Dr. Yuan Sun is a Lecturer in Business Analytics and Artificial Intelligence at La Trobe University's La Trobe Business School. His research focuses on leveraging machine learning for combinatorial optimization, including problem reduction methods and hybrid algorithms. He has contributed to top-tier journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and conferences such as ICML and NeurIPS. Sun collaborates with researchers from institutions like Monash University and Singapore Management University, and is affiliated with the ARC Training Centre in Optimisation Technologies (OPTIMA). He co-organized the ACM GECCO 2024 conference and serves on program committees for major AI conferences. Academic Position: Lecturer at La Trobe University (2022–present) Education: PhD in Artificial Intelligence (University of Melbourne), BSc in Applied Mathematics (Peking University) Research interests span machine learning, operations research, and optimization, with notable work on integrating AI with digital twins for sustainable power grids and federated learning frameworks like F3KM. Sun's methods enhance constraint programming and ant colony optimization through supervised learning, addressing complex real-world problems. Grants and collaborations include the 'Quantum Enhanced Optimisation for Energy Efficient Data Centres' project and consulting for the Australian mining workforce analysis. Teaching roles include Algorithms & Analysis and Evolutionary Computing, with joint supervision of PhD projects on combinatorial optimization and mixed-integer programming.
Wolfram Wiesemann is Professor of Analytics & Operations and Head of the Analytics, Marketing & Operations Department at Imperial College Business School. His research focuses on decision-making under uncertainty with applications in supply chain management, healthcare, and energy. Research Focus: Development of tractable approximation schemes for decision-making under uncertainty with rigorous error bounds. Applications span operations management, energy systems, and financial engineering. Teaching: BA1803 (Optimisation and Decision Models) and BA1806 (Introduction to Machine Learning) for MSc programs. Received Imperial College Business School Dean's Teaching Awards and nominations for Student Academic Choice Awards. Editorial Roles: Editor-in-Chief of Operations Research Letters and department co-editor for Management Science. Previously served on editorial boards of multiple leading journals. Research Impact: COVID-19 research on hospital care prioritization featured in Financial Times, Daily Mail, and France24.
Michael E. Cholette is an Associate Professor in the School of Mechanical, Medical & Process Engineering at Queensland University of Technology's Faculty of Engineering. With expertise spanning multiple engineering disciplines related to dynamic systems, reliability, and applied optimization, his work focuses on practical applications across various industries and infrastructure systems. Dr. Cholette earned his B.S. from the University of Michigan in 2007 and completed his Ph.D. at the University of Texas at Austin in 2012, where his research centered on fault detection and diagnosis for complex systems with applications in automotive systems and semiconductor manufacturing. Following his doctoral studies, he worked as a postdoctoral research fellow with the CRC for Infrastructure and Engineering Asset Management in Brisbane, Australia, focusing on reliability modeling and asset management before joining QUT as a Lecturer in 2013. He was promoted to Associate Professor in 2021. His primary research interests include asset management, reliability modeling, Condition-Based Maintenance (CBM), and maintenance optimization. Additional research areas encompass condition monitoring, control systems, autonomous vehicles, and building optimization. Dr. Cholette has successfully secured research funding from various industrial and government bodies including Queensland Rail, Wilmar Sugar, the Australian Research Council, the Australian Renewable Energy Agency, and the US Department of Energy. His recent publications reveal a strong focus on renewable energy systems, particularly concentrated solar power plants, with significant contributions to solar field design, heliostat soiling management, and thermal storage optimization. His transportation-related research shows expertise in railway infrastructure maintenance, track geometry monitoring, and predictive maintenance approaches. The interdisciplinary nature of his work bridges mechanical engineering, control theory, and operations research to solve practical engineering challenges. Fellow of the International Society of Engineering Asset Management Co-chair of the Scientific Committee for the 2017 World Congress on Engineering Asset Management Member of Engineers Australia Dr. Cholette has supervised numerous doctoral students whose research spans solar energy systems, autonomous vehicle control, building energy optimization, and maintenance planning. His teaching includes courses in Engineering Asset Management and Maintenance, Vibration & Control, and Advanced Dynamics. He actively participates in multiple academic and professional societies and maintains strong industry connections that inform both his research and teaching practices.
