David Leslie is a Professor of Statistics and Director of Engagement in the Department of Mathematics and Statistics at Lancaster University. He specializes in statistical learning, decision-making algorithms, and game theory, with applications in real-time website optimization through bandit algorithms. Previously, he served as a Senior Lecturer at the University of Bristol and co-directed a cross-disciplinary decision-making research group. He has led significant projects such as the EPSRC/NERC-funded DSNE initiative and contributed to strategic partnerships like ALADDIN (with BAE Systems and EPSRC) and NG-CDI (with BT). His work emphasizes bridging theoretical research with practical industry solutions. Education details are not explicitly provided, but his career trajectory includes prior roles at prestigious institutions. Research interests focus on statistical methodologies, AI-driven decision systems, and environmental data science. He advises multiple PhD students in areas like Statistical AI and Extreme Value Theory. Notable projects include AI Hub, NABS+, and ProbAI, reflecting his engagement with cutting-edge technologies and interdisciplinary collaboration. David actively participates in research groups such as STOR-i Centre for Doctoral Training and the Statistical Artificial Intelligence group. His contact information includes an office at B73 in the PSC building and a direct email address. No specific scientific awards are listed, but his extensive project leadership and contributions to foundational AI research highlight his academic impact.
Jefersson Alex dos Santos is an Assistant Professor (Lecturer) in Computer Vision at the University of Sheffield, UK. Previously, he served as an Associate Professor at Universidade Federal de Minas Gerais (UFMG), Brazil (2013–2022). He holds a PhD in Computer Science from the University of Campinas (Unicamp) and the University of Cergy-Pontoise, France (2013). His research focuses on remote sensing image processing, computer vision, and machine learning, with applications in geospatial data analysis and medical imaging. He is an IEEE Senior Member and serves as an Associate Editor for IEEE Geoscience and Remote Sensing Letters and Co-Chair of the ISPRS Working Group for AI/ML in Geospatial Data. Education: PhD in Computer Science: University of Campinas (Unicamp) & University of Cergy-Pontoise, 2013 Master's in Computer Science: Unicamp, 2009 Bachelor's in Computer Science: University of Mato Grosso do Sul (UEMS), 2006 Research Interests: Remote sensing image processing, computer vision, machine learning, and geospatial data analysis. His work emphasizes interdisciplinary research, including applications in environmental monitoring, medical imaging, and digital forensics. Grants & Awards: CNPq Productivity Research Scholarship (2016–2022) Serrapilheira Institute Research Grant (2021) Labs & Teams: Founder of the Laboratory of Pattern Recognition and Earth Observation (PATREO) at UFMG's Department of Computer Science.
Dr Luca Manneschi is a Lecturer in Machine Learning at the School of Computer Science, University of Sheffield, with an IBM Liaison role. He holds a PhD in Physics from the University of Sheffield (2021) and completed a PostDoc there before his current position since March 2022. His research focuses on designing learning algorithms inspired by biological networks for physically defined systems, emphasizing neuromorphic computing, reservoir computing, and stochastic environments. Education: Bachelor's in Physics: University of Padua (Italy) Master's in Physics: Sapienza University of Rome (Italy) PhD in Physics: University of Sheffield (2021) Research Interests: Dr. Manneschi explores algorithms for physically defined networks, leveraging biological network principles to enhance computation in dynamic environments. His work bridges machine learning with neuromorphic hardware, emphasizing adaptability and energy efficiency. Key areas include reservoir computing, magnetic metamaterials, and multi-timescale learning strategies. Grants & Funding: "Real-time Reservoir Computing on Prosthetic Devices" (2023–2025, Royal Society, PI) "MARCH: Magnetic Architectures for Reservoir Computing Hardware" (2021–2025, EPSRC, Co-PI) "CausalXRL: Causal Explanations in Reinforcement Learning" (2021–2024, EPSRC, Co-PI) "ActiveAI" (2019–2024, EPSRC, Co-PI) Lab/Team Affiliation: Machine Learning Research Group, School of Computer Science.
