جست‌وجوی استاد و پژوهشگر

• Transportation
• Logistics
• Supply Chains
• +۶ مورد دیگر

Assoc Prof Kok Choon Tan is an Associate Professor in the Department of Analytics and Operations at the NUS Business School. He holds a Deputy Director role at the Centre for Maritime Studies (CMS), Singapore. His research focuses on container terminal operations optimization, maritime logistics, and simulation-based decision-making. He has been with NUS since 1984, starting as a Senior Tutor in Mathematics, and later earning a Post-Graduate Diploma in Teaching in Higher Education (PG DipTHE) to enhance his pedagogical skills. Research Interests: Transportation and logistics optimization, applied mathematics, operations research modeling, and maritime systems. He leads the Port Operations Modelling & Analysis track at CMS, focusing on container terminal efficiency through mathematical optimization and simulation. Teaching Philosophy: Emphasizes student-centered learning, active learning strategies (e.g., collaborative learning, case studies), and bridging theory to real-world applications. His teaching integrates problem-solving, critical thinking, and presentation skills development through group projects and industrial case studies. Recent Research Trends: His articles address AGV scheduling, container relocation under uncertainty, and multi-modal port operations. These contributions highlight innovations in automated systems, data-driven decision-making, and sustainability in port logistics. Labs/Teams: Active in the NUS Centre for Maritime Studies and collaborates with departments in Industrial Systems Engineering and Civil & Environmental Engineering. His work supports Singapore’s maritime and logistics industry through applied research and strategic planning.

• Software Engineering
• Artificial Intelligence
• Genetic Programming
• +۴ مورد دیگر

Dr. Anthony Ventresque is a Visiting Associate Professor at the University College Dublin, School of Computer Science , with a PhD in Computer Science from the University of Nantes & INRIA France (2008). He is also the Director of the TCD Complex Software Lab and a Senior Investigator with Lero, the SFI Irish Software Research Centre. MSc in Philosophy and Logic from the University of Nantes PhD in Computer Science from the University of Nantes & INRIA France His research focuses on Software Engineering , Genetic Programming , and Multi-objective Optimization , with applications to Autonomous Systems , Cloud Computing , and Sports Analytics . Recent publications highlight his work on grammar-obeying program synthesis using large language models, mutation testing for software quality, and domain adaptation for object detection in rugby data. His scientific awards include a Teaching Award from the UCD School of Computer Science in 2020. He has contributed to grants like Mutation Testing at Scale and Advancing Computational Archaeology , and his teaching activities encompass modules such as Big Data Programming and Operating Systems over multiple years. He leads the TCD Complex Software Lab , fostering interdisciplinary research and innovation.

• Evolutionary Computation
• Human-Computer Interaction
• User Experience Design
• +۴ مورد دیگر

Reza Ghanbarzadeh is a Lecturer at the Faculty of Science and Engineering, Southern Cross University, Australia, with a PhD in Information Systems from Griffith University. He holds master's and bachelor's degrees in Software Engineering. His research focuses on evolutionary computation, human-computer interaction, user experience, and innovative technology applications in healthcare and education. Reza teaches IT units including immersive interaction design and software development in the School of Business and Tourism. Education: PhD (Information Systems, Griffith University), MSc (Software Engineering), BSc (Software Engineering). Research Interests: Evolutionary algorithms, healthcare informatics, generative AI in education, and user-centric software design. He has published extensively in journals and conferences, with notable work on metaheuristic optimization algorithms and their applications in software engineering and healthcare diagnostics. Awards: Recipient of the Outstanding Teaching Award (Southern Cross University, 2024). Advising & Grants: While no specific grants or students are listed, his publications suggest active involvement in collaborative research projects. He advises on topics related to AI, healthcare IT, and educational technologies. Labs/Teams: No formal lab affiliations mentioned, though his work intersects with interdisciplinary teams in health informatics and educational technology.

