Find Academic Supervisors

• Accelerator Physics
• Beam Dynamics
• Radio-Frequency Photoinjectors
• +5 more

Thorsten Kamps is a Professor of Physics at Humboldt University of Berlin and Head of the Department of High-Brilliance Electron Beams at Helmholtz-Zentrum Berlin . He is also Deputy Project Manager for the Superconducting RF Electron Accelerator Laboratory SEALAB . Education: Graduate Physicist (Dipl.-Phys.) from TU Dortmund (formerly University of Dortmund), Doctor rerum naturalium (Dr. rer. nat.) from Humboldt University of Berlin Research Interests: Particle accelerator beam dynamics, diagnostics, superconducting radio-frequency photoinjectors (SRF photoinjectors), photocathode growth, instrumentation for bright electron beams, free-electron lasers, ultrafast scattering sources, and future trends of accelerator-driven light sources Expertise: Accelerator physics, beam physics of electron beams, photoinjectors, storage rings, electron beam diagnostics, free-electron lasers, and project management of large-scale accelerator projects Labs/Teams: Involved with SEALAB (Superconducting RF Electron Accelerator Laboratory), BERLinPro (Berlin Energy Recovery Linac Project), and collaborations with institutions like Royal Holloway University of London, DESY, and DELTA/University of Dortmund Publications Trends: Thorsten Kamps’ recent works focus on SRF photoinjectors, beam diagnostics, photocathode development, and thermal load studies. His research spans applications in ultrafast electron diffraction, laser-driven acceleration, and energy-recovery linacs, with collaborations across Europe and the US. Topics include interferometric beam monitoring, multi-alkali antimonide materials, and solenoid alignment for beam control. Teaching: Promotes education in accelerator physics through BSc and MSc courses, and supervises BSc, MSc, and PhD students. Develops practical experiments for internships in accelerator physics.

• Explainable Artificial Intelligence
• Type-2 Fuzzy Logic Systems
• Computational Intelligence
• +12 more

Hani Hagras is a Professor in the School of Computer Science and Electronic Engineering at the University of Essex, UK , where he leads pioneering research in type-2 fuzzy logic systems and explainable artificial intelligence (XAI) . His work bridges theoretical advances with real-world applications in smart infrastructure, healthcare, telecommunications, and ambient intelligence. His research interests include explainable AI, type-2 fuzzy logic, computational intelligence, machine learning, human-computer interaction, and intelligent systems. He investigates how fuzzy systems can enhance the transparency, robustness, and adaptability of AI models, particularly in uncertain and dynamic environments. His recent work explores applications in genomics, predictive maintenance, and smart grid optimization. The publication trends show a strong focus on integrating fuzzy logic with deep learning and optimization techniques to create interpretable models. His work frequently appears in top-tier journals such as IEEE Transactions on Fuzzy Systems , IEEE Transactions on Artificial Intelligence , and Neurocomputing . He emphasizes real-world applicability, often collaborating with industry partners like BT. ORCID: 0000-0002-2818-5292 He has advised numerous PhD and Master’s students, including Mehrin Kiani, Ashish Bhatia, and Hugo Leon-Garza, many of whose research projects focus on workforce optimization, healthcare AI, and intelligent systems. His research has been supported by major grants, particularly in smart environments and telecommunications. Hani Hagras is actively involved in leading research labs and teams at the University of Essex, including work on augmented reality for field service support, georeferenced data visualization , and ambient-assisted living systems. His team develops fuzzy logic frameworks for real-time decision-making in complex environments.

