Find Academic Supervisors

• Machine Learning
• Distributed Systems
• Sensor Networks
• +7 more

Miloš Stanković is a Professor at the Faculty of Informatics and Computing, Singidunum University. His research focuses on distributed systems, reinforcement learning, machine learning applications, and sensor networks. He has contributed significantly to areas like multi-agent systems, optimization algorithms, and cybersecurity. Recent work includes distributed sensor network calibration, adaptive consensus algorithms, and AI-driven solutions for healthcare and infrastructure monitoring. Education: Not explicitly listed in provided texts. Likely holds advanced degrees in computer science or electrical engineering based on his publications. Research Interests: His primary areas include distributed multi-agent reinforcement learning, metaheuristic optimization for machine learning models, and real-world applications of AI in healthcare, surveillance, and smart infrastructure. He explores topics like sensor fusion, fault detection in networks, and consensus-based algorithms for decentralized systems. Article Trends: Recent publications emphasize AI-driven solutions (e.g., Parkinson’s disease detection via LSTM tuning, student dropout analysis with explainable AI) and distributed algorithms for multi-agent systems. His work bridges theoretical advancements in reinforcement learning with practical applications in sensor networks and cybersecurity. Grants/Projects: Not explicitly detailed, but involvement in EU-funded projects like Sinteza and SITCON conferences suggests external funding. Labs/Teams: Likely part of Singidunum’s research groups in AI, distributed systems, and cybersecurity. Collaborates with institutions like KTH Royal Institute of Technology (via co-author K.H. Johansson) on sensor networks and control systems.

Lars Vidar MagnussonView profile

Associate Professor

• Machine Learning
• Image Analysis
• Natural Language Processing
• +3 more

Lars Vidar Magnusson is an Associate Professor at the Department of Computer Science and Communication, Østfold University College. His research focuses on machine learning, image analysis, natural language processing, and explainable AI, with applications in healthcare decision support and computer graphics. He leads the Machine Learning research group and is affiliated with The Digital Society research priority area. Education: PhD from the University of Oslo, Master’s from Østfold University College. Research Interests: Includes evolutionary optimization for medical diagnosis (e.g., deep vein thrombosis), recurrent neural networks for industrial prediction, and AI-driven education tools (AI4AfL project). His recent work bridges machine learning with healthcare, emphasizing clinical decision support systems and interpretable AI models. Key Projects: AI4AfL (AI for Assessment for Learning) and Decision Support in Healthcare using AI. Active in conferences like IEEE Congress on Evolutionary Computation and Scandinavian Conference on Artificial Intelligence (SCAI).

Roland OlssonView profile

Associate Professor

• Machine Learning
• Automatic Programming
• Artificial Neural Networks
• +3 more

Roland Olsson is an Associate Professor at the Department of Computer Science and Communication, Oslo and Akershus University College of Applied Sciences (HIOF). He holds a doctorate from the University of Oslo (UiO) and previously served as an Associate Professor at Chalmers University of Technology in Gothenburg, Sweden. His research focuses on machine learning, automatic programming, and algorithm design through large-scale combinatorial search, particularly via his ADATE system. Olsson’s work emphasizes meta-machine learning, including the automatic design of neural network architectures. His research group explores applications in image processing, symbolic regression, and optimization. Key areas include improving algorithms for edge detection, oil well event prediction, and real-time CNC machine control through imitation learning. Publications span domains like evolutionary computation, industrial informatics, and smart grids. His work frequently appears in journals like IEEE Open Journal of the Industrial Electronics Society and conferences such as IEEE International Conference on Big Data and Smart Computing. Though no specific awards are listed, his contributions to automatic programming and machine learning algorithms are widely recognized in the academic community. Olsson’s advising and grant involvement is implied through collaborative projects with industry partners (e.g., CNC machine operators, oil well monitoring). He leads the Machine Learning group at HIOF and teaches courses in machine learning. His research extends to interdisciplinary applications, including IoT architectures for smart grids and healthcare activity recognition.

