Anish Mukherjee is a Lecturer in the Department of Computer Science. He has held postdoctoral positions at the University of Warwick, University of Warsaw / IDEAS-NCBR, and Charles University in Prague. He earned his Ph.D. from the Chennai Mathematical Institute in India. Ph.D. in Computer Science (Chennai Mathematical Institute) Postdoctoral Experience: Warwick, Warsaw/IDEAS-NCBR, Charles University His research focuses on theoretical computer science, particularly algorithms and complexity theory for dynamic, parallel, and distributed computation. Additional interests include streaming algorithms, graph algorithms, string algorithms, circuit complexity, and exact exponential-time algorithms for NP-hard problems. He has received the TCS Scholarship during his Ph.D. studies. Recent publications examine problems in semi-streaming matchings, network design parameterization, and dynamic query maintenance. Recipient of TCS Scholarship He coordinates the Cyber Security (COMP232) module. His research outputs span conferences like FOCS, ACM SPAA, SIAM, and journals such as the Journal of Computer and System Sciences.
Michael Wooldridge is Professor of Computer Science at the University of Oxford and Senior Research Fellow at Hertford College, having served as Head of Department from 2014-2021. He leads research in artificial intelligence with over 450 publications in multi-agent systems, game theory, and machine learning. His research examines computational approaches to multi-agent coordination, strategic reasoning, and trustworthy AI. Current projects explore foundations of trustworthy AI through theoretical frameworks for rational verification and equilibrium analysis in complex interactive systems. Recent publications demonstrate increasing focus on large language models and their applications in multi-agent coordination, security challenges in generative AI, and computational social systems. Research integrates theoretical work with experimental validation in complex simulation environments. Honors include: Lovelace Medal (BCS, 2020) ACM Autonomous Agents Research Award (2006) AAAI/EAAI Outstanding Educator Award (2021) European Association for AI Distinguished Service Award (2023) He currently supervises doctoral students in multi-agent reinforcement learning and game-theoretic verification. Major grants include a Turing AI World Leading Researcher Fellowship (UKRI, 2021) and ERC Advanced Grant 'Reasoning About Computational Economies' (2011). As Editor-in-Chief of Artificial Intelligence Journal and former president of IJCAI, EurAI, and IFAAMAS, he maintains extensive professional service commitments while leading the Whiteson Research Lab.
Kirsten Hermes serves as Senior Lecturer in Music Performance Technology at the University of Westminster since 2016, merging academic research with professional practice as electronica artist Nyokee. Her work bridges psychoacoustic engineering, electronic music performance, and creative technology development. Her educational foundation includes an EPSRC-funded PhD in Sound Recording and Psychoacoustic Engineering from the University of Surrey, where her thesis focused on spectral clarity predictors in music mixes. Research centers on automatic music mixing tools and spectral clarity modeling , expanding into AI's impact on creativity, virtual artist identities, and chiptune culture. Recent work examines pandemic adaptations in online music communities and audiovisual integration in live electronic performance, emphasizing practical applications for musicians. Analysis of her 15 most recent publications (2021-2025) reveals an evolution from core psychoacoustic research toward interdisciplinary exploration of AI, virtual performance, and cultural adaptation in electronic music. Key trends include human-AI collaboration frameworks, chiptune's digital resilience, and the role of 3D avatars in artist identity construction. She currently supervises doctoral researcher Clara Colotti, whose thesis investigates audiovisual expansion of orchestral cultural scope. While specific grant details aren't provided, her EPSRC-funded PhD demonstrates research funding capability. Affiliated with the university's Music Research Group, she actively engages the electronic music community through Nyokee performances at MAGfest, Comic Con, and Hyper Japan, blending academic inquiry with stage practice.
Dr. Muhammad Habib Ur Rehman is a Senior Lecturer in Computer Science at the University of Bedfordshire's Faculty of Creative Arts, Technologies & Science, within the School of Computer Science and Technology. His research focuses on federated learning in healthcare, blockchain technologies, privacy-preserving AI, and distributed systems. He holds a PhD in Distributed Computing (2017) and has expertise in teaching programming, artificial intelligence, and distributed systems. Research Interests : Federated Learning for Medical Imaging and Healthcare Decentralized AI and Blockchain Integration Privacy-Enhancing Technologies (PETs) Edge Computing and IoT Security Publications : Over 40 peer-reviewed articles on federated learning frameworks, blockchain applications, and IoT security. Notable works include: Privacy-preserving federated learning for radiology Blockchain-based trust systems for cryptocurrencies Edge computing solutions for smart cities Affiliations : Senior Member of IEEE. Active in research institutes like the Institute for Research in Applicable Computing (IRAC). Contact : muhammadhabibur.rehman@beds.ac.uk
Muhammad Muzammal is a researcher at Northumbria University , specializing in cutting-edge technologies at the intersection of computer science and decentralized systems. His work spans blockchain, IoT, and graph theory, with a focus on Security in 5G and IoT networks Efficient data fusion for medical applications Optimization of smart city infrastructure Privacy-preserving trajectory analysis Research trends in his publications highlight advancements in semi-supervised learning , signed network analysis , and blockchain-enabled decentralized solutions . Key themes include AI-driven anomaly detection, green energy distribution, and self-adaptive communication protocols for intelligent transportation systems. His collaborative work appears in journals like Information Fusion , Future Generation Computer Systems , and Knowledge and Information Systems , often addressing challenges in probabilistic data mining and spatio-temporal blockchain processing.
