Find Academic Supervisors

• Scheduling
• Supply Chain Management
• Operational Research
• +4 more

Ali Skaf is an Associate Professor at CESI LINEACT, affiliated with the Engineering and Numerical Tools Research team. His research focuses on scheduling problems, supply chain resilience, multi-objective optimization, and port logistics. He holds a PhD in Automatics from Belfort Montbéliard University of Technology (2020) and a Master's in Computer Science from the Lebanese-French University (2012). His educational activities span Computer Science, Operational Research, and Mathematics across engineering cycles and preparatory courses. Skaf's work emphasizes port terminal operations, including quay crane scheduling, yard truck coordination, and mixed-integer programming models for container handling. His recent publications (2018–2022) highlight contributions to NP-completeness analysis of scheduling problems and optimization algorithms for maritime logistics. His research integrates exact methods, heuristics, and computational complexity analysis to enhance supply chain efficiency. No scientific awards are explicitly mentioned, though his active publication record reflects ongoing scholarly engagement. His advising and grants details are not documented here, but his team's focus on numerical tools and engineering applications suggests collaborative projects in industrial automation and logistics systems. Labs/Teams: Member of the Engineering and Numerical Tools Research team at CESI LINEACT, focusing on applied operational research and port logistics solutions.

• Combinatorial optimization
• Predictive maintenance
• Multi-objective scheduling
• +5 more

BENCHEIKH Ghita is a Researcher-Lecturer at CESI LINEACT, specializing in Engineering and Numerical Tools. She holds a PhD in Industrial Engineering and Computer Science from the National Engineering School of Tarbes and National Polytechnic Institute of Toulouse (2020) and a Master’s in Operational Research and Decision-Making from the University of Bordeaux (2016). Her research focuses on combinatorial optimization, predictive maintenance, multi-objective scheduling, and metaheuristics, with applications in production planning and maintenance systems. She teaches Computer Science, Operational Research, and Mathematics at the 4th and 5th-year engineering curriculum and master’s level. Her work integrates advanced methodologies such as multi-agent systems, genetic algorithms, and machine learning to address challenges in manufacturing efficiency, predictive maintenance, and logistics optimization. Key contributions include pioneering approaches for joint scheduling of production and maintenance activities, leveraging health assessment of technical resources. She has published extensively in top-tier journals and conferences, including Journal of Manufacturing Systems and IEEE ETFA . Her research has explored topics like vehicle routing problems, flow network optimization, and data-driven predictive analysis for equipment reliability. She is affiliated with the Engineering and Numerical Tools research team at CESI LINEACT, contributing to cutting-edge projects in sustainable development and smart manufacturing systems.

Kenji DoeringView profile

Researcher

• Water-Energy Nexus
• Climate Change Impacts
• Hydropower Systems
• +3 more

Kenji Doering is a Researcher and Postdoctoral Associate in Biological and Environmental Engineering at Cornell University, affiliated with the College of Agriculture and Life Sciences. He serves as a Research and Outreach Specialist at the New York State Water Resources Institute. His work focuses on the water-energy nexus, climate change impacts, hydropower systems optimization, and renewable energy integration. Doering holds a B.S. in Biophysics from the University of Washington before transitioning to Environmental Engineering for his M.S. and Ph.D. at Cornell. His research combines interdisciplinary approaches to address energy grid resilience, climate adaptation strategies, and multi-objective reservoir management. Notable projects include analyzing the Susquehanna River's reservoir control policies and advancing community science frameworks for river eel conservation. He has contributed to over 20 peer-reviewed articles since 2015, spanning topics from nanopore DNA sequencing innovations to spatiotemporal energy grid modeling under climate change scenarios. Doering’s work bridges engineering, environmental science, and policy, with a particular emphasis on data-driven solutions for sustainable resource management. His outreach efforts integrate public participation in ecological monitoring and education programs.

Heinrich WörtcheView profile

Professor

• Sensor Technology
• Artificial Intelligence
• Robotics
• +4 more

Heinrich Wörtche is a Professor of Sensors and Smart Systems at Eindhoven University of Technology, leading research in sensor-driven digitization of industrial processes. He holds affiliations including Guest Scientist at the University of Groningen and Senior Researcher roles. His academic career spans positions at institutions like the University of Münster and Rijksuniversiteit Groningen. Education: Doctor rerum naturalium (Nuclear and High Energy Physics), Technical University Darmstadt (1994) Research Interests: Focuses on AI-driven sensor systems, robotics, cyber-physical systems, and industrial digitization. Key areas include visible light communication optimization, autonomous robot localization, and dementia behavior monitoring through sensor technology. His work aligns with UN SDGs for innovation and sustainable industry. Projects: Leads initiatives like MINDD (dementia assistive tech), NXT GEN Autonomous Factory, and XAIPre (explainable AI for maintenance). Active in interdisciplinary collaborations across engineering, healthcare, and AI. Awards: Best Paper Award - OL2A 2023 Grants & Labs: Coordinates EU-funded projects and leads the Sensors and Smart Systems research group. Involved in developing miniature wireless explorative sensor systems and digital twin applications for industrial processes.

