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Philip BrabazonView profile

Senior Lecturer

• Operational Research
• Logistics
• Mass Customization
• +5 more

Dr. Philip Brabazon is a Senior Lecturer at the University of Portsmouth, affiliated with the School of Organisations, Systems and People and the Centre for Operational Research & Logistics. His work focuses on operational research, logistics, and mass customization in industries such as automotive and manufacturing. He has published extensively on topics including order fulfillment systems, supply chain management, and production economics. Dr. Brabazon is currently accepting PhD students and has supervised 7 theses. His research explores themes like automotive order-to-delivery processes, virtual build-to-order systems, and the application of simulation in operational systems. He has contributed to understanding mass customization strategies, risk analysis in offshore operations, and safety case frameworks. His work bridges theoretical models with practical industry applications, particularly in optimizing supply chains and production systems. Notable publications include analyses of automotive logistics, long-tail retail dynamics, and Markovian approximations in fulfillment systems. His research has been cited over 100 times in key journals like the European Journal of Operational Research and Production and Operations Management.

R RaviView profile

Professor

• Discrete Optimization
• Operations Research
• Computer Science
• +4 more

R Ravi is the Vasantrao Dempo Professor of Operations Research and Computer Science at Carnegie Mellon University's Tepper School of Business. He holds a B.E. from IIT Madras and M.S./Ph.D. from Brown University. Active since 1995, he served as Associate Dean for Intellectual Strategy and Chair of the Future Educational Delivery Committee. He currently directs Analytics Strategy and leads the Center for Intelligent Business. His research focuses on discrete optimization, network optimization, and applications in supply chain logistics and online advertising, supported by NSF, Google, and others. He has advised over two dozen doctoral students and developed graduate courses like Network Optimization. He received the 30-year Test of Time Award (2023) and is an INFORMS Fellow. His academic roles include editorial leadership in Operations Research and FOCS chairmanship.

• Answer Set Programming
• Artificial Intelligence
• Logic Programming
• +4 more

Torsten Schaub is a Professor at the Institute of Computer Science , University of Potsdam. His research focuses on Answer Set Programming (ASP) , constraint solving, temporal reasoning, and combinatorial optimization, with applications in multi-agent pathfinding, product configuration, and course timetabling. Key contributions include ASP-based tools for industrial-scale optimization problems, metric temporal logic implementations, and frameworks for dynamic equilibrium logic. Recent work explores efficient design space exploration, stream reasoning, and multi-shot ASP solving for complex domains. His publications emphasize hybrid ASP systems , integrating constraints and temporal logic, with co-authors across Europe and Asia. He actively develops tools like clingo and Clingraph for practical ASP applications in logistics, bioinformatics, and robotics. The articles reveal a trend toward multi-agent systems (e.g., pathfinding algorithms) and temporal extensions in ASP, combining formal logic with real-world problem-solving. Sub-fields include constraint satisfaction, logical abduction, and declarative modeling for optimization tasks.

• Combinatorial Optimization
• Operations Research
• Stochastic Programming
• +5 more

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.

Justin LipmanView profile

Professor

• Cybersecurity
• Internet of Things (IoT)
• Wireless Communications
• +6 more

Justin Lipman is a Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS). He serves as Director of the Cyber Digital Centre and previously led the RF and Communications Technologies Lab. With over 12 years of industry experience at Intel and Alcatel, his expertise spans cybersecurity, IoT, 5G/O-RAN, digital agriculture, and smart cities. Lipman holds a PhD in Telecommunications Engineering from the University of Wollongong. Research & Funding Secured $35M+ in research grants, including projects like UTS Vault ($8M) and the Nokia 5G Futures Lab. Focus areas: Cybersecurity, IoT infrastructure, wireless communications, and smart agriculture. 24 U.S. patents granted, including innovations in IoT security and metamaterials. Education PhD in Telecommunications Engineering (University of Wollongong, 2004) Industry experience: Chief Architect at Intel (2006–2016), Program Manager at Alcatel-Lucent (2005). Awards & Recognition IEEE Senior Member (2012) Leadership roles: Deputy Chief Scientist (Food Agility CRC), Board Director (Internet of Things Alliance Australia). Teaching & Labs Supervises HDR/PhD students in areas like SDN and IoT. Labs: RF and Communications Lab, BlueSky initiative.

William YeohView profile

Associate Professor

• Computational Methodologies
• Cyberphysical Systems & Internet of Things
• Human-Computer Interaction
• +3 more

William Yeoh is an Associate Professor in the Department of Computer Science & Engineering at Washington University in St. Louis’s McKelvey School of Engineering. His research focuses on artificial intelligence (AI), particularly enabling human-AI collaboration through human-aware decision-making algorithms. He holds a PhD from the University of Southern California (2010), an MS from USC (2007), and a BSE from the University of Pennsylvania (2004). His expertise includes distributed constraint optimization, automated planning, and scheduling. His work has been funded by NSF and DARPA, and he has held prior roles at New Mexico State University (2012–2017), Singapore Management University, and the University of Massachusetts, Amherst. Notable awards include an NSF CAREER Award (2016) and recognition as one of AI’s 10 to Watch by IEEE Intelligent Systems. Yeoh’s research affiliations include the Division of Computational & Data Sciences and the Center for Trustworthy AI in CPS. He has contributed to AI conferences like AAAI, AAMAS, and IJCAI, serving in roles such as program committee member, tutorial co-chair (AAAI/PRIMA), and student outreach co-chair (AAAI). His recent work includes developing the TRACE-cs scheduling system and advancing generative AI through a $1.5M DoD grant.