Aly-Joy Ulusoy is an Imperial College Research Fellow in the Department of Civil and Environmental Engineering at Imperial College London, affiliated with the Faculty of Engineering and the Grantham Institute. Her research focuses on optimisation methods for the design and control of water distribution networks, integrating adaptive systems, hydraulic modeling, and resilience analysis. She holds a Ph.D. (2021) and M.Sc. (2016) from Imperial College London, and an M.Sc. from École Supérieure d’Électricité, France (2016). Previously, she worked as a postdoctoral researcher in Dr. Ivan Stoianov's InfraSense Labs. Education: Ph.D. in Civil and Environmental Engineering, Imperial College London (2021) M.Sc. in Civil and Environmental Engineering, Imperial College London (2016) M.Sc., École Supérieure d’Électricité, France (2016) Her research interests span optimisation algorithms for water network control, adaptive hydraulic systems, data-driven hydraulic modeling, and resilient infrastructure design. Recent work emphasizes dynamically adaptive networks for pressure management, fault localization, and integration of AI for demand forecasting. She explores multi-objective optimization techniques to balance operational efficiency and resilience in water distribution systems. Key research trends include: (1) distributed optimization for real-time control under time-coupling constraints, (2) interpretable AI for water demand prediction, and (3) principled approaches for model maintenance using principal component analysis. Her work bridges theoretical optimisation with practical water infrastructure challenges. Advising and grants: No specific advisees or grants listed. Affiliated with InfraSense Labs, focusing on smart infrastructure and sensor networks. Active in developing methodologies for network resilience under uncertainty. Ongoing projects include adaptive MPC strategies for burst incident management and design-for-control frameworks for dynamically adaptive systems.
Yingqian Zhang is an Associate Professor in the Information Systems group at the Industrial Engineering and Innovation Sciences department of Eindhoven University of Technology (TU/e). She is affiliated with the Eindhoven Artificial Intelligence Systems Institute (EAISI), specifically with the EAISI High Tech Systems and EAISI Foundational groups. Her research focuses on applying Artificial Intelligence to solve complex decision-making problems across various domains including logistics, transportation, manufacturing, and e-commerce. Dr. Zhang received her PhD in Computer Science from the University of Manchester, UK. Prior to joining TU/e, she served as an Assistant Professor in the Econometrics Institute at Erasmus University Rotterdam and as a postdoc researcher in the Algorithmics group at TU Delft. She was also a visiting professor at the Institute for Advanced Computer Studies at University of Maryland, College Park, USA. Her research expertise lies at the intersection of Artificial Intelligence and optimization, with particular focus on machine learning, deep reinforcement learning, and trustworthy data-driven optimization. Dr. Zhang develops socially aware algorithms that can optimize decisions in data-rich environments. Her work bridges the gap between theoretical AI advancements and practical applications in industrial settings, addressing real-world challenges through innovative algorithmic solutions. She is particularly interested in how AI can support human decision-making while maintaining transparency and trustworthiness. Dr. Zhang's recent publications reveal a strong trend toward applying graph neural networks and reinforcement learning to complex scheduling and optimization problems. Her work demonstrates increasing sophistication in handling stochastic elements in decision-making processes, with applications spanning healthcare diagnostics, logistics, transportation, and manufacturing. She has made significant contributions to the field of neural combinatorial optimization, particularly for job shop scheduling problems and vehicle routing. Dr. Zhang has received several prestigious awards recognizing her contributions to the field: Winner of the MLVRP2023 GECCO competition (2023) Best Paper Award from Omega-International Journal of Management Science (2017) Best Industrial Paper Award (2020) Best Student Paper Award (2019) Best Student Paper Award of ICAART 2022 (2022) As a dedicated mentor, Dr. Zhang supervises numerous PhD students including Mohsen Abbaspour Onari, Abdo Abouelrous, Luca Begnardi, Xia Jiang, Chengpeng Hu, Minshuo Li, Robbert Reijnen, Jesse van Remmerden, Bart von Meijenfeldt, Ya Song, and Igor Smit. Her research is supported by various grants, including the LEO (Learning and Explaining Optimization) project co-funded by Holland High Tech | TKI HSTM via the PPP allowance scheme for public-private partnerships. Dr. Zhang actively contributes to the academic community as the Chair of the Benelux Association for Artificial Intelligence (BNVKI) and as a member of the Technical Board for the European Big Data Value Association (BDVA). She serves as an associate editor for the "Annals of Mathematics and Artificial Intelligence" journal and participates in the technical Program Committee for major AI conferences such as IJCAI, AAAI, AAMAS, and ECAI. She is also on the executive committee of the Data Science meets Optimisation (DSO) working group of EURO to promote collaboration between AI and Operations Research communities.
