Find Academic Supervisors

• Machine Learning
• Reinforcement Learning
• Decision Making Under Uncertainty
• +6 more

Michail G. Lagoudakis is a Professor at the Department of Electronic and Computer Engineering, Technical University of Crete. His academic journey includes a Ph.D. in Computer Science from Duke University (2003), an M.Sc. from the University of Louisiana, Lafayette (1998), and a B.Sc. from the University of Patras (1995). He has held prestigious positions such as Postdoctoral Fellow at Georgia Institute of Technology's School of Industrial and Systems Engineering. Research Interests : Spanning machine learning (especially reinforcement learning), decision-making under uncertainty, robotics, algorithm selection, computational biology, and human-computer interaction. Publications : Over 15 recent works, including key contributions to robotics (auction-based multi-robot routing), medical diagnosis (urgent endoscopy prediction), and foundational machine learning (Least-Squares Policy Iteration, RCPI algorithm). Scientific Recognition : Recipient of Duke University's Outstanding Dissertation Award (2002-2003) and two Outstanding Teaching Assistant Awards. Professional Affiliations : Member of AAAI, IEEE, and ACM. Collaborations include industrial applications in disassembly planning, dynamic packet routing, and medical imaging. He advocates for computational biology and interdisciplinary research, aiming to integrate tools from mathematics and control theory into machine learning.

• Logic Programming
• Knowledge Representation
• Artificial Intelligence
• +6 more

Bart Bogaerts is an Associate Professor in the Department of Computer Science at KU Leuven's Faculty of Engineering Science. He is affiliated with the Declarative Languages and Artificial Intelligence (DTAI) research unit and is a member of Leuven.AI - KU Leuven Institute for Artificial Intelligence. Bogaerts serves on the Council of the Faculty of Engineering Science as senior academic staff and participates in the Programme Committee for Artificial Intelligence curriculum development. His research focuses on foundational aspects of logic programming and knowledge representation, with particular expertise in approximation fixpoint theory, higher-order logic programming, and non-monotonic reasoning. Bogaerts investigates the theoretical underpinnings of stable model semantics, justification frameworks, and executable query languages. His work bridges theoretical computer science with practical applications in artificial intelligence and knowledge-based systems. Bogaerts' publication record demonstrates consistent contributions to top venues in logic programming and artificial intelligence. His recent work shows increasing focus on category-theoretic approaches to approximation theory, distributed web traversal specifications, and certified model expansion techniques. The publications reveal a strong emphasis on formal methods with applications spanning from theoretical mathematics to practical AI systems. As a promotor for multiple significant research projects, Bogaerts leads investigations into certified answer set programming (CertifASP), first-order model expansion (CertiFOX), proof generation for combinatorial optimization, distributed configuration problems, and knowledge integration paradigms. These projects, funded through 2028-2029, demonstrate his leadership in advancing the theoretical foundations of AI and logic programming. Bogaerts is actively involved in teaching courses on knowledge representation and reasoning, contributing to the development of next-generation AI researchers. His work within the DTAI research unit positions him at the forefront of declarative AI approaches in Belgium's leading research university.

Valentino SantucciView profile

Associate Professor

• Artificial Intelligence
• Evolutionary Computation
• Machine Learning
• +7 more

Valentino Santucci is an Associate Professor of Computer Engineering at the University for Foreigners of Perugia, Italy, affiliated with the Department of International Human and Social Sciences (SUSI). Since 2021, he has served as the Rector's Delegate for Technological Innovation and Information Flows, highlighting his leadership in digital transformation within the institution. His research spans key areas in Artificial Intelligence, particularly Evolutionary Computation, Natural Language Processing, Machine Learning applications in e-learning and sustainability, and digital technologies in education. He employs algebraic techniques to analyze combinatorial search spaces and evolutionary algorithm dynamics, contributing to both theoretical and applied advancements. The most recent publications reflect a strong trend in combinatorial optimization using algebraic frameworks, hybrid evolutionary-swarm algorithms, and applications in text complexity classification and educational technology. His work bridges computer science with humanities and sustainability, reflecting a multidisciplinary approach. PhD in Computer Science and Mathematics, University of Perugia (2012) He teaches courses in Artificial Intelligence, Computer Science for Humanities, Cybersecurity, and Sustainability across various degree programs. His editorial roles include contributing to WoS/Scopus-indexed journals, and he has taught at the University of Perugia and Hong Kong Baptist University. Dr. Santucci actively mentors through teaching and research supervision. While specific student names are not listed, his involvement in academic projects and publications suggests advisory roles. He has no explicitly mentioned grants, but his research output and leadership position indicate active project engagement. He is involved in institutional innovation through his role in technological advancement and has contributed to projects integrating soft skills and learning technologies. His work emphasizes practical applications of AI in education and sustainability, positioning him at the intersection of technical innovation and societal impact.