Roles and Affiliations: Sukhpal Singh Gill is a Lecturer (Assistant Professor) in Cloud Computing at Queen Mary University of London (QMUL), UK. He leads the GillNet Research Lab and is the Editor-in-Chief of the International Journal of Applied Evolutionary Computation (IJAEC) . He also serves as an Associate Editor for journals like IEEE IoT and Nature Scientific Reports. As Programme Director for MSc Advanced Computer Science and MSc Business Analytics, he contributes to curriculum development and education excellence. Research Interests: His research focuses on Cloud Computing, Edge AI, Internet of Things (IoT), and Energy Efficiency. He explores AI-driven solutions for resource management, security, and sustainable computing. Key areas include fog-edge integration, serverless computing frameworks, and healthcare applications. Publications and Impact: With over 200 peer-reviewed publications (including IEEE TCC, Elsevier JSS, and ACM TOIT), Dr. Gill has achieved 12,500+ citations and an H-index of 54 (Google Scholar). His work has been featured in IEEE Spectrum and Tech Monitor. Notable contributions include frameworks like HealthEdgeAI (healthcare systems), CloudAISim (cloud simulation), and EdgeAISim (edge computing modeling). Awards and Recognition: Recognized with the 2024 IEEE Outstanding Reviewer Award, Elsevier Editor’s Choice Award, and Queen Mary Education Excellence Award. He is a Fellow of the Higher Education Academy (FHEA). Teaching and Leadership: Teaches modules like Cloud Computing (Postgraduate) and Semi-structured Data Modeling. Leads the Networks and Systems Teaching Group (N&STG) and chairs academic misconduct panels. Advocates for inclusive curriculum design and intercultural development in higher education. Labs and Collaborations: The GillNet Lab develops next-generation systems for EdgeAI, CloudAIBus, and CloudAISim. Collaborates with institutions like Lancaster University, The University of Melbourne, and industry partners on fog-cloud IoT ecosystems.
Dr. Simone Krummaker is an Associate Professor of Insurance and Associate Dean of the MSc Programmes at Bayes Business School, City St George’s, University of London. She holds a Dr. rer. pol. from Leibniz University of Hannover and is a certified insurance professional (Versicherungskauffrau) with over 10 years of industry experience in underwriting and controlling. Her research focuses on organizational insurance demand, ESG integration in insurance, and export credit insurance. She has led multiple grant-funded projects and serves as a Fellow at Offenburg University of Applied Sciences and the Higher Education Academy. Education: Doctor rer. pol., Leibniz University of Hannover Diplom-Ökonom (Business Economics), Leibniz University of Hannover Higher Education Academy Fellowships (United Kingdom) Research Interests: Simone explores how firms strategically manage insurance demand, particularly in export and political risk contexts. She evaluates the role of ESG frameworks in shaping insurance practices and advises organizations on mitigating geopolitical trade risks. Her work bridges academic theory with practical applications, as seen in her consulting for export finance institutions. Awards: Fellow, Higher Education Academy Senior Fellow, Advance HE Institute for Trade and Innovation Fellowship Advising & Grants: Simone has secured grants for projects on export credit markets and co-authored over 40 publications. She oversees MSc programmes and mentors students in actuarial science and insurance domains. Labs/Teams: Active in Bayes’ Centre for Risk and Insurance, leading interdisciplinary projects on global trade finance and sustainable insurance practices.
Jin Zhu is a Researcher in the Department of Statistics at the London School of Economics and Political Science (LSE), working with Prof. Chengchun Shi on reinforcement learning and machine learning. His research focuses on developing algorithms with statistical and computational guarantees, alongside statistical software design to enhance algorithmic applications. Prior to LSE, he earned his PhD in Statistics at Sun Yat-Sen University under Dr. Xueqin Wang and Dr. Na You. Key expertise includes reinforcement learning, machine learning, and computational statistics. His work addresses challenges in off-policy evaluation, robustness in RL, and sparsity-constrained optimization. He has contributed to open-source tools like skscope and abess for efficient statistical computation. Research interests also span causal inference, high-dimensional data analysis, and algorithmic design for complex systems. Notable contributions include methodologies for genetic factor identification, spatial experimental design, and nonparametric statistical inference. Jin’s research bridges theoretical advancements with practical software implementations to address real-world computational and statistical challenges.
Sze Ming Lee is a Researcher in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on Social Statistics, with expertise in large-scale data analysis, latent variable modelling, survival analysis, and quantile regression. Lee holds a BSc in Mathematics, an MPhil in Risk Management Science, and a postgraduate diploma in Education, all from the Chinese University of Hong Kong. His academic supervisors are Dr. Yunxiao Chen and Professor Fiona Steele. His research interests emphasize methodological advancements in statistical modelling, particularly in handling high-dimensional data and latent variables. Recent publications (2023-2025) highlight contributions to quantile regression, factor analysis stability, and longitudinal data methodologies. Lee’s work bridges theoretical statistics with practical applications in social sciences and biostatistics. No scientific awards or grants are explicitly mentioned in the provided texts. He is affiliated with LSE’s Social Statistics research group and contributes to the department’s focus on data-driven social science research.