Graziana Cavoneمشاهده پروفایل

دانشیار

• Automatic Control
• Model Predictive Control
• Cyber-Physical Systems Security
• +۵ مورد دیگر

Associate Professor Graziana Cavone is with the Department of Civil, Computer and Aeronautical Technologies Engineering at Roma Tre University, Rome, Italy. Her research integrates automatic control, optimisation and AI to guarantee safe, secure and efficient operations of cyber-physical systems ranging from industrial robots and drones to water networks and intermodal freight terminals. Education & affiliation: Department of Civil, Computer and Aeronautical Technologies Engineering, Roma Tre University Scientific disciplinary sector ING-INF/04 – Automatic Control Research interests revolve around four pillars: (i) model-predictive and data-driven control of complex dynamic systems, (ii) safe and ergonomic human-robot/human-drone interaction in logistics 4.0 environments, (iii) cyber-security and resilience of industrial control and water-supply networks, and (iv) stochastic optimisation and Petri-net modelling for railway traffic and intermodal freight terminals. Recent 2024-2025 publications exhibit a clear trend toward AI-enabled anomaly detection, digital-twin security frameworks, and socio-technical studies on robot acceptance, while earlier work concentrated on COVID-19 mitigation control and collaborative-robot motion planning. Scientific awards & editorial activity: Guest-editor for IEEE CASE 2018 special section, publicity chair for IEEE CASE 2023; specific prizes not listed in supplied text. Advising & grants: No explicit student lists or funded-project details are provided in the source. Labs & teams: No dedicated laboratory designation is mentioned; research is conducted within the departmental laboratories of Roma Tre University.

Rand Hussein Raheemمشاهده پروفایل

مدرس

• Wireless Networks
• Telecommunications Engineering
• Computer Networks
• +۳ مورد دیگر

Rand Hussein Raheem is a Lecturer in Computer Science at Middlesex University, London, United Kingdom. She holds a Ph.D. in Interference Management and System Optimisation for Small Cells Technology in Future 4G/5G Networks (2016) and has been employed at Middlesex University since September 2013. Education: Diploma in Electrical and Computer Engineering (2009, First Class Honours), BSc in Computer Communication Engineering (2010, First Class Honours), MSc in Telecommunication Engineering (2011, Merit). Research Interests focus on wireless sensor networks, femtocell technology, interference management, machine learning applications in network scheduling, and mobility management in LTE/5G systems. Her work explores bio-inspired algorithms for dependability in safety-critical networks and optimization techniques for next-generation telecommunications. Recent Publications highlight advancements in wireless sensor network scheduling using bio-inspired models (2025-2024), interference mitigation in femtocell systems, and ransomware payment analysis in cryptocurrency ecosystems. Her research spans both theoretical and applied domains, with applications in vehicular networks and railway environments. Teaching Responsibilities include modules on Network Management, Project Management, Compliance, and Computer Systems Architecture.

Yu Jiangمشاهده پروفایل

دانشیار

• Transportation Science
• Transit Assignment
• Traffic Assignment
• +۱۳ مورد دیگر

Yu Jiang is an Associate Professor in the Department of Transportation Systems Modelling at the Technical University of Denmark (DTU), within the Department of Technology, Management and Economics. He is actively engaged in research and supervision, with a strong focus on transportation science and systems optimization. He is accepting PhD students and maintains an active research profile with numerous ongoing projects and publications. Education: PhD, The University of Hong Kong (2010–2014) His research interests include static and dynamic transit/traffic assignment, network design, infrastructure resilience and vulnerability, multi-objective optimization, and metaheuristics. These areas are central to improving urban mobility, public transport efficiency, and sustainable transportation systems. His work contributes to UN Sustainable Development Goals related to sustainable cities and responsible consumption. Recent publications (2025) reflect a strong trend in applying advanced optimization and machine learning techniques to urban transportation challenges, including traffic signal control, last-mile delivery, intermodal travel behaviour, and demand prediction across shared mobility systems. The research spans both theoretical modelling and practical case studies. Scientific Awards: No scientific awards listed in the provided text. Advising and Grants: Yu Jiang is the main supervisor for several PhD projects, including 'Development of an integrated optimisation model for operating urban transit system (SmoothTrip)', 'Crowdsourced Delivery as an Activity for Sustainable Cities', and 'Network Optimisation for Connected, Cooperative and Automated Mobility'. He is also a co-investigator in the NEMESYS project funded by the European Commission, focusing on dynamic pricing and tradable credits for emissions management. His academic activities include organizing conferences, presenting at international events, serving as a journal editor, and conducting peer reviews. Labs and Research Teams: He is part of the research network at DTU's Department of Technology, Management and Economics, collaborating on projects related to urban transit optimization, shared mobility, and sustainable freight. His work involves close collaboration with researchers from institutions such as Oxford University, NUS, and HKU, as well as international partners in ongoing research initiatives.