• Evolutionary Computation
• Multi- and Many-Objective Optimization
• High-Dimensional Visualization
• +3 more

Dr. Amin Ibrahim is an Associate Teaching Professor in the Faculty of Business and Information Technology at Ontario Tech University, specializing in evolutionary computation, optimization, and machine learning. He holds a PhD and MASc in Computer Engineering from Ontario Tech University and a BASc in Computer Engineering from the University of Toronto. His research focuses on multi- and many-objective evolutionary algorithms, high-dimensional visualization, and applying optimization techniques to big data challenges. He has won two Teaching Excellence Awards at Ontario Tech University (2008 and 2018) and contributed to peer-reviewed journals, book chapters, and conferences. Education: PhD in Computer Engineering, Ontario Tech University MASc in Computer Engineering, Ontario Tech University BASc in Computer Engineering, University of Toronto Research interests span evolutionary algorithms, machine learning applications, and optimization-driven solutions for complex systems. Recent work includes electric vehicle infrastructure planning and resilient supply chain design. His articles address multi-objective optimization, risk management, and sustainable transportation. Awards and Honors: Teaching Excellence Award (2008, 2018) Graduate Continuing Scholarship (2008) Graduate Entrance Scholarship (2007) Professional Involvement: Session chair at IEEE World Congress on Computational Intelligence (2016), workshops on cybersecurity and supply chain management, and contributions to IBM CASCON (2008). Active in the Business Analytics & AI Research Group, focusing on transforming data into actionable insights for business decision-making. Teaches courses ranging from cryptography to discrete mathematics and business analytics.

• Model Predictive Control (MPC)
• Optimization Techniques
• Aerospace Engineering
• +3 more

Eric Kerrigan is a Professor of Control and Optimization at Imperial College London's Department of Electrical and Electronic Engineering, part of the Faculty of Engineering. He holds a joint appointment in the Department of Aeronautics. His research focuses on Model Predictive Control (MPC), numerical optimization techniques, and their applications in aerospace, renewable energy, and information systems. Key projects include developing real-time optimization algorithms for embedded systems, co-design frameworks for closed-loop systems, and drag reduction in aerodynamics. He has supervised over 30 PhD students and post-doctoral researchers, many of whom have secured academic positions. Education: PhD in Control Engineering from the University of Cambridge and BSc in Electrical Engineering from the University of Cape Town. Research interests emphasize robust control methods, dynamic optimization, and interdisciplinary applications. Notable contributions include the ICLOCS (Imperial College Optimal Control Software) toolbox and frameworks for energy-efficient UAV communication networks. His work is funded by EPSRC, the European Commission, and industry partners like Siemens and ESA. Funding and collaborations include grants from the Royal Academy of Engineering and Royal Society, with consulting roles in industrial control systems. Editorial roles include Associate Editor for IEEE Transactions on Automatic Control and former Senior Editor for IEEE Transactions on Control Systems Technology . Labs and affiliations include the Control and Power Research Group, Energy Futures Lab, and Space Lab at Imperial College.

Kamesh MunagalaView profile

Associate Professor

• Algorithm design
• Discrete optimization
• Approximation algorithms
• +8 more

Kamesh Munagala is an Associate Professor of Computer Science at Duke University, where he has been employed since 2004. His research spans theoretical computer science with applications in e-commerce, databases, data analysis, and networks. He serves as an Area Editor for PeerJ Computer Science and has held leadership roles including Director of Graduate Studies for the Duke CS department from 2012 to 2015. Education: PhD in Computer Science from Stanford University (2003) BTech from IIT Bombay (1998) Munagala's research focuses on algorithm design and discrete optimization, particularly in scenarios with uncertain inputs. His work encompasses approximation algorithms, online algorithms, and algorithmic game theory. Recent research has concentrated on two main themes: (1) Persuading optimizers or learners toward certain objectives through information revelation and pricing, and (2) Ensuring fairness to groups based on proportionality and stability in resource allocation and societal decision making contexts. His theoretical work has practical applications in designing data networks, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. Analysis of Munagala's recent publications reveals a strong trend toward interdisciplinary research at the intersection of computer science, economics, and social choice theory. His work shows increasing sophistication in handling fairness constraints across multiple dimensions, particularly in societal decision-making contexts like school assignment, participatory budgeting, and redistricting. There is also a notable expansion into the emerging area of large language models for combinatorial optimization, reflecting the field's evolution toward AI-assisted algorithmic solutions. His research consistently bridges theoretical foundations with real-world applications requiring nuanced approaches to resource allocation under uncertainty. Scientific Awards: NSF CAREER Award (2008) Alfred P. Sloan Research Fellowship (2009) Best paper award at WWW 2009 conference Munagala has served as Director of Graduate Studies for the Duke CS department from 2012 to 2015 and was a Visiting Research Professor at Twitter in 2012. His research has been supported by prestigious grants including the NSF CAREER award. He actively mentors students, with many of his publications featuring student co-authors across his two main research thrusts. His work addresses fundamental challenges in computing efficient solutions under uncertainty, designing pricing and incentive mechanisms for selfish agents, and developing fairness-preserving algorithms for societal decision making. Munagala leads research projects in two broad categories: proportionality in resource allocation and societal decision making, and asymmetric information and persuasion in learning and optimization. His work on proportionality explores fairness concepts like proportionality or core-stability across various societal contexts, while his research on asymmetric information addresses how parties with incomplete information collaborate or compete to achieve optimization objectives through strategic information revelation.