Mingli Chen is an Associate Professor of Economics at the University of Warwick’s Department of Economics. She holds affiliations including Turing Fellow at the Alan Turing Institute, External Fellow at the Centre for Panel Data Analysis (University of York), and Warwick-China Coordinator. Her research focuses on econometrics, machine learning, time series analysis, financial econometrics, and empirical industrial organization. She has served as an Associate Editor for the Journal of Econometrics since 2024 and organized workshops on data science and network analysis. Education: Ph.D. in Economics from Boston University (2015), B.A. in Information and Computing Science from Shanghai University (2009). She has held visiting positions at Stanford University, UC Berkeley, and the Federal Reserve Bank of Boston. Research Interests include high-dimensional econometrics, panel data models, social networks, quantile regression, and the integration of AI with econometrics. Key publications cover topics like quantile graphical models for systemic risk, latent panel quantile regression in asset pricing, and sparse β-models for network analysis. Awards include the International Partnerships Fund (2023), Turing PDRA Award (2020), and Co-Winner of the LABOUR Prize (2017). She advises Ph.D. students at Warwick and Cambridge, with placements at leading institutions like the University of Tokyo. Grants include leadership in UK-China partnerships and the Turing Institute. Teaching focuses on advanced econometrics at the Ph.D. level, including causal inference and machine learning. She co-organizes workshops and serves on conference committees, emphasizing data science and policy applications.
Sukhi Shergill is a Professor of Psychiatry & Systems Neuroscience at King's College London's Institute of Psychiatry, Psychology & Neuroscience (IoPPN), affiliated with the NIHR Maudsley Biomedical Research Centre (BRC). They hold a clinical role as a Consultant Psychiatrist at the National Psychosis Service at Bethlem Royal Hospital. Shergill's research focuses on the mechanisms underlying psychotic symptoms in schizophrenia, leveraging functional neuroimaging, psychophysics, and therapeutics. Their work intersects with UN Sustainable Development Goals related to mental health and well-being. Education: PhD in Psychiatry (2001, thesis on auditory hallucinations using functional MRI). Research interests span schizophrenia pathophysiology, neuroimaging biomarkers, and digital health interventions for psychosis. Key projects include the TEASC study on antipsychotic treatment response and the STOP app-based therapy for paranoia. Shergill has supervised 9 academic works and leads 2 active research grants from the Medical Research Council (MRC). Publications emphasize digital mental health safety, oxytocin modulation in psychosis, and clozapine monitoring. Their labs and collaborations include the CSI Lab and international networks studying neuroimaging and clinical interventions. Grants and partnerships involve MRC, Deloitte, and NHS initiatives addressing treatment-resistant psychosis and cognitive remediation.
Paulo Lisboa is a Professor in Applied Mathematics at Liverpool John Moores University, where he conducts interdisciplinary research at the intersection of computational modeling, machine learning, and biomedical applications. His work spans healthcare technology, sports biomechanics, and genomic data analysis. His research focuses on developing advanced computational methods for real-world health challenges. Key areas include explainable AI for clinical classifiers, deep learning for perinatal monitoring, and modeling complex biological interactions in genomics. He also investigates biomechanical loading during physical activity using sensor-based and simulation techniques. The recent publication trends highlight consistent contributions in artificial intelligence applied to medicine and biology, particularly in making black-box models interpretable, predicting preterm birth through genetic interactions, and analyzing human movement dynamics. These works are published in high-impact journals across computational biology, biomedical engineering, and sports science. Paulo Lisboa actively collaborates with researchers in physiology, sports science, and clinical domains. His peer review activities for journals like Communications Medicine and PLOS ONE reflect his engagement with the broader scientific community. While no formal awards or student supervision details are listed, his extensive publication record demonstrates sustained scholarly impact in applied mathematics and translational computational research.
