Ivan Bravi is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on artificial intelligence, particularly in the context of game AI and automated playtesting. He explores topics such as statistical forward planning, reinforcement learning, and evolutionary optimization for game agents. Bravi's work emphasizes developing intelligent systems capable of navigating complex game environments, including card games like Splendor and unforgiving grid-based simulations like the Game of Life. His contributions include the Rinascimento framework, which employs event-value functions and hyperparameter tuning to enhance agent decision-making. His publications highlight advancements in agent optimization, behavioral expressivity, and self-adaptive algorithms. While no awards are listed, his active research in game AI underscores a commitment to pushing boundaries in computational decision-making and adaptive learning. Bravi collaborates with academic and industry partners to refine AI-driven solutions for gaming and beyond, leveraging interdisciplinary approaches within the School's vibrant research ecosystem.
Rokas Volkovas is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. His work focuses on game design tools, artificial intelligence applications in games, and software engineering methodologies for interactive media. He has contributed to projects such as the 'State Explorer' game design tool and 'Mek' mechanics prototyping framework. His research explores automated game tuning, learning curve analysis in puzzle games, and diversity maintenance in evolutionary algorithms. He holds a position in the School’s academic staff and is affiliated with the university’s research initiatives in computational game theory and software development. No academic awards or grants are explicitly listed in the provided materials. His current activities include advancing tools for game mechanics prototyping and analyzing player behavior through data-driven approaches.
Gavin Brown is a Professor of Computer Science at the University of Manchester, affiliated with the Data Science Institute and the Machine Learning and Optimisation group. His research focuses on machine learning theory, ensemble methods, feature selection, and hardware-accelerated learning. He holds roles in interdisciplinary initiatives like the Centre for Digital Trust and Society, EnnCore project (privacy-preserving neural architectures), and the Robotics and Artificial Intelligence Centre. Education: PhD in Computer Science from the University of Birmingham (2003), with a thesis on 'Diversity in Neural Network Ensembles'. Research interests span theoretical foundations of machine learning, including bias-variance decomposition, ensemble diversity, and algorithmic stability. Applications include healthcare (e.g., outlier detection in vital signs), robotics (low-cost prediction hardware), and ethical AI (conceptual guarding of neural networks). He has pioneered work on feature selection for resource-constrained systems and neuromorphic computing. Recent articles emphasize unifying ensemble theory, hardware-efficient learning (FPGA-based feature selection), and clinical applications of machine learning. Projects include EnnCore (privacy-preserving AI), RAI Centre (robot-AI ethics), and Data Visualisation for clinical trials. He leads or co-leads 8 major projects, including £multi-million initiatives in digital trust, robotics, and AI. Supervised 24 doctoral students, with a focus on interdisciplinary work combining theory and applied machine learning.
Maria Christodoulou is a Senior Statistical Consultant at Oxford University Statistical Consulting within the Department of Statistics at the University of Oxford. With a background spanning evolutionary biology and quantitative sciences, she specializes in biostatistics with expertise ranging from experimental design to machine learning, particularly focused on handling large longitudinal datasets. Her work bridges statistical methodology with biological applications, particularly in morphometrics and botanical classification. PhD in Biological Sciences, University of Reading (2012-2016) MSc Plant Diversity - Taxonomy and Evolution, University of Reading (2009-2010) BSc(Hons) Biological Sciences, Imperial College London (2006-2009) BSc(Hons) Mathematics with Statistics, Imperial College London (2003-2006) Dr. Christodoulou's research centers on biostatistical methods development and application, particularly in morphometrics for biological classification. Her work demonstrates how statistical learning techniques can be combined with morphometric approaches to solve challenging classification problems, as evidenced by her innovative research on apple cultivar identification. She has made significant contributions to plant genomics, aging research, and epidemiological modeling, showing remarkable versatility across biological disciplines while maintaining statistical rigor. Her recent work extends into single-cell transcriptomics, environmental science applications, and cognitive bias research. Analysis of Dr. Christodoulou's publication record reveals a strong interdisciplinary approach that combines statistical methodology with biological applications. Her work spans from fundamental morphometric techniques in botanical classification to advanced applications in genomics, epidemiology, and environmental science. A consistent theme is the development and application of innovative statistical approaches to solve complex biological problems, particularly where traditional classification methods face limitations. As a Senior Statistical Consultant, Dr. Christodoulou provides crucial support for grant applications and manuscript writing while ensuring robust statistical practices in research projects. She supervises students including Dan Phillips and Zhixiao Zhu, and is actively involved with the Econometrics and Population Statistics research group. She is also a Mental Health First Aider at the university. Dr. Christodoulou is particularly passionate about developing and delivering statistical training for non-statisticians, focusing on practical applications of R programming. Her expertise in translating complex statistical concepts for diverse audiences enhances research quality across multiple disciplines at Oxford.