• Optimization methods
• Metaheuristics
• Heuristics
• +12 more

Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.

• Artificial Intelligence
• Game AI
• Procedural Content Generation
• +4 more

Julian Togelius is an Associate Professor at the Department of Computer Science and Engineering, Tandon School of Engineering, New York University. He co-directs the NYU Game Innovation Lab and serves as Editor-in-Chief of IEEE Transactions on Games. His research focuses on AI techniques for game design and games as testbeds for AI development, including procedural content generation, evolutionary algorithms, and player modeling. He has organized major competitions like the General Video Game AI Competition and the Mario AI Championship. Research Interests: Julian explores AI-driven game design, procedural content generation (PCG), evolutionary computation, and the intersection of games with broader AI challenges. His work spans automatic level design, opponent AI, and using games to benchmark general intelligence. He emphasizes data-driven models of player experience and integrating machine learning into game development. Organizational Activities: He has held roles including General Chair of IEEE Computational Intelligence and Games (2017), co-organizer of the General Video Game Playing Competition, and editor roles in key journals. His work also extends to music generation (MetaCompose) and data-driven game design (e.g., 'Data Adventures' from open datasets). Professional Contributions: Julian has advised numerous PhD students focusing on AI and games, published over 160 peer-reviewed works, and authored/co-authored books like Artificial Intelligence and Games (2018) and Procedural Content Generation in Games (2016). His research bridges academic rigor with practical game development, emphasizing computational creativity and interactive systems.

Mile ŠikićView profile

Professor

• Computational Biology
• Genomics
• Deep Learning in Sequencing
• +4 more

Prof. Mile Šikić is a Full Professor at the Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing (University of Zagreb). His research spans computational biology, genomics, and machine learning applications in sequencing technologies. Focus on nanopore sequencing analysis, genome assembly, and protein interaction prediction Developed tools like GraphMap , RiNALMo , and Orthobalancer Active in metagenomics, RNA structure prediction, and CUDA-based algorithm acceleration Scientific contributions include: Advances in de novo genome assembly for error-prone long reads Deep learning models for base modification detection Efficient algorithms for sequence alignment and similarity searches Technical implementations cover: GPU-accelerated sequence alignment libraries (e.g., SW# ) Web platforms for comparative protein analysis Simulation tools for epidemic spread on complex networks

Christof RöhrigView profile

Professor

• Robotics
• Mobile Robotics
• Wireless Sensor Networks
• +3 more

Christof Röhrig is a Professor at the FH Dortmund - University of Applied Sciences and Arts , conducting research at the Institute for the Digitalization of Work and Living Environments (IDiAL). He leads the Intelligent Mobile Systems Lab , focusing on robotics, wireless sensor networks, and kinematic modeling. His research spans mobile robotics , indoor localization , and industrial automation , with a strong emphasis on IoT , 5G networks , and rescue robotics . Key trends in his recent work include LoRaWAN localization , modular robot design , and digital twin architecture . He collaborates extensively on motion control , sensor fusion , and energy-efficient systems , though no specific students, awards, or grants are listed in the provided data. The lab at IDiAL explores robotics in emergency response and industrial IoT applications , with a focus on precision localization and collaborative automation .

• Robot Vision
• Robot Navigation
• Computer Vision
• +7 more

Helder Araujo is a Professor at the Department of Electrical and Computer Engineering, University of Coimbra. His research focuses on Robot Vision , Computer Vision , and Medical Imaging , with applications in Capsule Endoscopy , Autonomous Driving , and Sensor Fusion . Recent publications highlight advancements in 3D Object Detection using transformer networks (e.g., RetSeg3D , DDet3D ), Medical Robotics (e.g., Self-supervised monocular pose estimation ), and Federated Learning (e.g., FAIR-FATE ). Key subfields include Transformer Models , Deep Learning , and Autonomous Navigation . His projects span Medical Imaging (e.g., Multi-Cam Capsule Endoscopy ), Autonomous Robotics (e.g., Learning to Navigate Endoscopic Capsule Robots ), and Edge Computing (e.g., Benchmarking CNN Inference on Low-Power Devices ). Grants include funding from Fundação para a Ciência e a Tecnologia (e.g., PTDC/EEI-ROB/1155/2020) and the European Commission (e.g., AdvanCed Hardware/Software Components for Embedded Vision). He has served as an invited researcher at Université Blaise Pascal (2012–2014). Peer review activities cover journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and Autonomous Robots . His work bridges Medical Imaging , Autonomous Systems , and AI Fairness .