Simon J E TaylorView profile

Professor

• Modelling & Simulation
• Digital Infrastructures
• Cloud Computing
• +7 more

Professor Simon J E Taylor is a Professor of Computer Science and Vice Dean Research at the College of Engineering, Design and Physical Sciences, Brunel University London. He is Co-Director of the Modelling and Simulation Research Group and Adjunct Director of the Smart Technology Advancements for Health and Rehabilitation (STAHR) Centre. Research Interests: Modelling & Simulation Digital Infrastructures and Services Cloud Computing International Development Open Science His research focuses on high-performance simulation infrastructures, AI-based optimization, and open science applications in healthcare and industry. He has led international projects improving digital infrastructure across Africa and contributed to standards in simulation interoperability. Scientific Contributions & Leadership: Co-founder and former Editor-in-Chief of the Journal of Simulation Chair of ACM SIGSIM (2005–2008) General Chair, IEEE/ACM 2025 Winter Simulation Conference Executive Chair, Simulation Exploration Experience (SEE) Member, Computer Simulation Archive steering committee PhD Supervision & Teaching: Professor Taylor has supervised over 20 doctoral students and examined more than 25 PhDs globally. He leads modules in distributed computing and has developed several postgraduate degrees. He is actively involved in undergraduate and postgraduate teaching, including NATCOR courses, and supports student internships. Research Grants & Projects: Framework Development of Simulation-Augmented Many Objective Optimization for Sustainable Supply Chains (MITRE Corporation, 2023) HIDALGO: HPC and Big Data Technologies for Global Systems (EU, 2018–2021) COLA: Cloud Orchestration at the Level of Application (EU, 2017–2019) CloudSME: Cloud-based simulation platform for manufacturing (EU, 2013–2015) Sci-GalA: Science Gateways and e-Infrastructures in Africa (EU, 2015–2017)

Idriss DagalView profile

Assistant Professor

• Renewable Energy Systems
• Photovoltaic Systems
• Power Electronics
• +6 more

Idriss Dagal serves as an Assistant Professor at Beykent University's Department of Electrical and Electronics Engineering. With a PhD and Master's in Electrical Engineering from Yildiz Technical University (2015 and 2023 respectively), and additional academic credentials from Ethiopian Airlines Aviation University and Mongo Polytechnics University, his career bridges academic research with industry experience as a Sales Engineer. PhD: Yildiz Technical University (2015) Master's: Yildiz Technical University (2023), Ethiopian Airlines Aviation University (2008) Bachelor's: Mongo Polytechnics University (2006) His research focuses on renewable energy systems , particularly photovoltaic power optimization using metaheuristic algorithms (Hybrid PSO-Salp Swarm, Gray Wolf Optimization). He also explores control systems for solar energy and aircraft dynamics, including PID and fuzzy logic controllers. Additional work spans machine learning applications in energy and medical diagnostics, along with power electronics for battery charging. Recent publications highlight 15 2025 articles on topics like hybrid energy systems , AI-driven MPPT , and aircraft control frameworks . His work appears in journals such as Scientific Reports , IEEE Access , and International Journal of Aeronautical and Space Sciences . Teaching experience includes courses in Electrical Machines and Information Technologies . Non-university roles at Elektra Electronic Company and Aktif Group Company involved sales engineering, complementing his academic profile with industry insights.