José Carlos AlvesView profile

Associate Professor

• Reconfigurable Digital Systems
• FPGA Technology
• Underwater Robotics
• +3 more

José Carlos Alves is an Associate Professor at the Faculty of Engineering of the University of Porto and a Senior Researcher at INESC TEC's Robotics and Autonomous Systems Centre. He holds a PhD in Electrical and Computer Engineering (1998) and has taught advanced digital design, microelectronics, and circuit analysis. His primary research focuses on reconfigurable digital systems (FPGA-based), underwater robotics, and acoustic localization. Education: BEng (1985) and PhD (1998) in Electrical and Computer Engineering from the University of Porto. Early career included roles as a teaching assistant and research at INESC Porto's CAD & Microelectronics group. Research interests span FPGA technology, marine robotic systems, sensor optimization, and underwater navigation. He has supervised multiple theses on topics like underwater acoustic systems, sensor calibration, and autonomous vehicle design. Contact: jose.c.alves@inesctec.pt.

Adwait SharmaView profile

Assistant Professor

• Human-Computer Interaction
• Wearable Technologies
• Machine Learning
• +5 more

Dr. Adwait Sharma is an Assistant Professor in Computer Science at the University of Bath, leading the Advanced Interaction and Sensing (AIS) Lab. His research focuses on enabling always-available computing through microgesture recognition, wearable technologies, and machine learning, with applications in healthcare, robotics, and smart environments. He holds a PhD from Saarland University and a Master's from the Media Interaction Lab, Austria. Education: PhD (HCI, Saarland University), MSc (Media Interaction, Austria) Research Interests: Interaction Design, Sparse Sensing, Real-Time Recognition, E-textiles His work bridges user-centered design with data-driven insights, addressing scenarios where traditional input methods are impractical. Recent projects include domestic robotics idle-time utilization, emotion-aware in-car systems, and electrotactile feedback for surgery. He has received awards such as the People's Choice Best Demo Award (CHI'23) and holds leadership roles in top conferences like UIST and CHI. Advised students include PhD researchers in wearable computing, robotics, and automotive interaction. His lab collaborates with institutions across Europe under the EMIL project, advancing XR innovation.

Jie LiView profile

Senior Lecturer

• Machine Learning
• Deep Learning
• Computer Vision
• +4 more

Dr. Jie Li is a Senior Lecturer at Teesside University's School of Computing, Engineering and Digital Technologies, specializing in Cybersecurity & Networks. He holds a PhD in Fuzzy Interpolation Systems from Northumbria University (2018), with prior master's and bachelor's degrees from the same institution. His research focuses on Machine Learning, Deep Learning, Computer Vision, and Generative AI, with applications in healthcare, IoT, and cybersecurity. Key projects include the D-PRIV data anonymization system (Innovate UK-funded, £90K), SuDs flood monitoring collaboration (£400K), and MC-IoT IoT security initiative. He has industry experience as AI Lead at THERAPYAUDIT and CTO of NewApex Ltd., developing healthcare AI systems and the StreetAway mobile app. Dr. Li actively engages in academic service, serving on program committees for IEEE conferences and the NCSC Cyberfirst Schools Scheme. His current research explores generative AI for synthetic data and real-time system optimization, aiming to bridge academic research with industrial applications.

• Wireless Sensor Networks
• Mobile Computing
• Crowdsourcing Systems
• +5 more

Qi Han is a Professor in the Department of Computer Science at Colorado School of Mines, with a distinguished research career spanning over two decades in wireless sensor networks, mobile computing, and distributed systems. Their work bridges theoretical foundations with practical applications in augmented reality, robotics, and smart city infrastructure. Research interests focus on three interconnected domains: 1) Networked robotic systems including UAV-UGV collaboration and swarm intelligence, 2) Mobile augmented reality with emphasis on real-time performance and spatial accuracy, and 3) Crowdsourced data systems for smart city applications. Recent work demonstrates sophisticated integration of these areas, particularly in energy-aware path planning, communication-resilient robot teams, and quality-aware AR systems. Analysis of recent publications (2022-2025) reveals a strategic shift toward integrating large language models with physical systems, developing more robust communication frameworks for drone networks, and creating comprehensive evaluation metrics for AR applications. The research trajectory shows increasingly complex system integration, moving from single-device solutions to coordinated multi-agent systems with sophisticated networking requirements. Principal Investigator for multiple NSF-funded projects on networked robotic systems Recipient of best paper awards at MobiQuitous and DCOSS conferences Senior member of IEEE Computer Society Organizing committee member for ACM MobiSys and IEEE PerCom conferences As a research advisor, Qi Han has mentored numerous graduate students who have gone on to publish in top-tier venues and secure positions in both academia and industry. Their lab maintains strong industry partnerships with companies developing AR/VR technologies and drone systems. Current research directions include energy-aware coordination of heterogeneous robot teams, privacy-preserving crowdsensing frameworks, and adaptive AR systems for industrial applications.

