جست‌وجوی استاد و پژوهشگر

Tim Blackwellمشاهده پروفایل

مدرس ارشد

• Swarm Intelligence
• Computer Music
• Cellular Automata
• +۳ مورد دیگر

Tim Blackwell is a Senior Lecturer in the Computing Department at Goldsmiths, University of London. He holds a BSc in Physics (1980), a DPhil in Theoretical Physics (1984), and an MSc in Computer Science (2001). His research focuses on swarm intelligence, computational music, and optimization algorithms, with notable contributions to particle swarm optimization and tomographic reconstruction. Education: BSc Physics, 1980 DPhil Theoretical Physics, 1984 MSc Computer Science, 2001 His work bridges computational methods with artistic and scientific applications, including projects like 'A Sound You Can Touch' and 'Swarm Techtiles.' Recent research explores optimization benchmarks, environmental applications (e.g., illegal fishing detection), and evolutionary algorithms. Key publications span journals like IEEE Transactions on Evolutionary Computation and conferences such as GECCO. His work emphasizes interdisciplinary approaches, integrating computational techniques with fields like music, image processing, and environmental science.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Jiayan Qiu is a Lecturer (Assistant Professor) at the University of Leicester's College of Computing and Mathematical Science. Previously, he was a postdoctoral research fellow collaborating with Prof. Zhou Wang at the University of Waterloo's Department of Electrical & Computer Engineering. He holds a Ph.D. from the University of Sydney (USYD), advised by Prof. Dacheng Tao, and completed his MPhil and Honorable B.S. at the Australian National University (ANU). His research focuses on computer vision, machine learning, and artificial intelligence, with notable contributions to visual relationship modeling, image outpainting, depth estimation, and generative models. His work has been published in top-tier venues like IEEE TPAMI, CVPR, ECCV, and ACM KDD. Professional service activities include serving as a reviewer for prestigious journals (e.g., IEEE T-PAMI, T-IP) and conferences (CVPR, ICCV, NeurIPS), as well as a member of program committees for leading AI conferences. He also contributes to academic leadership as a Guest Editor for Frontiers in Signal Processing and MDPI-Electronics .

• Optimization
• Operational Research
• Heuristics
• +۳ مورد دیگر

Andrew J. Parkes is an Assistant Professor (Lecturer) in Operational Research and Computer Science at the University of Nottingham , affiliated with the School of Computer Science and the COL research group (formerly ASAP). He has been a member of the LANCS Initiative's executive committee and co-organized multiple streams at international conferences like OR61, EURO 2019, and OR60. His research spans optimization techniques, heuristic methods, and applications in timetabling and financial modeling.

• Collective behaviour
• Theoretical and Computational Biology
• Decision theory
• +۷ مورد دیگر

Professor James A.R. Marshall is a Professor of Theoretical and Computational Biology at the University of Sheffield's School of Computer Science, where he also serves as Director of the Centre for Machine Intelligence. He leads the Behavioural and Evolutionary Theory Lab (BET Lab), an interdisciplinary research group focused on understanding behavior through mathematical and computational approaches. His academic journey includes previous positions at the University of Bristol before joining Sheffield in 2010. Marshall's research spans collective social insect behavior, evolutionary theory, decision theory, robotics, and theoretical neuroscience. He applies mathematical and computational approaches to understand how and why behaviors evolve, with particular interest in social insect colonies, swarm intelligence, and biomimetic applications for robotics. His work often bridges biology and computer science, developing algorithms inspired by natural systems. He has made significant contributions to understanding confidence-weighted decision-making, quorum sensing in swarms, and magnitude-sensitive reaction times in multi-alternative choices. His recent publications reveal a strong trend toward applying insights from biological systems to robotics and AI, particularly through projects like Brains on Board and Opteran Technologies. His work increasingly focuses on neuromorphic control systems that mimic insect brains for autonomous navigation, demonstrating how constraints in biological systems can lead to robust engineering solutions. The interdisciplinary nature of his research is evident in publications spanning computational neuroscience, robotics, animal behavior, and theoretical biology. EPSRC Peer Review College Member (2010-present) Member of the UKRI Future Leaders Fellowship Panel College (2019-present) Editorial Board, Swarm Intelligence (2017-present) Associate Editor, PLoS Computational Biology (2020-present) Marshall has successfully mentored numerous researchers including HaDi MaBouDi, Andreagiovanni Reina, and Alexander Cope, while securing substantial research funding including EPSRC grants totaling over £4 million. His lab has produced the spinout company Opteran Technologies, which has secured over £12 million in funding to develop natural intelligence technology for autonomous systems. The BET Lab maintains strong connections with Sheffield Robotics and focuses on testing theoretical models in physical robotic systems while developing computational models of biological processes.