Kalyanmoy DebView profile

Professor

• Multi-Objective Optimization
• Evolutionary Algorithms
• Genetic Algorithms
• +5 more

Kalyanmoy Deb is a Professor at the Department of Electrical and Computer Engineering, Michigan State University. Previously affiliated with Indian Institute of Technology Kanpur and Indian Institute of Technology Madras, he is a leading researcher in evolutionary algorithms and multi-objective optimization. Research Focus : Multi-Objective Optimization, Genetic Algorithms, Constraint Handling, Machine Learning-assisted Optimization Key Contributions : Development of NSGA (Non-dominated Sorting Genetic Algorithm) series, hybridization of machine learning with evolutionary algorithms, fidelity evaluation techniques Scientific Accomplishments : With over 347 publications and 19,618 citations, his work on NSGA algorithms has become foundational in evolutionary computation. His recent research explores mixed-fidelity evaluations, innovized knowledge extraction, and neural architecture search.

Omer KhanView profile

Professor

• Computer Architecture
• Parallel Processing
• Graph Intelligence
• +4 more

Dr. Omer Khan is a Professor of Electrical and Computer Engineering at the University of Connecticut (UConn), leading the Computer Architecture Group (CAG) and serving as Associate Director of the Connecticut Advanced Computing Center (CACC). His research focuses on computer architectures for high-performance applications, particularly graph intelligence, parallel processing, and secure processors. He holds a PhD from the University of Massachusetts Amherst and a BSc from Michigan State University. Education: PhD in Electrical and Computer Engineering, University of Massachusetts Amherst, 2009 BSc in Electrical and Computer Engineering, Michigan State University, 2000 Research Interests: Dr. Khan’s work addresses computational challenges in artificial intelligence, particularly graph-based data and multi-objective optimization. He develops architectures that exploit sparsity and parallelism to accelerate graph neural networks, secure processors against side-channel attacks, and ensure resilience in large-scale multicores. His research includes hardware-software co-design for thermal management, confidential computing, and algorithm-architecture co-optimization. Publications: His recent work spans parallel algorithms for graph intelligence (e.g., OPMOS, PruneGNN), secure multicore architectures (IRONHIDE, SSE), and GPU acceleration for GNN training. These contributions highlight advancements in efficiency, security, and scalability of modern computing systems. Awards & Grants: Principal Investigator of an NSF REU Site on Trustable Embedded Systems Security (2021–2025). His work has been supported by grants from NSF and industry partnerships. Advising & Mentoring: Guided over 20 graduate and undergraduate researchers, many of whom have secured roles at top tech firms (e.g., Qualcomm, Meta) or pursued advanced degrees at prestigious institutions. Labs & Teams: Leads the CAG at UConn, collaborating with industry and academic partners to advance next-generation computer architectures.

David GraceView profile

Professor

• Intelligent Networks
• Cognitive Radio
• Wireless Architecture Design
• +6 more

Professor David Grace is a Research Professor at the University of York, leading the Communication Technologies Research Group and co-directing the York-Zhejiang Lab for Cognitive Radio and Green Communications. He holds additional roles as Head of the Communication Technologies Discovery Theme, Director of the Centre for High Altitude Platform Applications, and pillar lead for Advanced Communications at the Institute for Safe Autonomy. His expertise spans intelligent networks, cognitive radio, and non-terrestrial systems like High Altitude Platforms (HAPs). Grace has authored over 280 publications and two books, and led major projects including the FP6 CAPANINA initiative and EPSRC HiQ quantum communications research. Education: MEng (First Class) and DPhil from the University of York, with BT-sponsored placements. His research focuses on 6G architectures, AI-driven radio resource management, and green communications. Key projects include developing quantum key distribution via HAPs (HiQ project) and 5G trials in rural areas (MANY project). Research Interests: Application of AI to wireless networks, 6G system design, dynamic spectrum access, aerial platform-based communications, and cognitive networks. His work emphasizes energy-efficient solutions and regulatory policy for emerging technologies. Awards include WUN CogCom Best Paper Awards (2012-2013) and recognition for contributions to green communications. He has supervised over 40 PhD/MSc students and actively participates in international conferences as a speaker, reviewer, and organizer. External roles include Guest Professor at Zhejiang University and leadership in IEEE technical committees. Grants and collaborations include EPSRC funding (HiQ, Radio Networks ITwins), EU H2020 projects (SPOTLIGHT), and industry partnerships with Huawei. His lab activities focus on aerial platform networks, cell-free MIMO systems, and 5G/6G testbed development.

