AI/ML applications in engineering, game theory, cybersecurity, Industry 4.0, quantum machine learning, smart grids, and communication networks. Education: PhD in Electrical and Computer Engineering (UIUC, 2006); MSc (UIUC, 2003); BEng (Bogazici University, 1999). Research interests include adversarial machine learning, cybersecurity games, quantum computing, and renewable energy systems. Authored over 200 papers and two books, including Network Security: A Decision and Game Theoretic Approach (Cambridge, 2011). Recipient of IEEE Senior Membership (2012) and multiple best paper awards. He leads the WILAB and has secured grants such as the ARC Training Centre in Optimisation Technologies. Current projects include quantum machine learning, adversarial reinforcement learning, and smart grid modeling. Supervised 17 PhD and 3 Master’s students.

Christine CurrieView profile

Professor

• Operational Research
• Simulation Optimisation
• Healthcare Management
• +9 more

Professor Christine Currie is a Professor of Operational Research within the School of Mathematical Sciences at the University of Southampton. She is a Fellow of the Alan Turing Institute and previously served as Director of the Centre for Operational Research, Management Science and Information Systems (CORMSIS). Her research is primarily funded by EPSRC and spans healthcare, disaster relief, and pricing optimisation. Research Interests: Simulation Optimisation Healthcare Management Decision Making Under Uncertainty Disaster Relief Logistics Optimal Pricing and Revenue Management Her recent work focuses on real-time simulation for emergency departments, infectious disease modelling, patient flow optimisation, food relief procurement in Indonesia, and robust pricing models in transportation and leisure sectors. The trend in her publications shows a strong emphasis on applied operational research with societal impact, particularly in digital twin integration and stochastic optimisation under uncertainty. Scientific Awards: Companion of Operational Research (2024) Professor Currie actively supervises PhD students and has secured multiple research grants, primarily from EPSRC. She has led projects such as 'Dial-a-Ride', 'CREST-OR', and 'Designing a resilient food supply network for natural disasters in West Java, Indonesia'. She also collaborates with external organisations and has supervised student projects with industry partners. Leadership and Editorial Roles: Editor-in-Chief, Journal of Simulation (2015–2024) Member, Editorial Board, Royal Society Open Science (2021–present) Co-chair, OR63 National Operational Research Conference (2021) Member, Operational Research Society Research Panel She is actively involved in the operational research community and leads interdisciplinary research teams including the Operational Research group, Institute for Life Sciences, CORMSIS, and the Centre for Healthcare Analytics.

Matthew K. TamView profile

Associate Professor

• continuous optimisation and its applications
• monotone operator theory
• variational and convex analysis
• +3 more

Matthew K. Tam is an Associate Professor at the School of Mathematics and Statistics, The University of Melbourne, specializing in Operations Research. He is also an investigator at the Melbourne Centre for Data Science and an associate investigator in the ARC Training Centre OPTIMA. PhD in Mathematics (2016) from University of Newcastle under Jonathan Borwein Postdoctoral research at University of Göttingen with RTG-2088 and Alexander von Humboldt Foundation Junior Professor at University of Göttingen (2017-2020) His research focuses on continuous optimization, monotone operator theory, and variational analysis, with applications in wavelet construction and inverse problems. Key trends include distributed algorithms, resolvent splitting, and convergence analysis for feasibility problems. Discovery Early Career Researcher Award (DECRA) Alexander von Humboldt Fellowship He collaborates with institutions like ANZIAM, Springer, and IEEE, with publications spanning mathematical optimization, harmonic analysis, and computational mathematics. His work emphasizes algorithmic design for complex data systems and real-world applications in imaging and industrial modeling.

• Robotics
• Machine Learning
• Reinforcement Learning
• +3 more

Pawel Ladosz is a Lecturer in Engineering Systems for Robotics at the Department of Mechanical and Aerospace Engineering, The University of Manchester. His research focuses on applying machine learning and computer vision to mobile robots, particularly in extreme environments such as total darkness or cluttered spaces. He is actively involved in developing autonomous navigation systems, wireless signal mapping, and high-level decision-making for robotic swarms. He teaches courses including Robotic Systems Design Project and Autonomous Mobile Robots. Education: PhD in Establishing and Optimising Unmanned Airborne Relay Networks (Loughborough University, 2014–2019) MEng in Aerospace Engineering (The University of Manchester, 2010–2014) Research Interests: Ladosz’s work emphasizes reinforcement learning for robotics, vision-based autonomous systems, and exploration in challenging environments. His projects often intersect with UN Sustainable Development Goals, contributing to innovations in robotic autonomy and sensor networks. Awards: He received the 2nd Autonomous Flying Technology Competition award in 2021, recognizing his contributions to autonomous flight systems. His research has also led to the establishment of the Centre for Robotic Autonomy in Demanding and Long-Lasting Environments (CRADLE), fostering cross-disciplinary collaborations. Grants & Projects: As Principal Investigator in the Aerospace Engineering initiative (2010–2035), he explores UAV communication networks and trajectory planning. His work addresses urban environment challenges, including relay positioning and signal prediction. Labs/Teams: Ladosz contributes to CRADLE, advancing robotic autonomy in extreme scenarios. His lab focuses on integrating AI and robotics for real-world applications.