Professor Jotun Hein is a Professor of Bioinformatics at the University of Oxford, affiliated with University College. He holds a position in the Department of Statistics and is a leading researcher in computational molecular biology. Prior to Oxford, he was a lecturer at Aarhus University (1991–2001). His work spans stochastic and algorithmic aspects of molecular evolution, population genetics, and origins of life. Research interests include statistical alignment, ancestral recombination graphs, evolutionary models, and computational approaches to studying genetic recombination. He has developed algorithms for analyzing sequence evolution with insertions, deletions, and substitutions. Notable contributions include methods for minimizing recombination events in phylogenetic analysis and reconstructing ancestral protein sequences using modern machine learning techniques. His publications focus on genetic recombination in SARS-CoV-2, evolutionary models for multigene families, and computational models of life's origins. He collaborates with groups in computational biology and statistical genetics. His work has applications in epidemiology, virology, and drug discovery. Key affiliations include the Computational Biology and Bioinformatics and Statistical Genetics and Epidemiology research groups at Oxford.
Leto Riebel is a Research Associate at the University of Oxford's Department of Computer Science, supervised by Professor Blanca Rodriguez. Their work focuses on computational modeling of cardiac electrophysiology and regenerative therapies. Research interests include: Digital twinning of cardiac systems Computational evaluation of stem cell therapies Arrhythmia risk assessment Ion channel dynamics in cardiomyocytes Integration of medical imaging with simulation Machine learning applications in cardiac modeling Current research trends involve developing simplified elasticity models for cardiac digital twins and investigating Purkinje network dynamics through in silico clinical trials. Their work bridges computational biology with biomedical engineering applications. Key affiliations: University of Oxford - Department of Computer Science Supervisor: Blanca Rodriguez (Professor of Computational Medicine) Location: Wolfson Building, Parks Road, Oxford OX1 3QD
Dr Chico Camargo is a Senior Lecturer (equivalent to Tenured Assistant Professor) in Computer Science at the University of Exeter, where he also serves as the Computational Social Science Theme Lead at the Institute of Data Science and Artificial Intelligence and Deputy Director at the Centre for Climate Communication and Data Science. Additionally, he holds a Research Associate position at the Oxford Internet Institute, University of Oxford, and is a Visiting Professor at the Department of English Language and Literature, Ewha Womans University, Seoul. He is also a Turing Fellow at the Alan Turing Institute and director of the CC Lab. His work has been supported by grants from the Volkswagen Foundation, Oxford IT Innovation Challenges Panel, Lloyd’s Register Foundation, and EPSRC via the Alan Turing Institute. Camargo holds a BSc in Molecular Sciences from the University of São Paulo, followed by postgraduate work at the Wolfson Centre for Mathematical Biology and the Department of Zoology at the University of Oxford. He earned a DPhil (PhD) in Systems Biology as a Clarendon Scholar at Brasenose College, University of Oxford, focusing on complex systems and machine learning applied to biological evolution. His research interests span computational social science, complex systems, data science, cultural evolution, and algorithmic information theory. He investigates how ideas spread and evolve, blending data science with theories of human behavior, culture, and society. Key areas include public opinion dynamics, misinformation analysis, and the application of NLP techniques to social media data. His work also explores collective behavior, digital humanities, and the intersection of computational methods with evolutionary biology. Scientific awards and recognitions include the prestigious Clarendon Scholarship during his doctoral studies. His science communication efforts, such as the award-winning YouTube channel BláBláLogia (part of Science Vlogs Brasil), highlight his commitment to public engagement with science. While specific student names are not listed, Camargo contributes to interdisciplinary research teams and collaborates on projects funded by major organizations. His involvement in initiatives like the TRANSNET project (improving urban transport infrastructure) and “Current Affairs 2.0” (analyzing EU agenda-setting) demonstrates his applied focus on societal challenges. He leads the CC Lab and contributes to the Alan Turing Institute’s research programs, emphasizing collaboration across disciplines to address complex societal questions.
Yongjun Zheng is a Lecturer in Computing at the Department of Electronics, Computing and Mathematics. His research focuses on social network analysis, IoT security frameworks, and E-Healthcare systems. He has contributed to dynamic user interest discovery models in IoT-enabled social networks and secure access mechanisms for multi-cloud environments. His work integrates data mining techniques with cybersecurity principles to address challenges in interconnected systems. Recent publications highlight advancements in emotion perception through eye movement analysis for healthcare applications. Over 265 total views and 70 downloads of his research outputs demonstrate academic engagement, including 1 download this month. No grants, awards, or lab affiliations are explicitly mentioned in the provided text.