• 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.

Anna FarihaView profile

Assistant Professor

• Data Management
• Human-Centered Systems
• Usability Engineering
• +4 more

Anna Fariha is an Assistant Professor in the Kahlert School of Computing at the University of Utah. She co-leads the Data Management Research Center for Human-centered, Efficient, and Scalable Systems. Her research focuses on enhancing data system usability, explainability, and trustworthiness through algorithmic innovations and practical implementations. She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts, Amherst, under Prof. Alexandra Meliou. Education: Ph.D. in Computer Science, University of Massachusetts Amherst (2021) Master's in Computer Science, University of Massachusetts Amherst (2020) Undergraduate work in Computer Science, unspecified institution Her research interests include data wrangling tools, constraint discovery, conversational AI for data science, and human-centered database systems. Recent work emphasizes automated data summary recommendations, constraint violation detection, and educational tools for data science programming. Grants & Awards: NSF CIRC: ENS/Grand: POWDER-ENS Award (2024) Stena Center Seed Grant for Fintech Data Analysis (2025) Microsoft Research Dissertation Grant (2020) SIGMOD 2022 Comprehensive Reproducibility Award Teaching: Recently taught Advanced Database Systems , Human-Centric Data Management , and Deep Learning courses at the University of Utah.

Siegfried NijssenView profile

Assistant Professor

• Data Mining
• Pattern Mining
• Exploratory Data Analysis
• +5 more

Siegfried Nijssen is an Assistant Professor of Data Mining and Artificial Intelligence at the Catholic University of Louvain (UCLouvain) in Belgium, working within the ICTEAM research institute's Artificial Intelligence and Algorithms group. He has been at UCLouvain since 2016, previously serving as an Assistant Professor at University Leiden (2012-2016) and completing postdoctoral work at KU Leuven (2006-2015). He earned his PhD in Computer Science from University Leiden in 2006. His research focuses on making data analysis simpler through intersections between pattern mining, exploratory data analysis, and programming paradigms in Artificial Intelligence, particularly constraint programming and probabilistic programming. He has developed techniques for analyzing diverse data types including graphs, networks, and multi-relational data. His work bridges theoretical foundations with practical applications in decision tree learning, probabilistic networks, and source code analysis. Nijssen's recent publications demonstrate a strong focus on optimal decision trees, constraint-based pattern mining, and applications in bioinformatics and education. His research shows consistent evolution from foundational graph mining work (including the Gaston algorithm developed in 2004) to current work integrating machine learning with constraint programming for interpretable AI solutions. As an educator, he teaches courses including Mining Patterns in Data, Databases, and Artificial Intelligence and Machine Learning seminars at UCLouvain. He has advised numerous PhD students and postdocs, primarily in collaboration with Pierre Schaus, with former students like Tias Guns now holding professorships.

• Polymer Chemistry
• Energy Storage
• Organic Electronics
• +4 more

Kenichi Oyaizu is a Professor in the Department of Applied Chemistry at the Faculty of Science and Engineering, Waseda University, Tokyo. His research spans polymer chemistry, energy storage, and materials science, with a focus on functional polymers for batteries, hydrogen storage, and high refractive index applications. He maintains active collaborations across academia and industry. Professor Oyaizu's research centers on designing polymers with tailored redox properties for energy storage systems, including organic radical batteries and hydrogen carriers. He pioneers high refractive index materials through molecular engineering of hydrogen-bonded networks and sulfur-rich frameworks. His group integrates machine learning with experimental synthesis, utilizing lossless data platforms for materials discovery and optimization in electrochemistry and optical applications. Analysis of his 15 most recent publications (2020-2025) reveals three dominant research trajectories: (1) High-refractive-index polymers leveraging hydrogen bonding and sulfur incorporation for optical devices, (2) Energy storage systems using redox-active polymers for batteries and hydrogen carriers, and (3) Materials informatics approaches applying generative models and quantum-inspired algorithms to accelerate polymer design. These streams demonstrate consistent innovation in structure-property relationships for functional materials. No scientific awards or major honors are documented in the provided materials. Professor Oyaizu leads an active research group mentoring graduate students and postdoctoral researchers in polymer synthesis and characterization. His work receives funding from Japanese national agencies supporting sustainable energy materials and advanced polymer research, though specific grant details are not disclosed in the source text. Current projects emphasize machine learning-driven development of solid-state electrolytes and hydrogen storage polymers. His laboratory operates within Waseda University's advanced materials infrastructure, utilizing specialized facilities for polymer synthesis, electrochemical testing, and optical characterization. The team collaborates with international researchers on battery technologies and participates in university-industry consortia focused on sustainable materials development, with recent projects highlighted in Waseda University News and EurekAlert!.