Dr. Conor Muldoon is a Senior Lecturer in Software Engineering at the Department of Computing and Mathematics, Manchester Metropolitan University. His research focuses on distributed artificial intelligence, sensor networks, and multi-agent systems with applications in environmental monitoring and smart home technologies. Previously, he held postdoctoral positions at University College Dublin (UCD) and the University of Oxford, supported by prestigious fellowships including the INSPIRE Marie Curie and Government of Ireland Embark awards. He earned a Ph.D. in Computer Science from UCD, along with a first-class B.Sc. in Computer and Software Engineering and a Postgraduate Certificate in Higher Education Teaching. His research portfolio includes developing water quality forecasting systems, citizen science platforms (e.g., COBWEB), and autonomic energy management solutions. He has published extensively in journals and conferences, addressing topics such as sensor network optimization, incentive mechanisms for crowdsourcing, and adaptive middleware for ambient intelligence. Dr. Muldoon is a Fellow of the Higher Education Academy and supervises PhD candidates in areas like multi-agent systems and environmental informatics. Education: Ph.D. in Computer Science, University College Dublin B.Sc. (Honours) in Computer and Software Engineering, University College Dublin Postgraduate Certificate in Teaching and Learning in Higher Education His work bridges theoretical computer science with real-world applications, emphasizing sustainability and participatory technologies. Key projects include the Dublin Bay Water Quality Modelling and the MERA Data Extraction Toolkit. Awards include recognition for his contributions to agent-based systems and environmental data science.
Dr. Yakubu Tsado is a Lecturer in Cyber Security and Smart Grid at the Department of Computing and Mathematics, Manchester Metropolitan University. He holds a Ph.D. in Electrical Engineering (Smart Grid) from Lancaster University, where he received the Centre for Global Eco-Innovation Ph.D. Scholarship. B.Eng. Mechanical Engineering - Federal University of Technology Minna, Nigeria M.Sc. Communication Engineering & Digital Signal Processing - Lancaster University, UK Ph.D. Electrical Engineering (Smart Grid) - Lancaster University, UK His research focuses on cybersecurity in critical infrastructures , with an emphasis on digital twins , cyber-physical systems , and AI/ML for energy systems . He explores smart grids and microgrids to enhance communication network reliability and real-time monitoring . His work spans industrial IoT security , energy consumption prediction , and peer-to-peer energy trading platforms . Dr. Tsado's publications highlight deep learning frameworks for intrusion detection, reinforcement learning in energy storage optimization, and OLSR routing protocols for resilient smart grid communication. His recent work integrates convex optimization and digital twin technologies to address security challenges in community energy trading systems. Centre for Global Eco-Innovation Ph.D. Scholarship (2017) Dr. Tsado has contributed to high-impact projects including the €3.7M Horizon 2020-SmarterEMC2 Project, the €30M Smart City (Triangulum) initiative, and the £600k NICE project. At Manchester Metropolitan University, he teaches units such as Computer Networks , Information and Network Security , Advanced Computer Networks , and Principles of Data Science , aligning with his expertise in smart grid cybersecurity and AI applications.
Dr. Mobin Naderi is a Research Associate in Energy Storage System Modelling at the School of Electrical and Electronic Engineering, University of Sheffield. His research focuses on advancing microgrid technologies, particularly in renewable energy integration, energy storage systems, and electric vehicle (EV) infrastructure. Key areas include dynamic modeling of interconnected microgrids, stability analysis, and control strategies for enhancing grid resilience and efficiency. Research Interests: - Renewable Energy Systems - Microgrid Design and Control - EV Charging Infrastructure - Energy Storage Optimization - Power System Stability - Decentralized Control Architectures Recent work highlights include techno-economic analysis of hybrid energy storage systems for EV charging stations, experimental validation of lab-scale off-grid setups, and studies on synchronization challenges in interconnected microgrids. His publications emphasize practical solutions for grid resilience, renewable integration, and sustainable energy systems. Notable contributions include developing control algorithms for microgrid stability and exploring opportunities in decentralized energy systems. Current projects focus on advancing smart grid technologies and scalable microgrid solutions for distributed energy resources.