Paweł MyszkowskiView profile

Professor

• Evolutionary Computation
• Metaheuristics
• Multi-objective Optimization
• +7 more

Paweł Myszkowski is a Professor at Wrocław University of Science and Technology, affiliated with the Department of Artificial Intelligence within the Faculty of Computer Science and Management. He is a key member of the Metaheuristics Team and actively contributes to research in evolutionary computation, multi-objective optimization, and scheduling algorithms. His research focuses on evolutionary algorithms , metaheuristics , and multi-objective optimization , particularly applied to the Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP). He has developed hybrid algorithms combining differential evolution, greedy methods, and ant colony optimization. His work includes the creation of benchmark datasets (iMOPSE) and quality measures for optimization algorithms. The recent publications show a strong trend in algorithmic innovation for complex scheduling and design automation , with applications in architectural design and financial modeling. His work bridges theoretical optimization and practical implementation in software systems. Golden Badge of Wrocław University of Science and Technology He supervises diploma theses and collaborates extensively with researchers such as Maciej Laszczyk and Marek Skowroński. He has contributed to the development of tools and benchmarks that support reproducibility and comparative evaluation in computational intelligence research. He is involved in research projects on dark-box optimization, multi-criteria optimization for classifiers, and application-aware network optimization, indicating an ongoing active research agenda.

Michał PanekView profile

Researcher

• Machine Learning
• Optimization
• Multi-criteria Optimization
• +7 more

Michał Panek is a researcher at the Department of Computer Systems and Networks, Faculty of Computing, Wroclaw University of Science and Technology. He is a member of the Machine Learning Team (ZUM) and actively contributes to research in optimization and machine learning applications in networking. His research interests include: Machine Learning in cellular networks Multi-criteria and many-objective optimization Evolutionary algorithms with gene-linkage techniques Application-aware multi-layer network optimization Classifier training using optimization methods The research projects he is involved in focus on developing advanced general-purpose optimizers (Dark-Box Optimization), evolutionary methods for high-dimensional multi-criteria problems, and performance analysis for wireless network automation. These projects reflect a strong interdisciplinary trend combining computer science, optimization theory, and telecommunications engineering. Michał Panek serves as a supervisor for diploma theses and is engaged in teaching activities. He is currently completing his doctoral studies, with a thesis titled "Machine Learning-based performance analysis to enhance the wireless network automation," supervised by Prof. Michał Woźniak and Prof. Ireneusz Jabłoński. The defense is scheduled for March 18, 2025. He is involved in key research teams including: Machine Learning Team Teaching Team Computer Networks Team Advanced Data Analysis Methods Team Metaheuristics Team

