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Safia BarikzaiView profile

Associate Professor

• Educational Technology
• Courseware Engineering
• Technology Startups
• +4 more

Dr. Safia Barikzai serves as an Associate Professor in the School of Computer Science and Digital Technologies at London South Bank University (LSBU), holding responsibilities for employability, student experience, and academic enrichment across South Bank Academies. She previously contributed as the Equality, Diversity, and Inclusion (EDI) Lead for the School of Engineering, collaborating with external organizations to foster inclusive tech environments. Her educational background includes a PhD in Courseware Engineering completed at LSBU in November 2006, with a thesis titled 'Integrating Courseware into Collaborative Learning Environments'. Her academic journey began in 1996 through a Teaching and Learning Technology Programme (TLTP) funded project involving seven UK institutions. PhD in Courseware Engineering, London South Bank University (2006) Undergraduate degree completed prior to 1996 Dr. Barikzai's research spans educational technology foundations in courseware engineering to contemporary investigations of technology entrepreneurship. Her current work employs qualitative methodologies to analyze entrepreneurial pivoting in tech startups, examining triggers, adaptation strategies, and survival mechanisms within the UK high-tech ecosystem. This evolution reflects a strategic shift from learning environment design to venture development dynamics while maintaining a focus on collaborative systems. Analysis of her 2019-2024 publications reveals a dominant focus on technology entrepreneurship (80% of outputs), particularly through qualitative studies of startup pivoting. Her 2024 decolonization panel work extends this critical perspective to institutional history, while her sole 2019 publication demonstrates interdisciplinary application of machine learning in medical diagnostics for cancer and diabetes. Scientific awards: None documented in provided materials. Dr. Barikzai has supervised two postgraduate students and served as Course Director for a Masters programme at the University of Wollongong (2002-2005), managing offshore dissertations in Hong Kong. Her grant involvement includes the Teaching and Learning Technology Programme (TLTP) project and subsequent funding for collaborative learning tools. She maintains active roles in curriculum development, serving on education committees and teaching Analysis and Design alongside Innovation and Enterprise courses. Supervision: 2 postgraduate students Past Leadership: Masters Programme Course Director (University of Wollongong, 2002-2005) Committee Service: School Education Committee member As part of LSBU's Digital x Data Research Centre, Dr. Barikzai contributes to academic enrichment initiatives for South Bank Academies while advancing UN Sustainable Development Goals through educational innovation. Her collaborative networks include external diversity-focused organizations working to transform engineering and technology sectors.

• Macro News Announcements
• Financial Econometrics
• Empirical Asset Pricing
• +4 more

Dr. Kevin Evans is a Reader in Finance at Cardiff Business School, specializing in financial markets' information processing, high-frequency financial econometrics, and empirical asset pricing. He currently supervises 5 PhD students and has advised 9 previously, with research themes spanning ESG investing, corporate innovation, and macroeconomic news impacts. Current Supervision: Ebtehal Ramadan (ESG Investing), Doreen Dai (Cryptocurrencies), Xingpu Fan (Intraday Momentum), Jianchen Shi (Corporate Innovation), Yueyang Wang (Biodiversity Risk), Jaenam Ha (ESG). Past Supervision: Konstantinos Theodorou (Empirical Asset Pricing), Shihao Pei (Macro News), Kefu Liao (Financial Econometrics), Sahar Alabdullah (Corporate Culture), Junqiu Li (Investment Management), Mujeeb-U-Rehman Bhayo (Empirical Asset Pricing), Matthew Barwick-Barrett (Socially Responsible Investment), Woon Sau Leung (Contagion), Fangzhou Huang (Empirical Asset Pricing). Research Interests: Dr. Evans's work focuses on how financial markets react to macroeconomic news, volatility dynamics, and the intersection of ESG/sustainability with corporate innovation. Recent projects explore machine learning in asset pricing and drift burst analysis. Publications: His work appears in top journals like the Journal of Financial and Quantitative Analysis and Journal of Empirical Finance , with recent trends in volatility forecasting, R&D anomalies, and corporate governance. Scientific Awards: 2025: Shortlisted for Cardiff University Most Impactful Use of Assessment, Nominated for Student Voice and Partnership, Doctoral Supervisor of Year. 2024: Nominated for Cardiff Business School leadership and innovation awards. 2022: Winner of Cardiff Business School Outstanding Contribution and Teaching Awards. Education: PhD in Financial Economics from Swansea University (2008), MSc in Economics and Finance (Distinction, 1999) from Warwick, and BSc (First Class, 1998) from Swansea. Additional Roles: Deputy Head of Section for Research, Impact, and Innovation (2024–), Director of Empirical Finance Research Group (2019–2022), and external examiner for Hong Kong University and University of London.

