Find Academic Supervisors

• Unconventional Computing
• Bio-inspired Computing
• Modeling and Simulation
• +4 more

Dr. Antisthenis Tsompanas is a Senior Lecturer in Computer Science at the University of the West of England (UWE), part of the Faculty of Engineering and the Environment (FET). His research focuses on unconventional and bio-inspired computing, with expertise in modeling biological processes, cellular automata, and memristive systems. He holds degrees in Electrical and Computer Engineering from Democritus University of Thrace, Greece. Research interests span unconventional computing, bio-inspired algorithms, electronic systems design, and applications of cellular automata in computing and engineering. His work includes projects on fungal-inspired computing, neuroevolution-based medical device design, and FPGA implementations of biological models. Publications highlight trends in mycelium-based systems, memristive oscillators, and neuroevolution techniques applied to soft robotics and medical devices. Collaborative projects include EU-funded initiatives and interdisciplinary research in biomimetic engineering. He is a member of the Technical Chamber of Greece since 2009. Current contributions include advancing biohybrid actuators, developing models for engineered living materials, and exploring computational universality in biological systems.

Jason YeatmanView profile

Associate Professor

• Brain and Learning Sciences
• Child Development
• Neuroimaging
• +5 more

Dr. Jason Yeatman is an Associate Professor at Stanford University, holding appointments in the Graduate School of Education, Department of Psychology, and the Division of Developmental and Behavioral Pediatrics at the School of Medicine. His research focuses on understanding the neural mechanisms of reading development, dyslexia, and designing literacy interventions. He directs the Brain Development and Education Lab, employing neuroimaging techniques to study how reading instruction shapes brain circuits. He earned his PhD in Psychology at Stanford, studying the neurobiology of literacy. After a faculty position at the University of Washington, he returned to Stanford. His work bridges neuroscience, education, and technology, with a focus on scalable assessments like the Rapid Online Assessment of Reading (ROAR) tools. Research interests include brain development, learning sciences, and educational equity. He teaches courses such as 'The Science of Reading' and mentors numerous graduate and postdoctoral students. His lab develops open-source tools for neuroimaging analysis and collaborates on large-scale projects like the Human Connectome Project. Key contributions include identifying neural correlates of reading skill, studying white matter plasticity in children, and advancing adaptive testing methods for literacy screening. His work emphasizes translating neuroscience insights into practical educational strategies.

• Biomedical Engineering
• Biocybernetics
• Artificial Intelligence
• +3 more

Ryszard Tadeusiewicz is a Professor at the Department of Biocybernetics and Biomedical Engineering , Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering , AGH University of Science and Technology in Kraków, Poland. His research spans biomedical engineering, artificial intelligence, and automation, with a focus on medical imaging, neural networks, and healthcare sustainability. Primary research areas: AI-driven diagnostics, biocybernetics, and automated systems Contributions to conferences like ICAISC and journals in medical informatics Leadership in interdisciplinary projects at the intersection of engineering and medicine His recent publications highlight the integration of deep learning with biomedical applications, including tumor segmentation, pneumonia detection, mental health classification, and forest-planting robotics. While no explicit awards are listed, his extensive publication record underscores his role as a thought leader in AI and biomedical innovation.

Kai CasparView profile

Lecturer

• Sensory Biology
• Behavior of Subterranean Mammals
• Gibbon Cognition
• +5 more