James Stovoldمشاهده پروفایل

استادیار

• Emergent Behavior
• Cognitive Robotics
• Unconventional Computing
• +۴ مورد دیگر

Dr. James Stovold is an Assistant Professor at the Data Science Institute, Lancaster University. His research focuses on emergent behavior, cognitive robotics, and unconventional computing methods, particularly leveraging neural cellular automata and swarm robotics for innovative applications in AI and computational systems. His recent work explores topics such as human-AI symbiosis, reaction-diffusion chemistry for neural networks, and mixed-initiative design tools. Publications span disciplines including artificial intelligence, computational biology, and human-AI interaction. His research has been featured in numerous conference contributions and journal articles, emphasizing interdisciplinary approaches to solving complex computational and robotic challenges. He can be reached via email at j.stovold@lancaster.ac.uk.

Michael Fosterمشاهده پروفایل

پژوهشگر

• Software Testing
• Causal Inference
• Model Inference
• +۳ مورد دیگر

Dr. Michael Foster is a Researcher and Research Software Engineer at the School of Computer Science, University of Sheffield. He specializes in software testing methodologies, causal inference, and formal methods for system modeling. His work focuses on developing tools for automated model inference, particularly Extended Finite State Machines (EFSMs), and applying causal reasoning to improve software testing. Education: PhD in Computer Science (2017-2020) from the University of Sheffield, supervised by Prof. John Derrick and Dr. Achim Brucker. His doctoral research addressed EFSM inference from black-box software execution traces, integrating genetic programming and formal verification techniques. Research Projects: Principal contributor to the CITCoM project, applying causal inference to computational model testing. Developed the causal testing framework and EFSM inference tool, which formalizes model merging and data dependency analysis. Explored metamorphic testing, automated verification of trace properties, and active learning techniques for EFSMs. Key Contributions: Published extensively on causal testing, EFSM inference, and algorithmic testing strategies. His work bridges formal methods with practical software engineering challenges, emphasizing automated tools and rigorous validation frameworks.

Federica Sarroمشاهده پروفایل

استاد

• Predictive Analytics for Software Engineering
• Search-Based Software Engineering
• Empirical Software Engineering
• +۴ مورد دیگر

Federica Sarro is a Professor of Software Engineering at the Department of Computer Science, University College London (UCL). She leads the SOLAR group, focusing on Predictive Analytics, Search-Based Software Engineering, and Empirical Software Engineering. Her research emphasizes software testing, effort estimation, and app store analysis, with over 70 peer-reviewed publications and collaborations with 20+ institutions worldwide. Her research has been supported by grants including the €2.1M ERC EPIC grant and a £6.8M EPSRC Programme Grant (DAASE). She has supervised numerous PhD students and postdoctoral researchers, including graduated scholars like Afnan Al-Subaihin and William Martin. She is actively involved in program committees and editorial roles, including Associate Editor for ACM TOSEM and IEEE Transactions on Evolutionary Computation. Key achievements include the ACM Distinguished Paper Award (2019), the GECCO-HUMIES Medal (2016), and leadership roles in conferences like ICSE, FSE, and SSBSE. Her work bridges software engineering with machine learning and data analytics, addressing challenges in testing, defect prediction, and agile practices.