• Embedded Systems
• Embedded Autonomous Systems
• Wireless Communications
• +5 more

Karim El Sharkawy is a Full Professor in the Department of Electrical and Computer Engineering at Purdue School of Engineering and Technology, Indianapolis. He is also the Director of the Internet of Things (IoT) Collaboratory and has held leadership roles such as Vice President for Research at Egypt Japan University of Science and Technology (E-JUST) from 2009–2012. Ph.D. in Electrical Engineering (Southern Methodist University, 1985) M.S. and B.S. in Electrical Engineering (Alexandria University, 1979 and 1974) His research spans Embedded Systems , IoT , Wireless Communications , and Digital Signal Processing , with recent work focusing on lightweight neural networks, sensor fusion for autonomous vehicles, and real-time AI deployment on embedded platforms. Articles highlight trends in computer vision , edge computing , and low-power IoT applications . He has received prestigious awards including the U.S. Fulbright Scholar Award , Abraham M. Max Distinguished Professor Award , and multiple Best Paper/Presentation Awards at conferences. His grants include millions from Motorola , FreeScale , and NXP , and he has advised over 150 graduate students. Dr. El-Sharkawy established the IoT Laboratory at Purdue and contributed to curriculum innovation in Electric Vehicle Technology and Embedded Systems .

• Bilevel Optimization
• Numerical Methods
• Nonsmooth and Nonconvex Optimization
• +7 more

Alain Zemkoho is a Professor of Mathematical Optimization at the School of Mathematical Sciences, University of Southampton. He is affiliated with the OR Group and CORMSIS. His research focuses on bilevel optimization, numerical methods, and applications in transportation, healthcare, cybersecurity, and machine learning. He holds prestigious fellowships including the Alexander von Humboldt Experienced Fellowship (2024-2026) and Alan Turing Institute Fellowship (2019-2023). Research Interests: Bilevel and hierarchical optimization Numerical methods for continuous optimization Nonsmooth and nonconvex optimization Stability/sensitivity analysis Variational analysis Recent Research Trends: His work spans algorithm development for pessimistic/optimistic bilevel problems, cybersecurity applications using game theory and honeypots, and healthcare optimization via deep learning for S-ICD patient screening. Over 15 recent articles (2022-2025) address topics like trajectory optimization for UAVs, adversarial machine learning, and hyperparameter tuning for SVMs. Scientific Awards: Alexander von Humboldt Experienced Fellow Fellow of the Alan Turing Institute Institute of Mathematics & Its Applications Fellow Higher Education Academy Fellow Advising & Labs: Currently supervising 4 PhD students in Mathematical Sciences. Active in the OR Group and CORMSIS research teams, collaborating on interdisciplinary projects.

Seyedali MirjaliliView profile

Professor

• Optimization
• Swarm Intelligence
• Evolutionary Algorithms
• +6 more

Seyedali Mirjalili is a Professor of Artificial Intelligence and Director of the Centre for Artificial Intelligence Research and Optimization (AIRO) at Torrens University Australia. He holds distinguished professorships at VSB Technical University of Ostrava, Óbuda University, and De La Salle University, and is an adjunct researcher at Griffith University and Yonsei University. His research focuses on optimization algorithms, swarm intelligence, evolutionary computation, and machine learning. Education: PhD in Computer Science (Griffith University, 2016). Research interests include optimization techniques, swarm intelligence algorithms (e.g., Grey Wolf Optimizer, Whale Optimization Algorithm), machine learning applications, and robust optimization frameworks. He has published over 600 papers with an H-index of 110 and is among the most cited researchers in optimization fields globally. Awards include the Pro-Vice Chancellor Research Special Award (2019) and recognition as a top 1% highly cited researcher (since 2019). He serves as an associate editor for journals like Neurocomputing and Applied Soft Computing. Labs/Teams: Leads the AIRO Centre at Torrens University, focusing on AI-driven solutions for complex optimization problems.

• Computational Sustainability
• Cloud Computing
• Edge Computing
• +3 more

Ivona Brandic is a Full Professor of High Performance Computing Systems at TU Wien's Institute of Information Systems Engineering, leading the HPC Research Group. She specializes in Computational Sustainability, Cloud/Edge Computing, and Quantum-Classical Systems. Her roles include Head of the Computational Sustainability Research Unit and membership in TU Wien's Faculty Council. She teaches courses such as AI/ML in Climate Change and Hybrid Quantum-Classical Systems. Her research focuses on sustainable IT, energy-efficient systems, and hybrid quantum-classical workflows. Projects include computational sustainability initiatives funded by the Austrian Science Fund (FWF) and industry partnerships like the Virtual Shepherd project. She has contributed to over 50 publications, emphasizing edge computing, quantum algorithms, and HPC optimization. Notable contributions include developing frameworks like RIGOLETTO for hybrid scientific workflows and FRESCO for edge offloading. Her work bridges theoretical advancements with practical applications in environmental monitoring and energy efficiency.