• Computer Vision
• Natural Language Processing
• Computational Thinking in Education
• +3 more

Jiyoung Kim is a Researcher in computer science, with a focus on computer vision , natural language processing , and geospatial data analysis . She has collaborated extensively across disciplines, particularly in robotics , educational technology , and signal processing . Her research interests include: Developing advanced diffusion models for high-fidelity talking head generation . Creating automated pipelines for detoxifying Korean language in AI systems. Applying deep learning to haze removal and indoor navigation for accessibility. Exploring 6G communication through computer vision-aided beamforming . Recent publications highlight trends in multi-task learning , unsupervised segmentation , and geospatial knowledge graphs . Her work bridges theoretical and applied domains, from hardware design to educational interventions for computational thinking in early childhood.

Christian SchroerView profile

Professor

• X-ray Microscopy
• X-ray Optics
• Synchrotron Radiation
• +4 more

Christian Schroer is a Professor at the University of Hamburg and Leading Scientist at DESY, where he directs the scientific program of the PETRA III synchrotron radiation source. His research focuses on X-ray microscopy, X-ray optics, and their applications in materials science, nanotechnology, and condensed matter physics. He has contributed to the strategic development of PETRA IV, an ultra-low emittance synchrotron source, and co-founded the Helmholtz Imaging platform for data-driven imaging science. His career includes roles as a professor at TU Dresden (2006–2014), a scientist at DESY, and postdoctoral research at institutions like the University of Maryland and RWTH Aachen University. Schroer’s group develops advanced X-ray microscopy techniques and collaborates on projects ranging from solar cell analysis to catalyst dynamics. Education: PhD in Mathematical Physics, University of Cologne (1995) Studies in Physics at RWTH Aachen University (1986–1992) Habilitation in Physics, RWTH Aachen (2004) Research Interests: X-ray nanoscience and optics Aberration-corrected lenses and focusing techniques In situ and operando characterization of materials 3D tomography and multimodal imaging Synchrotron and free-electron laser applications Grants & Collaboration: Lead scientist for PETRA III and PETRA IV projects Member of Helmholtz Imaging initiative Active in international collaborations on X-ray microscopy and photon science Labs & Teams: X-ray microscopy group at DESY PtyNAMi (Ptychographic Nano-Analytical Microscope) facility

Goga CvetkovskiView profile

Professor

• Electric Machines Design
• Optimization Algorithms
• Renewable Energy Systems
• +3 more

Prof. Goga Cvetkovski is a Full Professor at the Faculty of Electrical Engineering and Information Technologies (FEIT), Ss. Cyril and Methodius University in Skopje, since 2012. Previously, he held roles including Associate Professor (2007–2012), Assistant Professor (2003–2007), and Assistant (1998–2002). He is affiliated with the Institute of Electrical Machines, Transformers and Apparatus. His research focuses on electric machine design, optimization algorithms (e.g., cuckoo search, genetic algorithms), and virtual reality (VR) applications in mechatronics education. Research Interests Optimal design of permanent magnet motors (PM BLDC, PM synchronous motors) Reduction of cogging torque and harmonic effects Energy efficiency optimization in electric drives Virtual reality-based educational tools for mechatronics Finite element analysis (FEA) for motor performance Key Contributions Prof. Cvetkovski has pioneered the ViMeLa project, an innovative VR platform for mechatronics training. His work emphasizes nature-inspired optimization algorithms for motor design, with applications in renewable energy systems and electric vehicles. Recent studies include multi-objective optimization of motor topologies and harmonic impact analysis. Awards & Recognition No specific honors are listed in the provided texts. Lab/Team Affiliation He leads research within the Institute of Electrical Machines, focusing on electromechanical systems and sustainable energy solutions.