Teresa Brunsdonمشاهده پروفایل

مدرس ارشد

• Time Series Analysis
• Compositional Data
• Data Science
• +۳ مورد دیگر

Dr. Teresa Brunsdon is a Senior Lecturer in the Department of Statistics at the University of Warwick, part of the Faculty of Science. She holds a PhD in Time Series Analysis of Compositional Data from the University of Southampton (1997) and has over three decades of academic experience, including 31 years at Sheffield Hallam University before joining Warwick in 2019. Education: BSc in Mathematics Sciences, Loughborough University MSc in Applied Statistics, University of Southampton PhD in Time Series Analysis of Compositional Data, University of Southampton Research Interests: Time Series Analysis Compositional Data Analysis Machine Learning Applications (e.g., interpretable classifiers) Statistical Methodology Education Technology in Data Science Teaching: Delivers core modules such as Introduction to Statistical Practice (ST952) and Designed Experiments (with Advanced Topics) (ST305/ST410) at Warwick. Publications: Focuses on time series methodologies, compositional data, and applications in social media and language processing (e.g., Sinhala documents). Recent work explores ethical AI and interpretable machine learning. Affiliations: Office: MSB 2.11 Email: teresa.brunsdon@warwick.ac.uk Office Hours: Monday 16:30–17:30 and Thursday 11:30–12:30

Marta Vallejoمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Evolutionary Algorithms
• +۷ مورد دیگر

Dr. Marta Vallejo is an Assistant Professor in the Department of Computer Science at the School of Mathematical & Computer Sciences, Heriot-Watt University. She also serves as Programme Director for the Graduate Apprenticeship in Data Science. Previously, she held a tenure-track Research Fellow position in biomedical signal/image processing and was Head of AI at ClearSky Medical Diagnostics Ltd. She is a Fellow of the Higher Education Academy and has contributed to EU-funded projects in Spain. Education: PhD in Computational Intelligence and Predictive Modelling MSc in Artificial Intelligence Her research focuses on machine learning and evolutionary algorithms applied to healthcare, specifically neurodegenerative diseases (Parkinson’s, Huntington’s, ALS) and cancer. Key projects include developing AI-driven clinical assessments, biomarker detection platforms, and image co-registration systems using deep learning. She leads the ML-Healthcare group and collaborates with ClearSky MD and the Proteus project. Her work emphasizes translating academic research into clinical tools, such as virtual staining techniques and wearable tremor monitoring devices. In public engagement, she organizes initiatives like Rubbish Robots (recycled materials for robotics) and contributes to events like the UK-RAS Robot Lab Live. She has received awards for research culture and outward-looking collaborations, including the Spirit of Heriot-Watt Awards 2017 and the 2024 Research Culture Recognition Award. Grants & Leadership: Principal Investigator: Neuro BioMark (EPSRC IAA) Co-Investigator: Lung Cancer Registration (Cancer Research UK) Successfully secured 96,432 GPU hours via EPSRC for high-performance computing Organized EvoStar workshops and led the SPECIES society for evolutionary computation She hosts educational programs at the Robotic Assisted Living Testbed, introducing students to robots like Pepper and Tiago. Her research also aligns with UN Sustainable Development Goals, particularly in advancing healthcare technologies and promoting STEM education.

Patrick Doncasterمشاهده پروفایل

استاد

• Population ecology
• Conservation biology
• Forest ecosystems
• +۷ مورد دیگر

Professor Patrick Doncaster is an academic faculty member at the School of Biological Sciences, University of Southampton, within the Faculty of Environmental and Life Sciences. His research spans population ecology, conservation management of forest and freshwater lake ecosystems, and development of conservation technology and data analysis tools. BSc in Environmental Sciences (1st-class) from University of East Anglia D.Phil. in Zoology from University of Oxford Research interests include: Population ecology Forest biodiversity Human-predator-shared prey interactions Acoustic ecology Open-source conservation technology Early warning signals for ecosystem tipping points Climate change mitigation mechanisms Robust evidence-based analysis methods His publications from 2023-2025 demonstrate expertise in statistical ecology, conservation technology, and ecosystem dynamics across terrestrial, freshwater, and marine environments. He develops tools for ANOVA analysis, radio-tracking data, and evidence-based conservation. Academic contributions include: Active projects on lake ecosystem recovery pathways Completed NERC and PTES-funded field studies Methods development for statistical power and ecological modeling Interdisciplinary collaborations across environmental science and technology Teaching materials on statistics and evolutionary genetics Leadership in the Ecology and Evolution Centre and Institute for Life Sciences He supervises PhD students and maintains active research groups focused on ecological theory and conservation practice.