• Energy Decarbonisation
• Electrical Power Systems
• Smart Grids
• +۱۲ مورد دیگر

Connor McGarry is a Research Fellow in the Department of Electronic and Electrical Engineering at the University of Strathclyde, within the Faculty of Engineering. His work is centered on the decarbonisation of energy systems, particularly focusing on the electrification of heat and transport and its impact on local electricity networks. PhD in Electrical Power Systems, University of Strathclyde (2024) MSc in Electrical Power Engineering with Business, University of Strathclyde (2018) BEng in Mechanical and Power Plant Systems, Glasgow Caledonian University (2017) Connor's research interests span energy decarbonisation, spatial-temporal demand modelling, demand flexibility, microgrids, digital twins, and the socio-economic aspects of energy infrastructure. He applies high-resolution geospatial and temporal frameworks to assess how electrification of transport and heating affects distribution networks, with a focus on equitable and resilient energy transitions. His recent publications highlight a strong trend toward sustainable mobility infrastructure, including electric HGV charging and railway station parking electrification in Scotland, as well as broader system impacts like harmonics from EV charging and flexibility in rural networks. His work integrates technical analysis with policy relevance, supporting decision-making for infrastructure investment and decarbonisation strategies. While no formal scientific awards are listed, his research is published in high-impact journals such as Applied Energy and IEEE Access , and he contributes to the UN Sustainable Development Goals, particularly in clean and affordable energy. Connor supports teaching as a Teaching Assistant in digital electronics and optimisation courses and supervises undergraduate and MSc student projects. He was a Research Co-investigator in the EPSRC Centre for Doctoral Training in Future Power Networks and Smart Grids, reflecting his active role in research leadership and training. His research is conducted within the Future Power Networks and Smart Grids group at Strathclyde, collaborating with key figures like Professor Stuart Galloway and Dr. Ian Elders. The team focuses on resilient, flexible, and equitable future power systems, leveraging data-driven and modelling approaches to support the energy transition.

Erandi Hene Kankanamgeمشاهده پروفایل

مدرس ارشد

• Multi-agent systems
• Computational intelligence
• Multi-objective optimisation
• +۴ مورد دیگر

Erandi Hene Kankanamge is a Senior Lecturer in Computer Science at the University of New South Wales, Canberra (UNSW Canberra), working within the School of Systems & Computing. Her academic career spans over a decade with significant contributions to artificial intelligence research and education. Her educational background includes: BSc (hons) in Computer Science at the University of Colombo, Sri Lanka (First Class) PhD in Computer Science at UNSW Canberra (2014) Dr. Kankanamge's research focuses on the intersection of artificial intelligence and human-computer interaction, with expertise in multi-agent systems, computational intelligence, multi-objective optimization, human-computer interfaces, and games for health. Her work bridges theoretical AI advancements with practical applications in defense, healthcare, and gaming contexts. She employs computational intelligence approaches to model complex behaviors and develop systems that enhance human performance and wellbeing through digital interactions. Her publication record shows a clear evolution from foundational work on behavior modeling to sophisticated applications in human-swarm interaction, trust architectures, and exergaming interfaces. The research demonstrates consistent emphasis on practical implementations of AI techniques in real-world scenarios, particularly in board games, military applications, and health-focused digital experiences, with strong interdisciplinary connections between computer science, psychology, and health sciences. Dr. Kankanamge has secured significant research funding through multiple competitive grants: Model Abstractions for Military Wargaming ($220,000, DSTG) Actionable Cyber Security Intelligence from Big Data ($75,000, Industry Grant) Simulation and Visualisation Using Data Farming ($350,000, DSTG) Game Master: Improved player mental and physical wellbeing ($50,000, Rector's Start-up Grant) As an academic advisor, Dr. Kankanamge supervises multiple PhD candidates across diverse research topics including human-swarm interaction, motive profile compositions for cooperative teams, stress detection during gameplay, and reinforcement learning for complex board games. Her professional service includes roles as Secretary of IEEE CIS ACT Chapter, conference organization roles, and journal reviewing activities across multiple prestigious publications in AI and gaming. Dr. Kankanamge's research contributes to multiple interdisciplinary teams focusing on AI-enabled analysis, human-machine teaming, and serious games development. Her work with the Defence Science and Technology Group demonstrates strong connections between academic research and defense applications, while her interest in games for health shows commitment to societal impact through technology.