Genki Miyauchi is a Researcher in the Department of Automatic Control and Systems Engineering at the University of Sheffield. His work focuses on swarm robotics, multi-robot systems, and human-swarm interaction. He is completing his PhD at the University of Sheffield, with an expected thesis submission in 2023. He holds an MSci in Robotics and Intelligent Systems from King's College London (2019). Research interests include scalable robotic systems, energy-efficient swarm strategies, and multi-operator control mechanisms. His recent work explores connectivity-preserving swarm control, energy management for heterogeneous drone fleets, and human-swarm collaboration paradigms. He has contributed to projects like CapBot (battery-free swarm robotics) and modular robotic fabrics using Kilobot platforms. Publications highlight advancements in swarm coordination, platooning strategies for crowded environments, and experimental studies on human-robot teamwork in agriculture. His research bridges theoretical control frameworks with practical applications in autonomous systems and cooperative robotics. Genki collaborates with leading institutions and has presented at top conferences like IROS and ICRA. His work emphasizes scalability, usability, and energy sustainability in robotic systems.
Jim Griffin is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on Bayesian nonparametric methods, computational statistics, and applications in financial and economic data analysis. He has collaborated extensively with researchers such as M. Kalli, F. Leisen, and M.F.J. Steel, producing influential work on nonparametric priors, volatility modeling, and sparse regression techniques. Griffin's research interests include developing novel Bayesian methodologies for high-dimensional data, time series analysis, and stochastic volatility modeling. His contributions to computational methods, such as adaptive MCMC and sequential Monte Carlo algorithms, have advanced efficient inference in complex statistical models. He has supervised numerous PhD students, including Alex Diana, Mark Sinclair-McGarvie, and Su Wang, whose work spans Bayesian nonparametrics, computational methods, and financial econometrics. Griffin has published widely in top-tier journals like the Journal of the Royal Statistical Society , Journal of Econometrics , and Bayesian Analysis . His recent work emphasizes integrating computational efficiency with theoretical rigor, addressing challenges in modern statistical applications across finance, ecology, and bioinformatics.
Dr. Devraj Basu is a Senior Lecturer in Accounting and Finance at the University of Strathclyde's Strathclyde Business School. He specializes in financial regulation, cybersecurity, and AI-driven solutions for financial crime mitigation. His research focuses on leveraging technology to combat fraud, money laundering, and malware threats while addressing commodity market volatility and institutional risk dynamics. Professional Activities: Co-investigator in the Fintech - Centre of Innovation in Financial Regulation (Glasgow City Region) project (2025-2026) Principal Investigator for 'Using synthetic data and unsupervised learning methods for malware detection' (2023-2024) Participant in the Financial Regulation Innovation Lab: Morgan Stanley Stakeholder Workshop (June 2023) Research Interests: Dr. Basu's work bridges financial systems and cybersecurity, with emphasis on: AI frameworks for end-to-end financial crime detection Data privacy in payment systems (e.g., authorized push payment fraud) Applications of unsupervised learning in malware identification Volatility mechanisms in commodity markets Grants & Projects: - Led a £150k Innovate UK project on malware detection using synthetic data (2022-2023) - Co-investigator in a £2M Glasgow City Region Fintech initiative Labs & Collaborations: Affiliated with StrathCyber, the University's multidisciplinary cybersecurity hub. Active in cross-disciplinary projects with the Financial Regulation Innovation Lab and Morgan Stanley.
Dr. Maria Kalli is a Senior Lecturer in Statistics at the Department of Mathematics, King's College London, since August 2021. Previously, she held the position of Senior Lecturer in Statistics at the University of Kent and worked as an investment banker at Goldman Sachs in New York. She holds a BSc in Econometrics and Mathematical Economics (LSE), an MBA in Financial Engineering (NYU Stern), an MSc in Mathematical Statistics (University of Michigan), and a PhD in Statistics (University of Kent). She is a Fulbright Scholar and Senior Fellow of the UK Higher Education Academy. Her research focuses on Bayesian Nonparametric Methods, Bayesian Regression, and Time Series Modelling in Macroeconomics and Finance, with applications in financial econometrics and high-dimensional data analysis. She serves as the PhD Admissions Tutor for the Statistics group. Her work emphasizes methodological advances in Bayesian statistics, including MCMC techniques, shrinkage priors, and volatility models. Notable contributions include the development of Bayesian nonparametric vector autoregressive models and flexible dependence frameworks for financial time series. Recent research explores market liquidity effects and predictive distributions in financial markets. Education: BSc Econometrics and Mathematical Economics, London School of Economics MBA Financial Engineering, New York University Stern School of Business MSc Mathematical Statistics, University of Michigan PhD Statistics, University of Kent Scientific Awards: Fulbright Scholar Senior Fellow of the UK Higher Education Academy Advising & Grants: While specific grants are not detailed, her research has been supported through institutional funding and collaborative projects. She actively mentors PhD candidates in Bayesian statistical methodologies and time series analysis. Labs/Teams: She contributes to King's Statistics group, focusing on time series analysis, Bayesian computation, and econometric modelling.