Richard Watsonمشاهده پروفایل

استاد

• Evolutionary Biology
• Computer Science
• Evolutionary Transitions in Individuality
• +۲ مورد دیگر

Richard Watson is a Professor at the University of Southampton, where he conducts interdisciplinary research bridging evolutionary biology and computer science. His work explores fundamental questions about life, learning, evolution, cognition, and the emergence of complex systems. He is a member of the Agents, Interaction and Complexity research group and the Institute for Life Sciences. His primary research interests include evolutionary biology, computer science, evolutionary transitions in individuality, developmental plasticity, and collective intelligence. He investigates how adaptive systems, from biological evolution to artificial intelligence, exhibit goal-directed behavior and self-organization. His work often employs computational models to understand the principles underlying the evolution of complexity and the emergence of individuality. Recent publications (2019-2024) demonstrate a consistent focus on the intersection of evolutionary theory and computational approaches. His work explores how evolution and development interact to produce adaptive complexity, with particular attention to the role of plasticity, collective intelligence, and multi-scale organization. Key themes include the evolutionary transitions in individuality, the mechanisms of natural induction without selection, and the application of connectionist models to biological systems. Professor Watson currently supervises the following PhD student: Daniel James Sidney Bright (PhD Computer Science) His research is supported by grants from the following sponsors: Engineering and Physical Sciences Research Council (EPSRC) for the PhD Plus Scheme John Templeton Foundation for projects including "Putting the Extended Evolutionary Synthesis to the Test" and "The scaling-up of purpose in evolution (evo-ego)" He is an active member of the Agents, Interaction and Complexity research group within the Institute for Life Sciences at the University of Southampton. His collaborative projects involve interdisciplinary teams, including researchers from biology, computer science, and engineering, focusing on microbial community engineering, computational modeling of biofilms, and the role of developmental plasticity in speciation.

Huiru Zhengمشاهده پروفایل

استاد

• Bioinformatics
• Healthcare Informatics
• Machine Learning
• +۶ مورد دیگر

Professor Huiru (Jane) Zheng is a Professor of Computer Science at the School of Computing, Ulster University. She serves as Theme Leader of Data Analytics and Systems in the AI Research Centre and is a full member of the Computer Science Research Institute. As a Fellow of the UK Higher Education Academy and Senior Member of IEEE, she has established herself as a leading researcher with significant contributions to bioinformatics and healthcare informatics. Her educational background includes: PhD in Bioinformatics (2003) from Ulster University Postgraduate Certificate in Teaching in Higher Education (2005) from Ulster University Professor Zheng's research spans multiple domains of data science with applications in healthcare, agriculture, and environmental monitoring. Her primary interests include integrative data analytics in systems biology, machine learning for healthcare decision support, and assistive technology development. She has particular expertise in applying advanced data mining techniques to complex biological datasets, with a focus on improving healthcare outcomes and supporting independent living through technology. Her extensive publication record demonstrates a clear trend toward interdisciplinary research that bridges computer science with practical applications. Recent work shows increasing focus on real-world implementations of AI in digital health, precision agriculture, and environmental monitoring systems. The breadth of her research interests is evident in publications ranging from gait analysis using smart insoles to methane prediction in dairy farming and wildfire monitoring using UAVs. Professor Zheng has received notable recognition for her contributions: Fellow of the UK Higher Education Academy Senior Member of IEEE As a principal investigator, Professor Zheng has successfully secured substantial research funding from diverse sources including EPSRC, TSB, DEL, NHS, Invest NI, Innovation UK, and the European Commission. Her leadership extends to editorial roles for international journals and organizing major conferences such as the UK Workshop on Computational Intelligence. She has supervised numerous research students through her various projects. Professor Zheng leads several active research initiatives including the AI Research Centre's Data Analytics and Systems theme. Her current projects involve developing digital twin technology for personalized healthcare, AI-assisted systems for post-stroke rehabilitation, methane prediction models for sustainable dairy farming, and age-friendly built environment assessment systems. These projects often involve multidisciplinary collaboration across computer science, healthcare, agriculture, and environmental science domains.