• Structural Mechanics
• Sustainable Construction
• Recycled Waste Materials in Concrete
• +۳ مورد دیگر

Dr. Yanni Bouras is a part-time Lecturer at the College of Sport, Health & Engineering , Victoria University, with a PhD in Structural Engineering (2020) and BEng (Hons) (2015). His research focuses on structural mechanics, sustainable construction, and waste material utilisation in concrete composites. Education BEng (Hons), Victoria University (2015) GCTE, Victoria University (2020) PhD, Victoria University (2020) Research Interests Yanni investigates the thermal stability of concrete structures, sustainable infrastructure rating tools, and kraft fibre reinforcement from waste cardboard in concrete composites. His work bridges structural engineering with environmental sustainability. Recent Research Trends His publications since 2020 highlight innovations in waste valorisation (cardboard, coffee cups), machine learning for concrete creep prediction, and fractional calculus-based modeling. Key themes include low-carbon concretes , lifecycle assessments , and multi-objective optimisation . Supervision & Grants Yanni serves as a joint supervisor for research on engineered cementitious composites. He contributed to the Social Value Creation for Transport and Infrastructure grant (2021–2022), focusing on sustainable infrastructure. Institute Affiliation He is affiliated with the Institute for Sustainable Industries & Liveable Cities at Victoria University, aligning his work with UNSDGs 12, 6, 7, and 9.

Ramin Raeesiمشاهده پروفایل

مدرس

• Combinatorial optimisation
• Exact and (meta-) heuristic solution algorithms
• Multi-objective optimisation
• +۵ مورد دیگر

Dr Ramin Raeesi is a Lecturer in Management Science at Kent Business School, University of Kent, and an Interim Director of the Centre for Logistics and Heuristic Optimisation (CLHO). He also holds a visiting researcher position at the Centre for Transport and Logistics (CENTRAL) at Lancaster University Management School. Research Overview: Focuses on combinatorial optimisation, vehicle routing, and sustainable logistics with environmental considerations. Research Interests include: Exact and metaheuristic solution algorithms Multi-objective optimisation Electric and hydrogen-fuelled transport systems Shared logistics and container terminal optimisation Publications highlight work on electric vehicle routing with time windows and mobile battery swapping. His research integrates big data analytics into transport and logistics networks. Labs & Groups: Centre for Logistics and Heuristic Optimisation (CLHO) Centre for Transport and Logistics (CENTRAL)

Daniel Karapetyanمشاهده پروفایل

استادیار

• Combinatorial Optimization
• Artificial Intelligence
• Data Science
• +۱۷ مورد دیگر

Daniel Karapetyan is an Assistant Professor at the School of Computer Science , University of Nottingham, and a member of the Computational Optimisation and Learning Lab . His research spans Artificial Intelligence , Data Science , Operational Research , and Optimization , with applications in transportation , logistics , satellite mission planning , and access control . Education: PhD in Computer Science, Royal Holloway, University of London (2007–2010) BSc/MSc in Computer Science, Bauman Moscow State Technical University (2001–2007) His research interests focus on the automated design of heuristic algorithms , multi-objective optimization , and parameter tuning for machine learning and combinatorial problems. He integrates Large Language Models (LLMs) and hyperparameter optimization into practical systems, addressing real-world challenges in airport sequencing , TV ad scheduling , and electric vehicle range prediction . Notable scientific contributions include the CORS Practice Prize (2014) for ferry scheduling work and pioneering fixed-parameter algorithms in security-related optimization problems. His publications emphasize combinatorial optimization , Markov Chain methods , and automated algorithm configuration . Teaching roles include module convener for COMP3008: Knowledge Representation and Reasoning , personal tutoring for COMP1002 , and supervision of COMP2002: Software Engineering Group Project . He also offers PhD supervision in areas like AI for optimization , interpretability in ML , and emergency department coordination .