• Disaster Management
• Risk Analysis
• Environmental Science
• +3 more

Mitchell Scovell is affiliated with James Cook University (JCU), where he completed his PhD in 2020 on investigating psychological factors influencing cyclone mitigation behavior. His research focuses on disaster management, risk perception, and behavioral science, with a particular emphasis on cyclone preparedness in North Queensland. He also explores applications of machine learning in software engineering, such as hyperparameter tuning for clustering algorithms. Scovell has published widely in peer-reviewed journals like Risk Analysis and Natural Hazards Review , and presented at conferences including the IEEE/ACM International Conference on Automated Software Engineering and the Australasian Fire and Emergency Service Authorities Council Conference. His work spans interdisciplinary collaborations, addressing both theoretical and applied aspects of disaster resilience. Key themes include understanding how personal experiences and demographics shape disaster mitigation decisions, and developing evidence-based strategies to enhance community preparedness. Scovell’s contributions bridge behavioral science and environmental hazards, with a focus on cyclone-prone regions. Notable outputs include studies on protective action decision models, cluster analysis of behavioral personas, and semi-automated hyperparameter tuning methodologies. His research aims to improve predictive accuracy of damage severity and optimize communication strategies for disaster risk reduction.

Wuyang ChenView profile

Assistant Professor

• Deep Learning
• Scientific Machine Learning
• Computer Vision
• +7 more

Wuyang Chen is an Assistant Professor at the School of Computing Science, Simon Fraser University. He holds a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin (2023). His research focuses on integrating AI with physics, developing principled deep learning methods, and advancing areas like scientific machine learning (SciML), computer vision, and foundation models. Notable contributions include PDE-Controller for LLM-based scientific reasoning and neural operators for fluid reconstruction. He has received prestigious awards, including the INNS Doctoral Dissertation Award (2024) and the NSERC Discovery Grant. Dr. Chen serves as an Area Chair for NeurIPS 2025 and ICML 2025. His lab, DeLTA, emphasizes AI applications in science and hosts active collaborations. He teaches CMPT 419/983 (Special Topics in AI) and mentors students in research areas like model efficiency and self-supervised learning. Education: Ph.D., Electrical and Computer Engineering, The University of Texas at Austin (2023) Recent Awards: INNS 2024 Doctoral Dissertation Award, iSchools Doctoral Dissertation Award 2024, Google GCP Credit Award Key Activities: Organized NeurIPS 2024 workshop on Foundation Models for Science; presented at AAAI 2025, SIAM CSE25, and Berkeley BLISS seminar

• ICT (Information and Communication Technology)
• Eco-informatics
• Semantic Technologies
• +7 more

Dr. Trina Myers is a researcher at James Cook University Queensland , with a focus on interdisciplinary applications of Information and Communication Technology (ICT) in environmental science, healthcare, and education. Her work spans eco-informatics , semantic technologies , and human-computer interaction , particularly in co-design methodologies for marginalized communities. Key Affiliations: James Cook University Queensland Research Themes: Sensor networks, coral reef modeling, telemedicine, educational technology Dr. Myers' recent publications highlight trends in dynamic optimization for logistics, health data quality analysis, and co-design frameworks for special education. Her work often integrates gamification and collaborative tools to address real-world challenges in sustainability and healthcare. She has contributed to advancing ontology development and environmental monitoring systems , with projects like the Tropical Data Hub and Semantic Reef platform. While no explicit awards or advisees are documented in the provided corpus, her collaborations span disaster management , marine conservation , and inclusive technology design .

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +7 more

Fernando Benites de Azevedo e Souza is a researcher at Zurich University of Applied Sciences (ZHAW) specializing in Natural Language Processing (NLP) and text analytics. He collaborates across disciplines including cultural management, speech recognition, and low-resource language technologies. Key research areas: NLP applications in arts management, speech-to-text systems, and German dialect identification Active in SwissNLP community and VarDial conference series Focus on practical implementations: e-assessment integrity, transliteration resources, and resource-constrained deep learning His work demonstrates technical expertise in: Comparative analysis of ASR systems (CEASR corpus) Transliteration pattern detection (TRANSLIT resource) Dialect identification challenges Combining lexical and sentiment analysis Scientific achievements include: Ranked first in ALTA 2018 patent classification competition Multiple top-three finishes at VarDial evaluations First place in GermEval 2020 low-resource speech recognition task

• Learning to optimize
• Optimization under uncertainty
• Efficient logistics and transport
• +4 more