Dr. Kai Caspar is a Lecturer in the Department of General Zoology at Heinrich Heine University Düsseldorf since February 2023. His research focuses on sensory biology, behavior of subterranean mammals, and cognition of small apes. He works within the Faculty of Biology and collaborates with Sabine Begall's Lab. 2019-2022: Doctoral studies in Zoology, Comparative Sensory Biology at University of Duisburg-Essen 2016-2018: Master's degree in Organismic Biology, Evolutionary Biology and Paleobiology at University of Bonn 2013-2016: Bachelor's degree in Biology at University of Duisburg-Essen Dr. Caspar's research spans multiple areas of vertebrate biology, with particular emphasis on subterranean mammals like mole-rats (Bathyergidae) and small apes (Hylobatidae). His work on sensory biology explores magnetoreception, auditory systems, and visual adaptations in these specialized animals. In primatology, he investigates gibbon cognition, communication, and the evolution of human eye morphology. His interdisciplinary approach combines field studies, laboratory experiments, and comparative analyses to understand evolutionary adaptations. His recent publications reveal a strong focus on sensory biology across diverse taxa, with particular attention to subterranean mammals and primates. Key themes include evolutionary adaptations to specialized environments, comparative cognition across species, and the biological basis of communication systems. His work often bridges traditional disciplinary boundaries, connecting mammalogy, sensory ecology, and evolutionary anthropology to address fundamental questions about adaptation and behavior. Dr. Caspar has been recognized with the Fritz-Frank-Award of the German Society for Mammalian Biology for his contributions to mammalogy research. Fritz-Frank-Award of the German Society for Mammalian Biology As a lecturer at Heinrich Heine University Düsseldorf, Dr. Caspar contributes to teaching in the Department of General Zoology. His research is conducted primarily through collaborations with Sabine Begall's Lab, focusing on sensory biology and behavior of specialized mammals. His extensive publication record demonstrates active research in comparative sensory biology and primatology. Dr. Caspar works within Sabine Begall's Lab at Heinrich Heine University Düsseldorf, collaborating with researchers across multiple institutions on projects related to sensory biology, mammalogy, and primatology. His work involves both field studies of wild populations and controlled laboratory experiments.

• Genetic Programming
• Evolutionary Algorithms
• Combinatorial Optimisation
• +6 more

Dr. Edgar Galván is an Associate Professor in the Department of Computer Science at Maynooth University's Faculty of Science & Engineering. He is a leading expert in Genetic Programming (GP) and Evolutionary Algorithms, with a focus on semantic-based approaches, neutrality, and multi-objective optimization. His work spans applications in combinatorial optimization, gaming (e.g., Carcassonne), and software engineering, including neuroevolution for deep learning architectures. Current Affiliation: Maynooth University Previous Roles: Senior Researcher at University College Dublin, Trinity College Dublin, and INRIA Paris-Saclay Research interests include: Semantic-based Genetic Programming Multi-objective Evolutionary Algorithms Monte Carlo Tree Search Circular Economy Applications Privacy-Preserving Optimization Neuroevolution in Autonomous Systems His recent publications analyze semantic diversity in GP, neural architecture search, and privacy-aware swarm optimization. Key awards include being ranked among the top 1% of GP researchers by University College London (2020), a Marie Curie Fellowship (2014), and a Best Paper Award at ECTA 2015. Current Projects: REBUILD (Circular Economy Buildings, 2024-2027), VISION (Circular Business Models, 2023-2026) Previous Grants: Stochastic Bio-inspired Algorithms (2014-2017, €267k), circAI (2022-2023, €142k) Dr. Galván serves on program committees for IEEE, ACM, and Springer conferences, and as Scientific Adviser for institutions in Ireland, France, and Mexico. His work bridges theoretical GP analysis with real-world applications in energy optimization and AI.

• Parallel and distributed computing
• Neural networks
• Video compression
• +1 more

Stepan Yuriyovych Skrupsky is an Associate Professor at the Department of Computer Systems and Networks, Faculty of Computer Science and Technology, Zaporizhzhya National Technical University (ZNTU). He graduated with honors in 2010 as a Master in Computer Systems and Networks and defended his PhD thesis on "Models and methods of video information compression in distributed computer systems" in 2013 at the Institute of Modeling Problems in Energy. His research focuses on parallel and distributed computing , neural networks , video compression , and artificial intelligence systems . He teaches disciplines such as Computer Architecture, Mobile Application Architecture, and Cloud Technologies. Education : Master in Computer Systems and Networks, ZNTU (2010) PhD Thesis : "Models and methods of video information compression in distributed computer systems" (2013) His scientific works explore parallel computing architectures for AI applications, including neuro-fuzzy models , Internet of Things infrastructure , and rule extraction methods . Publications span topics like big data reduction , medical diagnostics , and software-defined networks . He uses computational intelligence and stochastic optimization for distributed system design. Key trends in his research include parallelization of AI algorithms , resource-efficient distributed systems , and cross-domain applications from medicine to IoT. His articles appear in venues such as Radio Electronics, Computer Science, Control and Automatic Control and Computer Sciences .