• Smart Grids
• Power System Optimization
• Renewable Energy Integration
• +۷ مورد دیگر

Souhil Mouassa is a Spanish-based power-systems researcher who earned his PhD in 2021 from the University of Jaén , Spain, under the supervision of Dr. Francisco Jurado Melguizo. He is currently affiliated with the Department of Electrical Engineering at the same university, where he pursues advanced studies in smart-grid optimisation, renewable-energy integration and meta-heuristic algorithms. Education PhD in Electrical Engineering, University of Jaén, Spain (2021) Thesis: Optimisation de l’écoulement de puissance par les méthodes non-conventionnelles dans les réseaux électriques intelligents (Smart Grid) Research Interests Mouassa’s work spans the entire spectrum of modern power-systems engineering, focusing on: Optimal power-flow computations for hybrid thermal-wind-solar networks Meta-heuristic and bio-inspired optimisation (Electric Eel, Gorilla Troops, Kepler, Bonobo, Dwarf Mongoose, Tuna Swarm, AEO, GTO, FFA, etc.) Integration of FACTS devices (TCSC, SVC, UPFC) to enhance grid stability and power quality Smart-home and smart-grid energy-management systems with storage and demand response Photovoltaic systems, MPPT techniques and active-power filtering under non-ideal grid conditions Stochastic modelling of renewable-generation uncertainty and its impact on isolated and large-scale networks Power-electronics control—Lyapunov-based adaptive sliding-mode schemes for DC-DC converters Publication Profile Between 2017 and 2025 Mouassa authored more than thirty peer-reviewed papers, concentrated in high-impact energy, power-engineering and applied-soft-computing journals. His 2024-2025 output alone includes nine articles that apply cutting-edge swarm and evolutionary algorithms to real-world optimal-power-flow and energy-management problems, demonstrating a clear trajectory toward large-scale, practical implementation of renewable-rich power systems. Notable case studies include the isolated Adrar network in Algeria and various European-scale transmission systems. Scientific Awards No specific prizes, fellowships or medals have been publicly listed to date. Advising & Grants No supervised students or funded projects are explicitly mentioned in the provided materials. Laboratories & Teams Mouassa carries out his research within the Department of Electrical Engineering research group at the University of Jaén, accessing power-system simulation laboratories and computational clusters necessary for large-scale optimisation studies.

Marcel Mongeauمشاهده پروفایل

استاد

• Operations Research
• Global Optimization
• Numerical Optimization
• +۵ مورد دیگر

Marcel Mongeau is a Professor in Operations Research at the École Nationale de l'Aviation Civile (ENAC) in Toulouse, France, affiliated with the OPTIM team under the MORO axis . His research focuses on operational research, global optimization, numerical optimization, and mixed-integer programming, with applications in aeronautics, chemical engineering, life sciences, and other industries. Education PhD in Combinatorics & Optimization (1991), University of Waterloo, Canada MSc in Mathematics (1987), University of Montreal, Canada BSc in Mathematics (1985), University of Montreal, Canada Habilitation to Direct Research (2003), Paul Sabatier University, France Research Interests : Mongeau's work addresses complex optimization problems through mathematical modeling and algorithm development. Key areas include aircraft trajectory planning, contrail avoidance, stochastic programming, and multi-modal transportation optimization. His methodologies integrate exact optimization, heuristic approaches, and computational modeling. Publications Trends : Recent publications emphasize air traffic flow management , contrail-safe trajectories , stochastic aircraft scheduling , and multi-modal transportation synchronization . Applications span environmental impact minimization, machining efficiency, and climate-aware aviation planning. Scientific Recognition : He is a Member of the Academy of Technologies , reflecting his contributions to applied mathematical sciences. Advising & Grants : Mongeau has supervised PhD students and postdoctoral fellows, particularly in optimization and operational research. His research has been supported through collaborations with institutions like IBM, INRIA, and CNRS. Labs & Teams : He is associated with the OPTIM team at ENAC and previously worked with the MOGISA group at LAAS-CNRS and the Optimisation & Interactions group at Paul Sabatier University.