Jie XuView profile

Associate Professor

• Edge Computing
• Wireless Communications
• Federated Learning
• +5 more

Jie Xu is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Florida, part of the College of Engineering. Their primary research area is Computer Engineering, with a focus on Edge computing, wireless communications, federated learning, and reinforcement learning. Xu holds a Ph.D. from UCLA (2015), M.S. and B.S. from Tsinghua University (2010/2008). Key research interests include federated learning frameworks, edge computing optimization, quantum networking, and adversarial machine learning. Their work addresses challenges in distributed systems, IoT integration, and energy-efficient AI solutions. Xu has been recognized with prestigious awards including the NSF CAREER Award (2021) and the Distinguished Ph.D. Dissertation Award (UCLA, 2015). Recent publications emphasize advancements in federated learning techniques (e.g., FedALT, LoRA-FAIR), quantum entanglement routing, and carbon-aware distributed systems (CAFE). Their research bridges theoretical foundations with practical applications in edge computing and wireless networks. Xu’s lab focuses on collaborative edge intelligence, with projects involving automated neural network ensembles and mobility-assisted federated learning. They actively mentor students seeking Ph.D. opportunities in machine learning and communications, requiring strong mathematical and programming skills.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +5 more

Dr. Kit Yan Chan is a Senior Lecturer at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University. His research focuses on Artificial Intelligence, Machine Learning, Deep Learning, and Optimization, with applications in wireless communications, signal processing, and power systems. He has held editorial roles in journals such as Neurocomputing, Sensors, and the International Journal of Ad Hoc and Ubiquitous Computing. His teaching spans courses like Transmission and Interface Design, Advanced Research in AI, and Mobile Cloud Computing. Dr. Chan's work emphasizes interdisciplinary approaches, combining computational intelligence with engineering challenges. He has contributed to over 100 publications in areas such as resource allocation in heterogeneous networks, deep learning for load forecasting, and underwater acoustic communication systems. His research bridges theoretical advancements and practical implementations, addressing real-world problems in telecommunications, energy systems, and smart technologies. His recent projects include optimizing energy efficiency in 5G networks, developing robust power control strategies, and advancing neural network architectures for real-time applications. Collaborations with industry and global institutions underscore his commitment to impactful research.

Shiyong LuView profile

Professor

• Big Data Workflows
• Cloud Computing
• Scientific Workflows
• +3 more

Dr. Shiyong Lu is Professor of Computer Science at Wayne State University and Director of the Big Data Research Laboratory. He holds a PhD from State University of New York at Stony Brook and has published over 140 papers in venues including IEEE Transactions on Services Computing and IEEE Transactions on Knowledge and Data Engineering. Research focuses on: Architecture of big data workflow systems Secure execution in cloud environments Optimization algorithms for distributed computing Provenance management for scientific workflows Recent work demonstrates strong emphasis on secure workflow execution, with 80% of last 15 publications addressing trusted computing environments. Leads NSF-funded DATAVIEW project for cloud-based big data analytics. Awarded IEEE TCSVC Outstanding Service Award (2023). Teaches core courses in Database Management Systems and Data Modeling. Supervised 17 PhD graduates, 10 of whom hold faculty positions. Editorial board member for International Journal of Big Data and International Journal of Big Data Intelligence.

Luca Di GasperoView profile

Associate Professor

• Artificial Intelligence
• Combinatorial Optimization
• Metaheuristics
• +1 more

Luca Di Gaspero is an Associate Professor of Information Technology at the University of Udine, specializing in metaheuristic optimization techniques. His research enhances combinatorial optimization through hybridization of algorithms for scheduling, routing, and industrial applications. Research spans artificial intelligence in optimization, scheduling algorithms for manufacturing/healthcare, and metaheuristic framework development. Recent publications focus on LLMs in optimization, parallel batch scheduling, and energy-efficient manufacturing. Key Contributions: Developed EasyLocal++ framework for local search algorithms Advanced multi-neighborhood simulated annealing techniques Applied metaheuristics to healthcare logistics and emergency services