Alessandro Di Giorgio is Associate Professor of Automatic Control at the Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG) , Sapienza University of Rome, where he teaches courses on Automation, Network Control and Handling, and Modeling & Simulation. He coordinates the Smart Energy Research Group of the Consorzio per la Ricerca nell’Automatica e nelle Telecomunicazioni and acts as scientific referent of the university start-up Applied Research to Technologies . Education Ph.D. in Systems Engineering, Sapienza University of Rome, 2010 M.Sc. in Physics (110/110 cum laude), Sapienza University of Rome, 2005 Research interests revolve around advanced control architectures for future power systems, with particular emphasis on smart-grid planning and real-time operation , model-predictive control of micro-grids , large-scale integration of renewable generation and storage , stochastic optimisation of electric-vehicle charging infrastructures , and cyber-physical security against malicious attacks . His work combines systems theory with practical implementations validated within several European and national projects. Recent publications (2023-24) demonstrate a clear trend toward data-driven and stochastic model-predictive strategies that coordinate millions of grid-edge resources—electric vehicles, batteries, renewable plants—while guaranteeing economic efficiency, reliability and resilience of distribution networks. Projects & Technology Transfer Scientific responsible in 10 EU-funded research projects on smart grids and electromobility Principal Investigator of Italian academic and Industria 2015 programmes Co-founder & scientific reference, start-up Applied Research to Technologies (technology transfer on EV-charging optimisation) He is member of the Networked Systems Cybersecurity initiative and serves as reviewer and organiser for major IEEE conferences on power systems and control.
Corinne LUCET-VASSEUR is a University Professor at Université de Picardie Jules Verne (UPJV), leading Research Unit UR 4290 (OCIA - Optimisation Combinatoire, Images et Applications). Her office (Room 302, Tel: 5900) serves as the hub for her research group focused on combinatorial optimization and artificial intelligence applications. Her research spans: Combinatorial Optimization : Developing metaheuristics for NP-hard problems Healthcare Logistics : Patient flow optimization, facility location, simulation training Logistics Engineering : Parcel distribution, vehicle routing with time windows Algorithm Design : Ant Colony Optimization, Adaptive Large Neighborhood Search, portfolio methods She applies these methodologies to solve complex real-world problems, particularly in healthcare systems where resource constraints and scheduling complexity demand innovative optimization approaches. Her work bridges theoretical advances with practical implementation through industrial partnerships. Current research projects include: SMILE PICK UP (CIFRE industrial partnership) Simusanté (healthcare simulation) LORH (logistics optimization) These projects secure ongoing funding and provide doctoral training opportunities through industry collaboration. Her publication record demonstrates consistent methodological innovation applied to healthcare and logistics challenges across multiple European conferences and journals. Professor Lucet-Vasseur actively mentors junior researchers through co-authorship on conference papers and journal articles. Her supervision style emphasizes practical problem-solving with industry relevance, preparing students for both academic and industrial careers in optimization. The OCIA research unit provides a collaborative environment for tackling complex combinatorial problems with real-world impact. The OCIA laboratory serves as UPJV's center for combinatorial optimization research, specializing in metaheuristic development for healthcare and logistics applications. The lab maintains strong industry connections through CIFRE contracts and applied projects, ensuring research relevance while providing students with exposure to real business challenges. Current focus areas include adaptive algorithm selection using reinforcement learning and fitness landscape analysis for optimization problems.