Li Xiaoliمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Professor Li Xiaoli is the Head of the Information Systems Technology and Design (ISTD) Pillar at Singapore University of Technology and Design (SUTD), effective 15 August 2025. An internationally recognised AI researcher with over 30 years in academia and industry, he was previously Department Head of Machine Intellection at A*STAR I²R and Technical Director of the S$35.8 million national AIMfg centre. He holds adjunct professorial appointments at NUS and NTU and has advised Singapore government agencies including MTI, MOE, MOH and SNDGO. Education: PhD, Institute of Computing Technology, Chinese Academy of Sciences, 2001 Research Interests: Prof Li’s work straddles machine learning, data mining, graph learning and time-series analytics, with high-impact applications in smart manufacturing , semiconductors and the digital economy . He pioneered sensor feature learning with deep neural networks and co-coined the term positive-unlabelled (PU) learning ; his 2015 IJCAI paper alone exceeds 1,600 citations. Additional contributions span social & biological network mining, NLP & text analytics, and AI sustainability. Recent Publication Landscape (2022-2025): His latest 15 papers reveal a dual focus on time-series intelligence (domain adaptation, self-supervised representation, sensor alignment, remaining useful-life prediction) and efficient AI deployment (green AI, hardware-aware deep learning, game-theoretic NAS). Interdisciplinary threads link biomedical informatics (sleep stage, gene networks) and advanced NLP (preference optimisation, video grounding), underscoring a strategy of algorithmic innovation coupled with real-world validation . Honours & Awards: IEEE Fellow (2024) Fellow, Asia-Pacific Artificial Intelligence Association (2023) Clarivate Highly Cited Researcher Listed among World’s Top 2% Scientists (Stanford University) Three IEEE/Conference Best Paper Awards for sensor-data and network-mining research Grants, Labs & Industry Collaboration: Prof Li has secured and led more than 10 major collaborative grants with industry partners such as DBS, Singtel and KPMG, including the S$35.8 million AIMfg programme. He directs joint research labs, translating AI advances into aerospace, telecom, insurance and aviation solutions. His group maintains cutting-edge GPU clusters and sensor test-beds for manufacturing analytics, and he currently supervises a large multi-disciplinary team of research staff and graduate students at SUTD.

Bhupesh Mishraمشاهده پروفایل

مدرس

• Data Science and Machine Learning
• Explainable AI
• Citizen Science and Smart Cities
• +۴ مورد دیگر

Dr Bhupesh Mishra is a Lecturer at the University of Hull's Faculty of Science and Engineering, affiliated with the Data Science AI and Modelling Centre (DAIM). He holds a PhD in Modelling and Optimisation of relief items distribution in disaster scenarios from the University of the West of Scotland. His research focuses on Data Science, Machine Learning, Explainable AI, and IoT applications in smart cities and healthcare. Recent work includes studies on climate change awareness in Kathmandu Valley, graduate salary prediction using machine learning, and predictive models for UK electricity pricing. Research Interests: Data Science and Machine Learning Explainable AI Citizen Science & Smart Cities Scheduling & Optimization Edge Computing Natural Language Processing Key Projects: Principal Investigator: Predictive Manufacturing Optimization using Neural Networks (PROMINN) funded by Innovate UK Co-Investigator: Responsible AI project in Burkina Faso funded by the British Academy His articles explore topics ranging from humanitarian logistics optimization to AI-driven healthcare decision tools. Mishra supervises PhD students in AI and data science domains and actively engages in interdisciplinary collaborations across academia and industry.