Richard Jensen is a Lecturer in the Department of Computer Science at Aberystwyth University. His expertise spans Feature Selection, Rough Set Theory, and Fuzzy-Rough Sets with applications in data reduction and machine learning. He holds a BSc from Lancaster University, MSc from the University of Edinburgh, and a PhD from the University of Edinburgh. Research interests include developing fuzzy-rough methods for data analysis, instance selection, and optimization algorithms like Harmony Search and Ant Colony Optimization. His work addresses challenges in imbalanced datasets, missing data imputation, and classifier performance enhancement. He has contributed to over 70 publications since 2004, including edited books on computational intelligence and conference proceedings. Jensen has received the Most Cited Paper Award (2010) and serves on editorial boards for journals like IEEE Transactions on Fuzzy Systems and International Journal of Approximate Reasoning. His research aligns with UN Sustainable Development Goals, particularly in advancing computational methods for health data analysis and environmental applications.
Dr. Bob Brewin is an Associate Professor in Earth and Environmental Science at the University of Exeter, UK, and a UKRI Future Leader Fellow. His career includes roles as Senior Lecturer (2021–2024) and Lecturer (2019–2021) at Exeter, alongside prior positions at Plymouth Marine Laboratory and the National Centre for Earth Observation. His research focuses on satellite remote sensing of marine biogeochemistry, phytoplankton dynamics, and citizen science for environmental monitoring. Educated at the University of Plymouth, he holds a PhD in Marine Remote Sensing (2019), MSc in Applied Marine Science (201?), and BSc in Surf Science and Technology (1st class honours). His work bridges global and regional scales, studying ecosystems from polar waters to tropical oceans. Key research interests include marine optics, primary production, bio-optical modeling, and developing algorithms for phytoplankton group detection via remote sensing. He also advocates for citizen science to enhance observations in under-sampled regions, such as using low-cost tools like Secchi disks for water quality monitoring. His TEDx talk in 2019 explored the role of outdoor recreation in managing environmental change, linking societal awareness to climate action. Scientific awards include the UKRI Future Leader Fellowship (2021–present). Brewin collaborates globally, contributing to initiatives like the Atlantic Meridional Transect and NEOM marine ecosystem monitoring. His work integrates ecological concepts with advanced modeling, addressing climate variability impacts on phytoplankton distribution and ocean health.
Professor Gary Higgs serves as Professor in the Faculty of Computing, Engineering and Science at the University of South Wales, where he directs the Geographical Information Systems (GIS) Research Centre and co-directs the Wales Institute of Social and Economic Research, Data and Methods (WISERD). His leadership extends to WISERD's Training and Capacity Building Programme and collaboration with the National Centre for Research Methods (NCRM), focusing on spatial analysis applications in socio-economic policy contexts. His research expertise spans GIS applications in health geography, civil society analysis, and spatial inequalities in public service provision. Specializing in network-based accessibility models, he investigates healthcare access, emergency service distribution, environmental justice, and socio-economic patterns in Wales. His methodological innovations include participatory mapping for facility siting and multi-method spatial analytical approaches to address social vulnerability. Recent publication trends (2023-2025) reveal intensified focus on real-time data integration for dynamic accessibility modeling, particularly in public transport and healthcare services. This work addresses rural-urban service disparities using multimodal transport approaches, with growing emphasis on policy-relevant applications of spatial analytics to mitigate social inequalities. Scientific recognition includes: Impact Awards 2019 for research influence Fellowship of the Royal Geographical Society Professor Higgs has secured major grants including WISERD Phase 1 (2008-2012), WISERD Civil Society (2014-2019), and the current WISERD Transitional Centre 'People, Places, and the Public Sphere' (2024-2027). These ESRC/HEFCW-funded projects examine spatial-temporal service variations, civic stratification, and collaborative governance across Welsh communities. His supervision spans MSc projects, PhD candidates (including KESS-funded research), and extensive external examining roles at multiple UK universities. He leads the USW GIS Research Centre and WISERD's Data Lab initiative, developing community-led data collection systems for co-production projects. Current work focuses on fair work agendas, refugee rights, deliberative politics, and place-based economic innovations through the WISERD Transitional Centre framework.