• Fintech
• Sustainable Finance
• Behavioural Finance
• +4 more

Professor Qingwei Wang holds a Personal Chair of Finance at Cardiff Business School, Cardiff University, where he serves as Head of the Accounting and Finance section. He is the co-founder and co-director of the Cardiff Fintech Research Group and has previously directed the Cardiff Empirical Finance Group and Centre for China Business Research. Before joining Cardiff in 2014, Professor Wang was a Lecturer in Finance at Bangor Business School and a researcher at the Centre for European Economic Research in Mannheim, Germany. Professor Wang earned his Ph.D. in Finance from the University of Mannheim, M.A. in Economics and Management Science from Humboldt University at Berlin, LL.B. in International Economic Law from Fudan University, and BA in Economics from Anhui Normal University. He also holds a PGCert in Higher Education (2014). His research focuses on three interconnected areas: Fintech (cryptoassets, machine learning applications, big data investing), Sustainable Finance (ESG, CSR, greenwashing, climate risk), and Behavioural Finance (investor sentiment, attention, biases in financial decision-making). His work has been published in top journals including Journal of International Business Studies, Journal of Empirical Finance, European Financial Management, and International Journal of Forecasting. Professor Wang's recent publications reveal a strong focus on cryptocurrency market efficiency, behavioral aspects of investing, and the intersection of technology with traditional finance. His work spans both theoretical and empirical approaches, with increasing emphasis on sustainable finance applications in recent years. Best AFS Paper Award at the AFS 2025 Conference Best Paper Award at the 8th China Finance and Accounting Conference (2024) Best (PhD) Paper Award at the 2024 Edinburgh World-Class Workshop in Fintech and Sustainable Finance Best Paper Award at the 22nd BAFA SIG-AFEE Workshop (2022) Multiple finalist nominations for Fintech Wales Awards (2021-2023) Professor Wang has supervised numerous PhD students who have secured positions at universities including Bangor, Cardiff, Southampton, Northampton, Greenwich, and Groningen. He serves on editorial boards of British Accounting Review, Finance Research Letters, and The European Journal of Finance, and has delivered invited talks at prestigious institutions worldwide including European Central Bank, University of Glasgow, and Xiamen University.

Piotr OmenzetterView profile

Senior Lecturer

• Structural Health Monitoring
• Structural Dynamics
• Offshore and Subsea Structures
• +5 more

Dr. Piotr Omenzetter is a Senior Lecturer at the School of Engineering , University of Aberdeen, specializing in structural health monitoring (SHM), dynamic testing, and reliability analysis of civil infrastructure. His research focuses on offshore structures, wind turbines, bridges, and buildings, with applications of artificial intelligence and signal processing. Positions: Senior Lecturer (University of Aberdeen) Education: ME (Technical University of Gdansk), PhD (University of Tokyo) Research Interests include: Structural Health Monitoring (SHM) for offshore and subsea systems, wind turbines, and bridges Theoretical and experimental structural dynamics, including soil-structure interaction Reliability analysis under uncertainty and earthquake engineering Artificial intelligence (neural networks, deep learning) and signal processing (time series analysis) Structural control and optimization for wind farms and bridges Publication Trends show a focus on data-driven approaches like SHM and finite element model updating to assess damage severity in wind turbines and bridges. His work integrates machine learning (e.g., distance metric learning, neuro-fuzzy systems) and optimization algorithms (virus optimization, sequential niche techniques) to enhance damage detection accuracy. Teaching Responsibilities include courses on structural dynamics and reliability analysis for oil & gas engineering students. He has reviewed for 17 journals, including Computer Aided Civil and Infrastructure Engineering and Engineering Structures .