Vincenzo Della MeaView profile

Associate Professor

• Medical Informatics
• Digital Pathology
• Biomedical Ontologies
• +3 more

Vincenzo Della Mea is an Associate Professor of Information Processing Systems at the University of Udine's Department of Mathematical, Computer and Physical Sciences. His research focuses on medical informatics, digital pathology, biomedical ontologies, and AI applications in healthcare. He holds abilitation to full professorship and is a member of the WHO Italian Collaborating Centre for International Classifications (WHO-FIC), ICHI Task Force, and ESDIP's Executive Board. He leads the EU MSCA Doctoral Network BosomShield (2022-2026) and previously managed the AIDPATH Marie Curie project. As an editor for journals like Digital Health and Journal of Pathology Informatics , he bridges academia and practice. Beyond academia, he is a poet with awards, including the Nelle terre dei Pallavicino Prize for his 2004 collection Algoritmi , and organizes interdisciplinary events merging science and literature. Education & Roles : Holds a professorship in Computer Science and teaches courses on web technologies, medical informatics, and AI across multiple degree programs. Previously served as Vice-President of SIBIM (Italian Biomedical Informatics Society). Research Interests : Specializes in digital pathology, medical AI, telemedicine, and standardization of health classifications like ICD-11 and ICF. His work emphasizes ontology harmonization and machine learning for clinical decision support. Recent Contributions : Published on WHO classification harmonization, AI-driven pathology tools, and energy-sector LLM applications. Active in editorial roles and international collaborations, including the HEROHE Challenge for breast cancer analysis. Awards & Recognition : 2023 – Poetry collection Clone 2.0 (neurally generated) 2005 – Nelle terre dei Pallavicino Award Grants & Projects : Led EU-funded projects (AIDPATH, BosomShield) and contributed to initiatives like the ICD-11 Mortality Coding System. Collaborates with institutions like AcegasApsAmga on AI for utilities. Labs & Initiatives : Involved in digital pathology workflow optimization and educational platforms for medical coding and AI ethics.

• Cloud Computing
• Edge Computing
• Task Scheduling
• +5 more

Dr. Zhijun Wang is an Associate Professor of Research in the Department of Computer Science and Engineering at The University of Texas at Arlington. His work focuses on cloud and edge computing, resource management, task scheduling, and network traffic control. He holds a PhD in Computer Science from UTA (2005), an MS in Electrical Engineering from Penn State (2001), and a BS in Physics from Huazhong University of Science & Technology (1992). His research explores internet traffic control mechanisms , cloud resource allocation , and microservices architecture . Recent projects include NSF-funded work on tail latency guarantees for microservices ($600k grant, 2022-2026) and industry collaborations with Alibaba on datacenter transport protocols ($148k grant, 2018-2021). Publications highlight innovations like CurTail (tail latency scheduling) and Tailguard (data-intensive task scheduling). His work often combines price-aware protocols with distributed scheduling algorithms . Teaching focuses on foundational topics: discrete math, computer networks, and cloud computing. Grants: NSF (2022), Alibaba (2018) Key Patents: Tail Latency Scheduling (2023), Database Target Enforcement (2024) Recent Publications: 15+ papers since 2020 in top-tier venues

Jaejin JangView profile

Associate Professor

• Production System Optimization
• Supply Chain Management
• Biofuel Supply Chains
• +1 more

Jaejin Jang is Associate Professor and Department Chair of Industrial & Manufacturing Engineering at UW-Milwaukee. His research optimizes production systems and supply chains, with recent focus on biofuel subsidies and water-energy nexus management. Publications appear in International Journal of Energy Research and other leading journals. He developed algorithms for semiconductor fabrication throughput improvement and scheduling optimization under uncertainty. His doctoral work at Purdue focused on industrial system efficiency.

Benjamin RaichelView profile

Associate Professor

• Computational Geometry
• Algorithms
• Theoretical Computer Science
• +3 more

Benjamin Raichel is an Associate Professor in the Department of Computer Science at the University of Texas at Dallas, affiliated with the Erik Jonsson School of Engineering and Computer Science. His research focuses on computational geometry and geometric approximation algorithms, with contributions to areas such as Voronoi diagrams, Fréchet distance, clustering, and algorithmic efficiency. Dr. Raichel holds a PhD from the University of Illinois Urbana-Champaign under the supervision of Sariel Har-Peled. He currently teaches Computational Geometry (CS 6319) and has advised multiple PhD students, including Md. Billal Hossain and Jonathan James Perry. His work is supported by NSF grants such as 'Shape Matching in a Messy World Using Fréchet Distance' and 'Metric Violation Distance: Hardness and Approximation.' He is a member of the Algorithms and Theory Group at UT Dallas.

• Energy Systems Modeling
• Power System Optimization
• Renewable Energy Integration
• +3 more

Dr. Ross Baldick served as a Visiting Professor at the Technological Research Institute (IIT) of Comillas Pontifical University from October 2009 to May 2014. His research focuses on energy systems modeling, particularly in wind integration, power system optimization, and electricity market design. He contributed to advancements in unit commitment algorithms, reserve management, and renewable energy scheduling. During his tenure, Baldick delivered seminars on topics such as transmission expansion cost allocation, generation capacity optimization, and wind-energy market dynamics at Comillas University's Research Center. His work bridges theoretical modeling and practical applications in sustainable energy systems. Notable outputs include peer-reviewed publications in IEEE Transactions on Sustainable Energy and conference presentations at events like the FERC Technical Conference. His research emphasizes improving grid reliability through innovative approaches to renewable integration and market mechanisms.