• Computational Social Choice
• Algorithmic Game Theory
• Formal Methods
• +6 more

Jie He is a PreDoc Researcher at the Department of Cyber-Physical Systems, Technische Universität Wien. His research spans computational social choice, algorithmic game theory, and formal methods in robotics and IoT systems. He works on multidisciplinary problems involving complexity analysis, fair division, and preference modeling. Current projects: EdgeAI (2022–2025), TAIGER (2023–2027), ADEX (2020–2024) Key collaborations: Research with R. Grosu, E. Bartocci, D. Nickovic Research interests focus on computational aspects of collective decision-making , including fair division, matching problems, and preference modeling. He works on both theoretical foundations (e.g., parameterized complexity) and practical applications (e.g., robotic-IoT systems). His publication history reveals deep expertise in computational complexity of social choice problems, with recent work on 3D stable roommates , fair division in graph-structured settings , and preference modeling through Euclidean and Manhattan geometries. As an advisor, he supervised diploma theses on: Optimization strategies for 5G transceivers Dynamic object detection in multi-agent systems His work appears in top conferences like ACM/IEEE DAC, ICSE, and various computational social choice venues.

Thomas BäckView profile

Professor

• Artificial Intelligence
• Evolutionary Computation
• Machine Learning
• +6 more

Thomas Bäck is a Full Professor at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, and Chief Scientist at NORCE Norwegian Research Center, Norway. His career spans roles as Director of the Center for Applied Systems Analysis (Germany) and Vice Scientific Director at LIACS (since 2017). He holds additional positions like Adjunct Professor at the University of Calgary and Visiting Professor at Xi'an Jiaotong University. Bäck's research focuses on evolutionary computation, machine learning, and their applications in sustainable smart industry and health. He leads interdisciplinary initiatives like the Society, Artificial Intelligence, and Life Sciences (SAILS) program, with expertise in natural computing, data-driven optimization, and quantum computing challenges. IEEE Fellow (2022) Member of KNAW (2021) IEEE CIS Pioneer Award (2015) Fellow of International Society of Genetic and Evolutionary Computation (2003) Best PhD Thesis Award from German Society of Computer Science (1995) Bäck has graduated 22 PhD students and currently supervises 14, with 62 MSc students mentored. His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and Associate Editor for multiple journals. He has secured grants from the Dutch Research Council and The Research Council of Norway for projects like ECOLE and CIMPLO.

Banafsheh KhosraviView profile

Senior Lecturer

• Operational Research
• Logistics
• Mathematical Modelling
• +6 more

Dr Banafsheh Khosravi is a Senior Lecturer in Operational Research at the University of Portsmouth, affiliated with the School of Mathematics and Physics. She is a member of the Logistics, Operational Research and Analytics Research Group (LORA) and the Centre for Operational Research and Logistics (CORL). Her research focuses on mathematical modelling, combinatorial optimization, and applications in transportation and healthcare. She holds a PhD in Management Science from the University of Southampton, with earlier degrees in Industrial Engineering. Before joining Portsmouth, she worked at the University of Southampton's CORMSIS Centre and the University of Westminster's Health and Social Care Modelling Group. Dr Khosravi teaches Operational Research, Modern Computational Methods, and Supply Chain Management courses. She has supervised numerous academic projects and led industry-funded research with EPSRC, ESA, NHS, RSSB, and Arriva. Her work addresses challenges like railway scheduling, routing optimization, and healthcare capacity planning. She is a Fellow of the Higher Education Academy and contributes to professional organizations including The OR Society and IAROR. Her research trends span transportation logistics (railway rescheduling, electric vehicle routing) and healthcare systems (NHS patient activity forecasting). Recent work emphasizes risk-based surveillance and digital performance analysis in undersea infrastructure and insurance sectors. Collaborations with industry partners ensure practical impact across sectors like rail networks and space operations. Awards: Fellow of the Higher Education Academy (FHEA) Labs/Teams: LORA Group, CORL Centre, Intelligent Transport Research Cluster Grants: Funded by EPSRC, ESA, NHS, RSSB, Arriva

• Operations Research
• Combinatorial Optimization
• Operational Research
• +3 more

Edmund Burke is a Professor and Vice Chancellor at Bangor University. His research focuses on operations research, combinatorial optimization, and algorithm development, with notable contributions to scheduling, hyper-heuristics, and airport ground movement optimization. He has published extensively on topics such as constraint propagation, routing algorithms, and multi-objective optimization. Research interests include algorithmic approaches for real-world problems, such as airport slot allocation, maintenance crew scheduling, and timetabling. His work often integrates heuristics and metaheuristics to tackle complex optimization challenges. Recent publications explore cutting-edge techniques like memetic algorithms for ground movement and hyper-heuristics for nurse rostering. Burke has contributed to interdisciplinary projects, including collaborations with Danish railways and airport logistics systems. He actively participates in university administration, organizing alumni events and engaging in outreach activities such as exhibitions and community days. His academic leadership roles underscore his commitment to both research excellence and institutional governance.