• Sustainable Energy
• Energy Efficiency Technologies
• Renewable Energy Systems
• +3 more

Dr. Xiaoli Ma is a Senior Research Fellow at the University of Hull, affiliated with the Energy and Environment Institute and the Centre for Sustainable Energy Technologies under the Faculty of Science and Engineering. Her research focuses on sustainable building services, renewable/sustainable energy systems, energy efficiency technologies, and instrumentation technologies. She has secured over £2.7 million in research funding from bodies such as the EU, EPSRC, Innovate UK, and the National Science and Technology Committee of China, leading or co-leading 26 projects. Her research interests include innovative cooling systems for data centers (e.g., super-performance dew point cooling achieving 90% energy savings), solar-driven energy systems, waste heat recovery, and thermoelectric technologies. Notable projects include the development of a novel Loop-Heat-Pipe-based data center cooling system funded by the EU FP7 and a solar façade hot water heating system supported by the EU FP7 Programme. Dr. Ma has published over 90 journal articles, two books, and three book chapters, with recent works focusing on machine learning-driven energy optimization, solar cooking systems with energy storage, and advanced heat pump technologies. She holds two patents and actively supervises PhD students in renewable energy, energy efficiency, and built environment applications. Her lab and team are part of the Energy and Environment Institute, collaborating on projects like the pioneering near-zero-carbon air conditioning system using atmospheric latent heat and natural light energy (EPSRC-funded). Key grants include a £814k EPSRC project and a £692k IEEA initiative for data center cooling advancements.

• Control Systems Engineering
• Mechatronics
• Condition Monitoring
• +5 more

Roger Dixon is a Professor of Control Systems Engineering at the University of Birmingham's School of Engineering and a Visiting Professor at Loughborough University. His expertise spans applied control systems, mechatronics, condition monitoring, fault detection, and systems engineering. He holds a PhD from Lancaster University (1996) and has led major research initiatives in academia and industry, publishing over 150 papers. His work focuses on rail, road, aerospace, and energy sectors, emphasizing innovative solutions for industrial challenges. Education: BEng (Hons) Mechanical Engineering, Lancaster University (1992) MSc Mechatronics, Lancaster University (1993) PhD Control Systems Engineering, Lancaster University (1996) Research Interests: His research integrates advanced control strategies, fault detection, and condition monitoring. Key areas include: Rail infrastructure (e.g., fault-tolerant track switches) Aerospace actuator systems and fault-tolerant designs Energy systems (e.g., LNG platform monitoring) Model-based control and simulation Professional Highlights: Notable contributions include the 2013 REPOINT railway track switch concept and 2003 gas turbine fault-tolerant control patents. He chairs the UK Automatic Control Council (2017–2020) and has editorial roles in international journals. Awards: Fellow of the IMechE (2011) Fellow of the Higher Education Academy (2006) Chartered Engineer (2000) Lab & Collaborations: Leads the Control Systems theme at the Birmingham Centre for Railway Research & Education (BCRRE), focusing on rail innovation and multidisciplinary projects.

• Cybersecurity
• Machine Learning
• Network Security
• +6 more

Dr. Zhiyuan Tan is an Associate Professor in the School of Computing at Edinburgh Napier University (ENU), specializing in cybersecurity research. He holds a PhD in Computer Systems from the University of Technology Sydney (UTS), Australia (2014), an MEng from Beijing University of Technology, China (2008), and a BEng with high distinction from North-eastern University, China (2005). Before joining ENU in 2016, Dr. Tan held research positions at the University of Twente (Netherlands), University of Technology Sydney (Australia), and La Trobe University (Australia). Dr. Tan's research focuses on cybersecurity, machine learning, data analytics, virtualisation, and cyber-physical systems. His work has resulted in over 44 scholarly publications with an H-Index of 13 and more than 830 citations according to Google Scholar. His recent publications demonstrate a continued focus on network security, intrusion detection systems, and the application of machine learning techniques to cybersecurity challenges, with publications spanning from 2022-2025 in top venues including IEEE Transactions and international conferences. Dr. Tan has received significant research funding, including AUD 27,800 from CSIRO and UTS for autonomous network intrusion detection research and £6,987 from ENU for securing future 5G health care systems. His research has been recognized with awards including the National Research Award 2017 from the Research Council of the Sultanate of Oman, a Best Paper Award, and the Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award. National Research Award 2017 from the Research Council of the Sultanate of Oman Best Paper Award Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award Dr. Tan has mentored 9 PhD students over the past 5 years, with 6 successfully completing their studies. His students have produced 12 journal and 10 conference publications. He has also served as an editorial board member for international journals, organized special issues, and participated as a technical program committee member for major international conferences. Dr. Tan is currently recruiting PhD students for research projects on network security, adversarial machine learning for anomaly/malware detection, virtualization security, and IoT security.