Yuqi SongView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Data Mining
• +3 more

Yuqi Song is an Assistant Professor in the Department of Computer Science at the University of Southern Maine (USM), where she joined in August 2023 after completing her Ph.D. at the University of South Carolina. Her interdisciplinary research bridges machine learning with materials science, tourism, and recommender systems. Her educational background includes: Ph.D. in Computer Science, University of South Carolina (2023), supervised by Dr. Jianjun Hu M.S. and B.S. in Computer Science, Chongqing University, supervised by Dr. Ming Gao Dr. Song's research focuses on applying state-of-the-art deep learning techniques—including generative adversarial networks, graph neural networks, and transformer models—to solve real-world problems. She develops AI-driven solutions for materials discovery (predicting crystal structures and properties) and tourism applications (employee turnover prediction systems). Her work uniquely combines computational methods with domain-specific challenges, emphasizing practical implementation through user-friendly tools like her materials informatics web platform MaterialsAtlas.org. Analysis of her recent publications (2023-2025) reveals three dominant research thrusts: (1) materials informatics using transformer-based generative models for crystal structure prediction, (2) robust recommender systems security against data hybrid attacks, and (3) computer vision innovations in depth estimation and medical image analysis. Her work consistently leverages attention mechanisms and cross-disciplinary data integration. Dr. Song actively mentors graduate students, currently advising Reihaneh Maarefdoust (Complex Learning and Machine Learning) and Zahra JahediBashiz (NLP, Generative AI). She teaches core courses including Software Engineering (COS 430) and Artificial Intelligence (COS 470), emphasizing practical programming skills. Her lab seeks motivated students for projects in materials discovery and tourism analytics. She leads a research group focused on interdisciplinary AI applications, collaborating with materials scientists and hospitality industry partners to develop deployable solutions. Current initiatives include deep learning models for predicting piezoelectric properties and generative design of 2D materials, alongside tools for tourism workforce analytics.

• Robotics
• Machine Learning
• Reinforcement Learning
• +4 more

Nicolas Perrin-Gilbert is a CNRS Research Fellow at the Institute of Intelligent Systems and Robotics (ISIR), which is part of Sorbonne University in Paris, France. He has been working at ISIR since 2013 as part of the MLIA (Machine Learning and Artificial Intelligence) team, where he conducts research at the intersection of robotics and machine learning. His office is located at ISIR, Campus Pierre et Marie Curie, 4 place Jussieu, BC173, 75005 Paris. Dr. Perrin-Gilbert's research focuses on developing advanced algorithms for robot locomotion, control, and learning. His work bridges theoretical machine learning with practical robotics applications, particularly in reinforcement learning, motion planning, and humanoid robotics. He has made significant contributions to quality-diversity algorithms, state representation learning, and controller synthesis for dynamic multi-agent systems. His approach often combines insights from control theory with modern machine learning techniques to solve complex robotics challenges. His publication record shows consistent research output since 2010, with a notable acceleration in recent years. His work spans both theoretical contributions (such as unifying frameworks for motion planning and diversity search) and practical implementations (including humanoid robot control systems). A key trend in his research is the integration of motion planning techniques with reinforcement learning, creating more efficient and robust learning systems for robotics applications. Dr. Perrin-Gilbert is the primary maintainer of xpag , a modular reinforcement learning library with JAX agents that supports standard reinforcement learning and goal-conditioned reinforcement learning. This open-source project demonstrates his commitment to developing practical tools for the research community. The library has gained significant traction with 27 GitHub stars and 6 forks, and is used by other researchers in the field. His collaborations span multiple institutions and research groups, with frequent co-authorship with researchers from ISIR including Olivier Sigaud, Alexandre Chenu, and Stéphane Doncieux. He has also collaborated with researchers from other institutions on projects involving humanoid robots like COMAN and Cassie, demonstrating the applied nature of his work.