Dr. Beatriz Galindo-Prieto is a Researcher at Imperial College London's School of Public Health (Faculty of Medicine), part of the Environmental Toxicology Group within the Environmental Research Group. She specializes in developing multiblock/multi-omics and multivariate data analysis methods, with a focus on big data interpretation, variable selection, and applications in environmental health and bioinformatics. Her current projects include analyzing air pollution effects on health via RNA-seq datasets (UKRI NERC-funded) and policy studies on urban air quality. Affiliations include the MRC Centre for Environment and Health, and NIHR Health Protection Research Units in Environmental Exposures and Chemical/Radiation Hazards. Education: MSc in Chemistry (University of the Balearic Islands) and PhD in Chemometrics (Umeå University, Sweden). Her PhD involved novel variable selection algorithms for multivariate data (VIPOPLS, VIPO2PLS, MB-VIOP). Post-PhD roles include postdoctoral fellowships at NTNU (Norway) and Weill Cornell Medical College (USA), focusing on machine learning for big data in neuroscience and epidemiology. She has taught undergraduate/graduate courses and co-supervised students. Research Interests: Combining chemometrics, bioinformatics, and machine learning to address societal challenges. Key areas include multivariate data analysis for environmental systems, neurodegenerative diseases (Alzheimer’s), and real-time epidemic modeling (Ebola, COVID-19). She emphasizes interdisciplinary collaboration across analytical chemistry, systems biology, and public health policy. Grants & Funding: UKRI NERC, ERCIM (Big Data Cybernetics project), and institutional support from Imperial College London. Her work bridges academia and real-world applications, aiming to translate computational methods into actionable insights for health and environmental policy. Labs/Teams: Active in the Environmental Toxicology Group at Imperial, collaborating with the MRC Centre and NIHR units. Her lab develops novel methodologies for data fusion, visualization, and predictive modeling in complex systems.
Dr. Alessio Faccia is an Assistant Professor in Finance at the School of Business and Law, University of Birmingham Dubai, with a strong international academic and professional background. He holds a PhD in Business Economics (Accounting & Finance) from Università Politecnica delle Marche and has taught at institutions in Italy, UAE, UK, and Malta. He is also a qualified Chartered Accountant and Auditor in Italy, with prior experience at Accenture and as founder of an independent audit firm. Education: PhD in Business Economics (Accounting & Finance), Università Politecnica delle Marche, 2012 MBA (Hons) in Business Economics & Accounting, Università Roma TRE, 2006 BSc in Business Economics and Accounting, Università Roma TRE, 2004 LLM, Pegaso University, 2019 MSc in Criminology, Pegaso University, 2017 CA (Chartered Accountant and Auditor), Italy, 2009 Fellow of the Higher Education Academy (FHEA), 2021 Certified Management & Business Educator (CMBE), 2020 His research spans interdisciplinary domains including blockchain, accounting information systems, machine learning, fraud examination, and sustainable finance. He explores how emerging technologies like AI, blockchain, and cognitive computing can enhance transparency, resilience, and innovation in financial systems. His work often bridges theory with practical applications in fintech, auditing, and regulatory compliance. His recent publications reflect a strong trend toward leveraging big data, blockchain, and AI to transform traditional finance and accounting practices. Topics include blockchain for supply chain risk, NLP for financial transparency, generative AI in education, and quantum fintech. These contributions appear in high-impact journals such as Annals of Operations Research , Sustainability , and Big Data and Cognitive Computing . Scientific Awards and Recognition: Fellow of the Higher Education Academy (FHEA), 2021 Certified Management & Business Educator (CMBE), 2020 Dr. Faccia actively contributes to the academic community as a reviewer for top-tier journals including Technovation , Chaos, Solitons & Fractals , PLoS ONE , and International Journal of Production Economics . He serves on the editorial board of the International Journal of Accounting & Finance Review and Journal of Risk and Financial Management . He has also acted as Guest Editor for special issues and Publicity Chair for international conferences, demonstrating leadership in academic dissemination. He supervises MSc students’ dissertations in finance and accounting and teaches postgraduate modules such as Financial Statement Analysis, Risk Management, and Alternative Finance. His advisory role extends to guiding research on blockchain, fraud, and sustainable business models. He has no listed grants, but his collaborative research indicates strong engagement with international scholars. Dr. Faccia is involved in cutting-edge research teams focused on blockchain, AI in finance, and digital transformation. He contributes to initiatives like the International Conference on Cloud and Big Data Computing and participates in technical research on ERP integration, semantic web applications, and cybersecurity resilience, positioning him at the forefront of technological innovation in business education.