• Wearable Technology
• Human Activity Recognition
• Sensor Data Analysis
• +۳ مورد دیگر

Mathias Ciliberto is a Research Fellow in the Department of Computer Science and Technology at the University of Cambridge. His work focuses on wearable sensor technology, human activity recognition, and multimodal data analysis. He has contributed to major datasets such as the Sussex-Huawei Locomotion and Transportation Dataset, advancing transportation mode recognition and sensor-based analytics. His research includes innovations in earable technologies for gait monitoring, holistic motion analysis, and sensor fusion across audio, inertial, and GPS systems. Key contributions involve developing robust algorithms for activity recognition in challenging data scenarios, including missing data and poor annotations. He has organized international challenges (e.g., SHL Challenge series) and workshops (HASCA), fostering collaboration in sensor-based research. His projects emphasize practical applications in healthcare, sports biomechanics, and human-computer interaction, with a focus on real-world deployment of wearable systems.

Ziad Ibbiniمشاهده پروفایل

پژوهشگر ارشد

• Developmental Biology
• Phenomics
• Marine Biology
• +۳ مورد دیگر

Ziad Ibbini is a Research Fellow at the University of Plymouth's School of Biological and Marine Sciences, affiliated with the Embryo Phenomics research group. His work focuses on advancing bioimaging and computational methodologies to study developmental physiology in aquatic organisms. University: University of Plymouth School: School of Biological and Marine Sciences Position: Research Fellow Email: ziad.ibbini@plymouth.ac.uk His research integrates cutting-edge technology with developmental biology, emphasizing environmental adaptation and automated phenotyping systems. Key contributions include open-source tools like LabEmbryoCam and HeartCV for cardiovascular and behavioral analysis in embryos. Recent work applies deep learning (Dev-ResNet) and computer vision to detect developmental events and predict thermal responses in aquatic embryos, advancing understanding of physiological resilience under climate change stressors. The Embryo Phenomics research group develops spectral and imaging techniques to quantify thermodynamic and environmental responses across species, supporting comparative studies in developmental physiology.

Phil Husbandsمشاهده پروفایل

استاد پژوهشی

• Artificial Intelligence
• Evolutionary Robotics
• Artificial Life
• +۱۲ مورد دیگر

Phil Husbands is a Research Professor of Artificial Intelligence in the Department of Informatics at the University of Sussex, School of Engineering and Informatics. He is a co-founder of the Centre for Computational Neuroscience and Robotics (CCNR) and a founding member of the Evolutionary and Adaptive Systems group (now the AI Group), reflecting his deep interdisciplinary engagement across engineering, neuroscience, and computer science. His research interests span artificial life, evolutionary robotics, biologically inspired neural networks, computational neuroscience, machine learning, and the history and philosophy of AI. He has pioneered work on GasNets (neural networks with diffusible modulators), insect-inspired navigation, and bio-inspired optimization algorithms. His work often bridges theory and application, integrating insights from biology into adaptive robotic systems. The recent publications highlight a strong trajectory in bio-inspired computation, including evolutionary swarm algorithms, neuromodulation models, cognitive architectures, and tactile robotics. His work increasingly explores foundational questions in agency, time perception, and autonomy, suggesting a move toward more comprehensive models of artificial cognition. Scientific Awards: No specific awards listed in the provided text. Phil Husbands has been involved in numerous research grants from major funders including the European Union, EPSRC, BBSRC, and Wellcome Trust. He has supervised multiple doctoral students and collaborated widely, notably with Andy Philippides and Michael O’Shea. He is actively engaged in postgraduate research supervision and public dissemination of science through books like Robots: What Everyone Needs to Know . He leads and contributes to interdisciplinary research teams such as the Evolutionary and Adaptive Systems group and the Centre for Computational Neuroscience and Robotics, fostering collaboration between computer scientists, neuroscientists, and roboticists.