Kanishka BhattacharyaView profile

Adjunct Associate Professor

• Machine Learning
• Artificial Intelligence
• Generative AI
• +7 more

Kanishka Bhattacharya is an Adjunct Associate Professor of Management Science and Operations at London Business School since 2019. He is also a Partner at Bain & Company, specializing in advising corporations on digital transformation, AI integration, and strategic innovation, particularly in Private Equity and Financial Services. His career spans academia, quantitative finance, and technology consulting. Education: BSc (India), MSc and DPhil (Oxford). Research interests focus on machine learning applications in business strategy, generative AI tools, and leveraging data science for organizational decision-making. He teaches core courses in statistics, machine learning, and leadership in digital innovation at London Business School. Professional experience includes developing high-frequency trading algorithms as a quant and creating AI-driven products for large enterprises. His cross-sector advisory work combines technical expertise with strategic business acumen.

• Machine Learning
• Data Analytics
• Cybersecurity
• +5 more

Dr. Priyanka Chaurasia is a Lecturer in Data Analytics at the School of Computing, Engineering and Intelligent Systems, Ulster University, based in the Derry~Londonderry campus. Her research focuses on machine learning applications in healthcare, cybersecurity, and assistive technologies. She contributes to interdisciplinary projects such as the EU-funded 'IT4Anxiety' mental health initiative and has expertise in AI-driven solutions for lead toxicity prediction, smart city security, and Metaverse privacy frameworks. Her academic background includes a strong emphasis on data science, with notable work in IoT security, medical image segmentation, and regulatory frameworks for large language models. She has collaborated on projects addressing technology adoption by older adults and individuals with dementia, emphasizing ethical AI and user-centric design. Dr. Chaurasia's recent publications span topics like deep learning for hate speech detection, privacy-preserving techniques in virtual environments, and computational modeling for environmental health risks. She maintains an active research profile, publishing in top-tier venues and co-authoring over 40 peer-reviewed articles since 2010. Her lab focuses on bridging theoretical advancements with practical healthcare and cybersecurity solutions.

• Multimodal Deep Learning
• Clinical Machine Learning
• Natural Language Processing
• +3 more

Dr. Ruchit Agrawal is an Assistant Professor of Computer Science and Head of Computer Science Outreach at the University of Birmingham Dubai. Previously, he served as a Postdoctoral Researcher in AI for Healthcare at the University of Oxford’s Computational Health Informatics Lab, and as a Marie Curie AI Researcher in the transnational MIP-Frontiers project at Queen Mary University of London. His work focuses on optimizing healthcare systems using Machine Learning, alongside contributions to Natural Language Processing, Audio Signal Processing, and Multimodal Deep Learning. He holds a PhD in Computer Science from Queen Mary University of London and an MS by Research from IIIT Hyderabad. Education: PhD in Computer Science (Queen Mary University of London, 2021) MS by Research in Machine Translation (IIIT Hyderabad, 2017) Research Interests: Clinical Machine Learning for healthcare system optimization Natural Language Processing with a focus on Indian languages and context-aware models Audio Signal Processing for music performance analysis and stuttering detection Development of multimodal deep learning frameworks for diverse applications Adaptive AI systems leveraging contextual and positional encoding techniques Publications highlight trends in healthcare AI, multilingual NLP, and audio-visual alignment. Recent work includes Arabic sentiment analysis, stuttering detection via MMSD-Net, and stock price prediction using FB-GAN. Earlier contributions address structure-aware synchronization in music performance data and transformer-based post-editing for low-resource languages. His research bridges theoretical advancements with practical implementations in clinical, financial, and cross-modal domains. Scientific awards include the prestigious Marie Skłodowska-Curie scholarship (2017–2020) supporting his deep learning research in audio signal processing. Advising and grants: While no formal advisees are listed, his roles involve leading outreach initiatives and guiding collaborative projects at the Computational Health Informatics Lab during his postdoctoral tenure. Labs/Teams: Active member of the Computational Health Informatics Lab (Oxford) and Machine Translation group at FBK (Italy). His work also intersects with the MIP-Frontiers transnational research project.