Artem KaznatcheevView profile

Assistant Professor

• Evolutionary Biology
• Theoretical Computer Science
• Computational Complexity
• +5 more

Artem Kaznatcheev is an Assistant Professor at Utrecht University in the Department of Mathematics and Department of Information and Computing Sciences within the Science faculty. His research bridges theoretical computer science and evolutionary biology to analyze biological and social systems through an algorithmic lens. Current role since January 2023 Recruiting PhD students and postdocs Previously: James S. McDonnell postdoctoral fellow at University of Pennsylvania His work focuses on: Computational complexity of evolution Evolutionary game theory Algorithmic biology Mathematical modeling of cancer dynamics Cultural evolution of science Selected article trends show: Interdisciplinary integration of computer science and cancer biology Key themes: fitness landscapes, evolutionary games, treatment optimization 2021-2020 publications dominate Mathematical formalisms applied to biological and social phenomena Scientific contributions include: James S. McDonnell Foundation Independent Postdoctoral Fellowship 2019 Genetics paper on computational complexity as evolutionary constraint 2017 Nature Ecology & Evolution study on fibroblast-drug interactions in cancer Collaborative environments: Worked with Theory, Evolution and Games Group Previous affiliations: Oxford University, University of Pennsylvania, Moffitt Cancer Center, McGill University Developed teaching roles at Oriel College (Oxford)

Laurens BliekView profile

Assistant Professor

• Artificial Intelligence
• Optimization
• Sustainable Logistics
• +5 more

Laurens Bliek is an Assistant Professor at the Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology (TU/e). He specializes in combining artificial intelligence (AI) with optimization techniques for computationally intensive problems, focusing on sustainable applications such as public transport, electric vehicles, and CO 2 reduction. Education: MSc in Applied Mathematics (2014), PhD in Systems & Control (2019) from Delft University of Technology. Prior Role: Postdoctoral researcher at the Algorithmics group, Delft University of Technology. Research Interests: His work addresses AI-driven optimization of expensive cost functions, particularly in logistics, communications, and healthcare. He develops methods to handle computationally intensive simulators and digital twins, emphasizing real-time decision-making and sustainability. Recent Publications: His research spans predictive maintenance using Fourier graph neural networks, real-time container yard allocation, and 5G network optimization. Articles appear in journals like IEEE Transactions on Neural Networks and Learning Systems and Computer Networks . Collaborations: Laurens collaborates with industry partners (LioniX, Dutch Railways) and organizations (European Supply Chain Forum, Logistics Community Brabant). He co-leads the 12-PhD program AI Planner of the Future and participates in AI sustainability working groups. Supervision: Co-promotor of PhD students Ya Song and Abdo Abouelrous, focusing on AI applications in routing and maintenance logistics.

• Software Engineering
• Smart Cities
• Internet of Things
• +4 more

Siobhán Clarke is a Professor at the School of Computer Science and Statistics, Trinity College Dublin, specializing in software systems for smart urban environments . Her work addresses dynamic software adaptation in large-scale, mobile IoT ecosystems , with a focus on QoS optimization and collaborative agent models . Director, Enable : National SFI IoT Research Programme Director, Future Cities Centre for Smart & Sustainable Cities Co-Lead, ADVANCE : SFI Centre for Advanced Networks Co-PI, CONNECT (Future Networks) and Lero (Software Research) Her research spans smart city infrastructure , edge computing , and multi-agent coordination , informed by 15+ years of publications on service-oriented architectures , QoS prediction , and self-adaptive systems . Key project contributions include DIVERSIFY (2016) and TRANSFoRm (2015). Scientific awards include election to the Royal Irish Academy (2023) and a Best Student Paper at IEEE ICWS 2011. She has supervised 20+ PhD/MSc students, including Fan Li (2020: SLA Negotiation Systems), Gary White (2020: IoT QoS Forecasting), and Andrei Palade (2019: Stigmergic Optimization).