Xiangren Shi is a researcher at Bournemouth University , contributing to fields such as motion capture, robotics, and machine learning. His work focuses on enhancing inertial motion capture systems using transformers and optimizing 3D point-cloud compression with convolutional neural networks. Research Interests : Motion capture algorithms, sensor calibration, machine learning applications in robotics, point-cloud processing, teleoperation systems, and sensor fusion. Recent publications highlight advancements in dynamic on-body IMU calibration for motion capture and real-time humanoid teleoperation systems. His work integrates neural networks for sensor optimization and 3D motion analysis. Key trends include wearable sensor technologies, signal processing, and 3D reconstruction techniques.
Benedetta Catanzariti is a British Academy Postdoctoral Fellow at the University of Edinburgh's School of Social and Political Science, with dual affiliation as a PostDoctoral Affiliate at the Centre for Technomoral Futures within the Edinburgh Futures Institute. She actively contributes to the AI Ethics & Society network, focusing on the social, historical, and political dimensions of data-driven technologies through qualitative STS (Science and Technology Studies) methodologies. Her work critically examines machine learning data practices, classification systems in algorithmic decision-making, and engineering cultures across industry, research, and educational contexts. Education: PhD in Science, Technology and Innovation Studies, University of Edinburgh (2023) MScRes in Science and Technology Studies, University of Edinburgh (2019) Master in Philosophy, University of Turin (2016) Her research investigates how data objectivity claims emerge within specific cultural imaginaries, with current emphasis on translating medical uncertainty into diagnostic AI outputs. Recent projects analyze facial expression recognition in healthcare, generative AI threats to parliamentary democracy, and ethical integration in computer science curricula. She develops reflexive tools to address algorithmic harm while documenting global labor practices in AI development and anti-surveillance resistance tactics. Article trends reveal escalating focus on AI's societal crises: 2025 works dissect objectivity construction in data annotation and AI governance metaphors, while 2024 outputs target democratic vulnerabilities (Chamberfakes), CS curriculum politics, and translational ethics teaching. Medical AI and emotion recognition studies (2020-2023) establish foundations for current work on medical imaging uncertainty. All publications consistently apply STS lenses to expose hidden power structures in data systems. Scientific Awards: SPS Outstanding Dissertation Award (2023) for 'Seeing affect: knowledge infrastructures in facial expression recognition systems' AsSIST-UK Andrew Webster PhD Prize (2024) She supervises Oksana Dorofeeva (visiting PhD, Aarhus University) and four CDT project students (Jacqueline Rowe, Amanda Horzyka, Osman Batur Ince, Cyndie Demeocq), previously guiding Sandra Wheeler's MSc in Data Science for Health and Social Care. Funded by a British Academy Postdoctoral Fellowship (2023-2026) for 'Technology in Translation: Investigating Organizational Contexts of AI Development', she also secured DCMS Policy Fellowship support under AHRC's BRAID programme. Current teaching includes Data and AI Ethics as Practice (2025) and Data Ethics in Health and Social Care (2024). Operates within the Centre for Technomoral Futures and AI Ethics & Society network, collaborating with Scottish Centre for Crime & Justice Research on parliamentary democracy threats. Organizes key events like the 2024 'AI as the Broken Machine' conference and 2022 'Ethics of Care and Community in AI Practice' workshop, while developing conceptual tools for medical AI practitioners through her active British Academy project.
Prof. Leandro Sanchez Betancourt is a faculty member at the University of Oxford , affiliated with the Mathematical Institute . His work bridges Mathematical Finance and Stochastic Games , with a focus on high-frequency trading and market microstructure. He has been recognized with prestigious awards, including the Bruti Liberati Prize for Quantitative Finance research and the Gabino Barreda medal from UNAM. Education Doctor of Philosophy (DPhil) Master of Science (MSc) Bachelor of Science (BSc) Research Contributions Prof. Sanchez Betancourt’s research addresses critical challenges in algorithmic trading , latency impacts , and toxic order flow . His recent publications explore Nash Equilibrium in broker-trader dynamics, Mean Field Games for informed trading, and innovative applications of Lévy-Ito Processes and Quantum Measurement in financial modeling. These works highlight his expertise in integrating advanced mathematics with real-world trading strategies. Scientific Awards Bruti Liberati Prize for best PhD thesis in Quantitative Finance Best overall performance student award from King’s College London Gabino Barreda medal from Universidad Nacional Autónoma de México Labs and Teams He is a member of the Mathematical and Computational Finance research group at the University of Oxford, collaborating with leading experts like Álvaro Cartea, Sebastian Jaimungal, and others.