Charles Underwood is a Professor of Palaeobiology and Assistant Dean of Earth and Planetary Sciences at Birkbeck, University of London's School of Natural Sciences. His research focuses on chondrichthyan (sharks, rays, and relatives) evolution, tooth development, and Mesozoic-Cenozoic marine ecosystems. He has supervised five doctoral students at Birkbeck and three externally, including recent alumni like Sally Collins (2023) and Simon Wills (2023). Teaching includes modules on Invertebrate and Vertebrate Palaeontology, Palaeoecology, and field techniques. Research spans tooth morphology, rostral denticle evolution, and post-Cretaceous radiation of sharks. Collaborations with global institutions such as the Natural History Museum advance studies on elasmobranch phylogeny and mineralization processes.
Karen Petrie is a Professor (Teaching and Scholarship) of Algorithms and Education in the School of Computing at the University of Dundee. Her research focuses on constraint programming, genetic algorithms, and educational technology, with notable contributions to HIV intervention analysis in Malawi and the integration of social media like Facebook in higher education. She actively engages in public outreach, including organizing events such as Discovery Days and the School of Computing Christmas Lecture. Her work bridges theoretical computer science with practical applications in education and health, emphasizing reproducibility in computational research. She has supervised five students but their names are not listed here. Her activities include school engagement initiatives and contributions to workshops on computing education and computational methods.
Dr. Ana Cocho-Bermejo is a Senior Lecturer in the School of Engineering and the Built Environment at Anglia Ruskin University (ARU), part of the Faculty of Science and Engineering. She joined ARU in January 2022 and previously held roles such as Lecturer and Researcher at UIC Barcelona, Academic Manager and Vice-Dean for Student Affairs at UIC Barcelona, and Co-Director of the Master's program in Artificial Intelligence for Architectural Design at Barcelona Tech. Education: PhD in Technology in Architecture and Building Construction, Barcelona Tech (2012) MArch, Architectural Association of London (2006) MRes in Adaptive Architecture and Computation, The Bartlett (2011) MPhil in Artificial Intelligence, Barcelona Tech (2022) Research focuses on AI and machine learning applied to architectural and urban design, including hybrid design processes, complex systems, and distributed intelligence. Her work explores computational methods for sustainable architectural systems, ETFE membrane systems, and biomimetic strategies. Key areas include generative design, evolutionary algorithms, and urban optimization. Publications highlight contributions in architectural computing, biomimetics, and intelligent façade systems. Notable works include studies on evolutionary computation in urban design and ETFE membrane behavior analysis. She has served on editorial boards for journals like International Journal of Architectural Computing and Sustainability ArchiDOCT . Grants include scholarships from Santander Universia (2011-12) and Pedro Barrie de la Maza/British Council (2004-06). Active in professional organizations such as ACADIA, IACM, and ECAADE. Her teaching spans architecture technology, sustainable design, and computational methods.
Christine Zarges is a Senior Lecturer in the Department of Computer Science at Aberystwyth University. Her research focuses on evolutionary algorithms, artificial immune systems, and optimization techniques, with applications in data preprocessing, anomaly detection, and bioinformatics. She has contributed significantly to the theoretical foundations of immune-inspired randomized search heuristics and has explored their practical implementations in various domains. Her work often addresses challenges in combinatorial optimization, such as improving variable orderings in OBDDs and developing efficient algorithms for Lyndon factorization of biosequences. Recent projects include applications of adversarial diffusion for video anomaly detection and the use of rough set theory for big data preprocessing. She has also been involved in initiatives to address bias in physical science and engineering research. Christine has led and collaborated on externally funded projects, including the Horizon 2020-funded RoSTBiDFramework project and EPSRC grants focused on bias reduction in STEM fields. She has supervised graduate students, such as Yumna Zahid, whose work contributes to video anomaly detection. She actively contributes to the academic community by organizing conferences like EvoCOP and the Genetic and Evolutionary Computation Conference (GECCO), and serves on editorial boards of journals including Evolutionary Computation and IEEE Transactions on Evolutionary Computation .