Prof. Dr. Kevin Tierney is a Full Professor for Decision and Operation Technologies at Bielefeld University's Faculty of Business Administration and Economics. He also serves at the Department of Management Science & Business Analytics and is affiliated with the Bielefeld Center for Data Science (BiCDaS) and Center for Uncertainty Studies (CeUS). Chair of Business Administration, Decision and Operation Technologies Member of BIGSEM Graduate School PhD (2013) - IT University of Copenhagen Sc.M. (2010) & BS (2008) - Brown & RIT Research Interests His work focuses on: Learning to Optimize: Using deep reinforcement learning to automate solution heuristics for complex problems like routing and scheduling. Optimization under Uncertainty: Developing models that incorporate probabilistic elements for decision-making in unpredictable environments. Efficient Maritime Logistics: Specializing in container shipping, terminal operations, and fleet routing with real-world constraints. Recent publications demonstrate expertise in algorithm configuration, constraint programming, and machine learning applications to logistics challenges. Scientific Recognition Distinguished Paper Award - European Conference on Artificial Intelligence (2020) Projects & Grants Principal Investigator in projects: Self-learning methods with Deep Reinforcement Learning (DFG 2026) itsowl-MOVE (Land NRW 2024) AIPlan4EU Meta-planning engine (EU H2020 2023) Academic Leadership Module responsible for: Quantitative Business Administration Data Science Production and Operations Management

• Computer Science
• Engineering
• Deep Learning
• +7 more

Khalid Mahmood is an academic researcher with a strong focus on interdisciplinary applications of Computer Science and Engineering , particularly in domains such as Deep Learning , Optimization Algorithms , and IoT . He has co-authored numerous studies in journals like IEEE Access and IEEE Transactions on Consumer Electronics , addressing challenges in medical imaging , smart farming , cybersecurity , and smart cities . Research trends in his publications include: Integration of quantum computing with AI for enhanced security frameworks in IoT and metaverse applications. Application of optimization algorithms (e.g., Aquila, Jaya, Red Fox) to improve deep learning and graph neural network performance. Development of computer vision solutions for medical diagnosis (e.g., laryngeal cancer) and agricultural classification (e.g., soil types). His work emphasizes practical solutions for smart infrastructure , with applications in industry 5.0 , smart grids , and cyber-physical systems .

Jacob GardnerView profile

Assistant Professor

• Probabilistic Machine Learning
• Generative Modeling
• Bayesian Optimization
• +1 more

Jacob Gardner is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania, where he leads a research group focused on probabilistic machine learning. His work bridges theoretical foundations and practical applications, with emphasis on Bayesian optimization and generative modeling for scientific challenges like drug discovery and materials design. Education & Background He earned his Ph.D. in Computer Science from Cornell University under Kilian Weinberger, followed by postdoctoral work in Cornell's Operations Research department. Prior to joining Penn, he was a Research Scientist at Uber AI Labs. Research Focus His lab develops methods for: Bayesian optimization for high-dimensional scientific design problems Scalable Gaussian process inference Black-box variational inference with convergence guarantees Adversarial robustness in machine learning systems Publication Trends Recent work (2022-2023) demonstrates strong focus on efficient Bayesian methods: 80% of publications center on optimization techniques (local/global Bayesian optimization) and variational inference, with applications to latent space modeling and hyperparameter tuning. Papers consistently appear at NeurIPS, ICML, and AISTATS. Research Team Advises five PhD students: Natalie Maus (Bayesian optimization) Kaiwen Wu (optimization theory) Kyurae Kim (variational inference) Haydn Jones (structured data) Yimeng Zeng (generative modeling) Software Contributions Co-founded GPyTorch, a PyTorch-based framework for GPU-accelerated Gaussian processes that replaces traditional Cholesky decomposition with linear conjugate gradient methods.

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

Mohamed M. Abdallah is a researcher affiliated with Hamad Bin Khalifa University in Doha, Qatar, specifically within the College of Science and Engineering . His work focuses on advanced applications of Machine Learning , Artificial Intelligence , and Cybersecurity in domains such as Smart Grids , Internet of Things , and Wireless Communication . His recent research explores Federated Learning under adversarial conditions, optimization of Multi-Agent Systems for task offloading, and Privacy-Preserving Techniques in networked environments. Key contributions include frameworks for Deep Reinforcement Learning (DRL) in Edge Computing and 6G Networks , addressing challenges in Energy Efficiency , Latency , and Data Distribution Shifts . His publications highlight collaborations with institutions like Texas A&M at Qatar and Hamad Bin Khalifa University , emphasizing solutions for Heterogeneous Networks , Blockchain Applications , and Secure Communication in IoT and critical infrastructure.