Ketan P. DetrojaView profile

Associate Professor

• Control Theory
• State Estimation
• Fault Diagnosis
• +9 more

Ketan P. Detroja is an Associate Professor in the Department of Electrical Engineering at Indian Institute of Technology Hyderabad . With a Ph.D. from IIT Bombay (2006) and a specialization in Multivariable Control, Distributed State Estimation, and AI/ML for Control Applications , his research focuses on ensuring fault-tolerant and economical operation of microgrids , developing distributed Kalman filtering techniques , and applying machine learning to process monitoring and control . Education: B.E., D. D. Institute of Technology, Gujarat, India Ph.D., IIT Bombay, 2006 Research Interests span Advanced Process Control, Fault Detection and Diagnosis, Cooperative Control, Microgrid Optimization and Control , and Hardware-Software Design for Control Systems . His work addresses challenges in data-driven process monitoring , distributed state estimation , and machine learning-based controller design , particularly for large-scale systems. Scientific Contributions include innovative approaches to inverse-free Kalman filters , reinforcement learning-based PI controllers , and data reduction algorithms for fault diagnosis. His publications reflect a focus on control theory, optimization, and machine learning integration , with applications in power systems, wind energy, and fault diagnostics . Scientific Awards: Best Paper Award at ADCONIP - 2011 Best Paper Award at DYCOPS-2013 Advising has been a significant part of his career, with supervision of Ph.D. students working on topics like fault detection in induction motors , microgrid scheduling , and adaptive control using fuzzy logic . His M.Tech students have explored areas such as visual object tracking, battery SOC estimation, and wind farm control , many of whom are now in prominent roles across academia and industry.

• Artificial Intelligence
• Machine Learning
• Data-Driven Science
• +7 more

Janardhan Rao (Jana) Doppa is an Associate Professor in the School of Electrical Engineering and Computer Science (EECS) at Washington State University (WSU). He holds the Huie-Rogers Endowed Chair in Computer Science and the Berry Distinguished Professorship in Engineering. His research focuses on artificial intelligence, machine learning, and data-driven science, with applications in structured prediction, Bayesian optimization, and sustainable computing. He chairs the EECS Graduate Studies program and has received awards such as the NSF CAREER Award (2019) and the Voiland College Anjan Bose Outstanding Researcher Award (2024). Education: Ph.D., Computer Science, Oregon State University (2014) M.Tech., Computer Science, Indian Institute of Technology Kanpur (2006) Research Interests: Dr. Doppa's work spans AI-driven adaptive experiment design, sequential decision-making under uncertainty, robust machine learning, and applications in agriculture, health informatics, and electronic design automation. He leads the NSF-USDA AI Institute for Agricultural Decision Support and explores AI methods for nanoporous materials design and sustainable computing. Awards: NSF CAREER Award (2019) Voiland College Anjan Bose Outstanding Researcher Award (2024) Best Paper Awards at multiple conferences (e.g., ACM/IEEE Embedded Systems Week 2023) Outstanding Junior Faculty in Research (2020) and Reid Miller Teaching Excellence (2018) Advising & Grants: He advises a dynamic group of PhD and MS students, many of whom have won awards. His grants include NSF-CAREER and USDA AI Institute funding. He teaches courses like Machine Learning and Structured Prediction at WSU. Labs/Teams: His research group collaborates with industry and academia, advancing AI for engineering and scientific domains through interdisciplinary projects.

• Programming Languages
• Compiler Construction
• Parallel Computing
• +7 more

Wayne Kelly is an Associate Professor in the School of Computer Science at Queensland University of Technology (QUT), Faculty of Science. He has over 25 years of academic experience and serves as the Academic Lead for Teaching and Learning and Course Coordinator for the Bachelor of Information Technology degree. PhD in Computer Science, University of Maryland, College Park, 1996 BSc (Hons) in Computer Science, University of Queensland, 1989 His research expertise lies in Programming Languages, Compiler Construction, and Parallel Computing, with significant contributions to High Performance Computing, Big Data, and Bioinformatics. His work has led to collaborations with Microsoft Research and over $2 million in external funding. His recent publications reflect a strong trend in parallel and distributed systems, embedded computing, bioinformatics data analysis, and remote sensing. Key themes include optimization of computational systems, memory management, and scalable data processing. Wayne Kelly has made impactful contributions to both teaching and research, guiding numerous postgraduate students and leading curriculum development in information technology. Optimizing I/O cost and managing memory for bioinformatics A communication model for streaming applications on MPSoC Ruby.NET: a compiler for the Common Language Infrastructure He is actively engaged in real-world technology development, including a project with a vision-impaired student to improve public transportation accessibility, currently trialed by transport authorities in Australia and the US.