Professor Jackson C. Kirkman-Brown is a leading academic and clinician in reproductive biology, holding dual roles as Professor in Reproductive Biology at the University of Birmingham and Director of the Centre for Human Reproductive Science . He also serves as Director and Science Lead at Birmingham Women’s Fertility Centre, overseeing one of the UK’s largest PGD services. His work focuses on fertility preservation, sperm motility, and clinical translation of research. He is internationally recognized for developing fertility preservation techniques for patients with catastrophic genital injuries, earning an MBE in 2013 and being named Healthcare Scientist of the Year 2014 . He chairs the Association of Reproductive and Clinical Scientists (ARCS) and sits on committees for the Human Fertilisation & Embryology Authority. His research spans advanced sperm analysis (e.g., FAST software), fluid dynamics in reproductive tracts, and calcium signaling in sperm physiology. Recent publications emphasize standardized semen analysis protocols, computational sperm motility metrics, and lifestyle impacts on male fertility. Collaborations with engineers and clinicians drive interdisciplinary innovation, such as fluid dynamics modeling and pharmacological ligand studies. His leadership in global guidelines (WHO manual updates) ensures high-quality andrology practices worldwide. Key awards include the Commander Joint Medical Command’s Commendation (2012) and NHS Chief Executive’s Special Recognition Award (2010, shared with Major John Clark). He advises documentaries like BBC’s *The Donor Sperm Crisis* and trains professionals via ESHRE courses. As a PhD supervisor, he encourages students to pursue projects on sperm quality diagnostics, flagellar mechanics, and assisted conception trials. His lab, the Centre for Human Reproductive Science, integrates clinical and basic science teams across the UK and globally.
Dr. Mohamed Bader-El-Den is a Professor of Artificial Intelligence and Data Science at the University of Portsmouth's School of Computing. He leads the Portsmouth AI and Data Science Centre (PAIDS), established in 2016, which focuses on advancing AI and ML applications in healthcare, cybersecurity, and digital marketing. His research has secured over £1.1 million in funding, including a recent £580,000 project with ABL 1 Touch starting July 2025. Key collaborations include Fresh Relevance (digital marketing) and Campden BRI (food safety). He supervises 8 PhD students and has successfully mentored 13 others. Notable awards include the 2024 Best Application Paper and the 2021 Best Paper Award. His work aligns with UN Sustainable Development Goals, particularly in innovation and food safety. Research Interests: Machine Learning, Data Mining, Evolutionary Computation, and AI applications in healthcare, cybersecurity, and digital marketing. Projects: Active projects include AI Leadership Skills Bootcamp (Portsmouth City Council-funded) and KTP collaborations with TLC Global and Campden BRI. Past projects include Rule-Based Systems for Classification in ML (2013–2016). Awards: Tech SME of the Year 2023 for Fresh Relevance’s AI-driven marketing success, and two academic paper awards. Labs/Teams: PAIDS Centre, leading cross-disciplinary research in AI-driven solutions for industry and healthcare.
Katharina Huber is an Associate Professor in Computational Biology at the University of East Anglia (UEA), affiliated with the School of Computing Sciences. She currently chairs the Undergraduate and Postgraduate Boards of Examiners for the School and previously served as Director of Postgraduate Research from October 2013 to November 2023. She is a member of the London Mathematical Society since 2011 and the Computational Biology Laboratory. Education: Katharina holds a PhD in Mathematics from the University of Bielefeld (1997). She has held postdoctoral positions, including a Marsden Postdoctoral Fellowship at Massey University (1997–1999), and academic roles at Swedish institutions before joining UEA in 2004. She was awarded a 'docent' title by Uppsala University in 2003. Research Interests: Katharina’s work bridges Phylogenetics , Discrete Mathematics , and Computational Biology . She focuses on developing mathematical theory and algorithms to study molecular evolution, phylogenetic combinatorics, and the analysis of combinatorial objects like cluster systems and finite metric spaces. Her recent projects emphasize algorithmic solutions for phylogenetic networks and hybridization events, as well as applying machine learning to chemical structure prediction. Research Trends: Her recent articles reflect a strong focus on phylogenetic network reconstruction , combinatorial optimization , and computational tools for evolutionary biology. Collaborations span international institutions, and she actively engages in interdisciplinary projects at the intersection of mathematics, biology, and computer science. Grants and Advising: Katharina leads the Huber Studentship (UKHSA/OHID, £41,603.42), focused on small molecule structure elucidation. She has supervised projects funded by the Royal Society and the Engineering and Physical Sciences Research Council. She actively recruits PhD students interested in Phylogenetics and related algorithmic challenges. Labs and Teams: She contributes to the Computational Biology Laboratory at UEA and collaborates across institutions, including Mestrelab Research S.L. and the Swedish University of Agricultural Sciences.