• Socio-Technical Software Engineering
• Developer Attitudes
• Automation in Software Engineering
• +3 more

Dr. Emily Winter is a Lecturer in Computing and Communications at Lancaster University, part of the School of Computing and Communications (SCC) since 2018. Her research focuses on socio-technical aspects of software engineering, including developer attitudes toward automation, human values in SE, and diversity in computing education. She holds a PhD in Sociology from Lancaster University, an MA in Sociological Research, and a BA in History. Emily has contributed to projects like the Fixie Project (investigating developer attitudes to automated tools) and the Values in Computing initiative. She is a Fellow of AdvanceHE and has received an ESRC 1+3 award for her postgraduate studies. Her external roles include serving as a Reviewer for Information and Software Technology , Workshop Chair for the International Workshop on Genetic Improvement (2022), and Program Committee member for several conferences. Emily teaches the SCC 130 module and actively engages in research addressing gender dynamics in CS education and ethical software design. Key research interests include qualitative methods in SE, developer-centric tool design, and fostering inclusive educational environments. Her work bridges social sciences and computing, emphasizing empirical studies of human-computer interaction in software development. Emily has published widely on topics like automatic program repair, SAST tool usage, and gender representation in CS. Her awards reflect her commitment to pedagogical innovation and social science contributions to computing. She is affiliated with the Security Lancaster research group and is based at InfoLab21.

• Information Systems
• Digital Innovation
• FinTech
• +3 more

Dr. Ruilin Zhu is a Lecturer in Information Systems at Lancaster University Management School (LUMS), Lancaster University, within the Department of Management Science. His academic profile demonstrates active engagement in research supervision, with current PhD students under his guidance. Prior to his academic career, Dr. Zhu gained industry experience in financial institutions and completed visiting scholar positions internationally. Research Focus: Dr. Zhu's work integrates behavioral, economic, and legal perspectives to examine digital innovation phenomena. His primary domains include: FinTech and InsurTech systems development Health informatics and technology adoption Risk communication frameworks Digital transformation in business ecosystems Socio-technical aspects of information systems This multidisciplinary approach reflects his industry background and international research exposure. Publication Analysis: Recent scholarly output (2022-2024) reveals strong focus on crisis communication during COVID-19, health technology integration, and FinTech innovation. Methodologically, Dr. Zhu employs longitudinal studies, bibliometric analysis, and structural modeling to examine technology adoption patterns across healthcare, education, and SME sectors. Emerging themes include algorithmic fairness, digital banking evolution, and privacy concerns in technology deployment. Research Projects: Pennies Each Day (2024-2025): Micro-savings mechanisms in developed economies Digital health accessibility for elderly populations in Chinese communities (2019) Electronic food facilitation systems for export optimization in Bangladesh (2018-2019) Academic Leadership: Dr. Zhu currently supervises PhD candidate Itoro Abraham in Information Systems research. He contributes to LUMS research groups including the Centre for Technological Futures and Information Systems research cluster, facilitating cross-disciplinary technology innovation.

Manuchehr SoleimaniView profile

Professor

• Electrical and Electromagnetic Tomography
• Medical Imaging
• Industrial Tomography
• +5 more

Professor Manuchehr Soleimani is a Professor in the Department of Electronic & Electrical Engineering at the University of Bath, leading the Engineering Tomography Lab (ETL) since 2011. He specializes in AI-driven tomographic imaging techniques, including Electrical/Electromagnetic Tomography, X-ray CT, and Ultrasound Tomography. His research focuses on medical imaging, industrial process monitoring, and robotic touch sensing. He collaborates with CERN on projects like the TIGRE open-source software platform and leads Bath's educational initiatives, including the MSc in AI for Engineering and Design and the Robotics Engineering undergraduate program. His research interests span inverse problems, machine learning integration, and multi-modality tomography. Key applications include lung monitoring, industrial quality control, and medical diagnostics. Professor Soleimani has secured funding from Innovate UK, EPSRC, EU Horizon 2020, and industry partners. He serves on the Scientific Advisory Panel at the University of Cambridge and has supervised 31 doctoral/master’s students. His lab develops cutting-edge imaging systems, such as contactless EIT sensors and ultrasonic tomography for in-vivo imaging. Key Projects: AdvanCT (EU), TIGRE (CERN collaboration), and industrial tomography systems for steel casting and CO₂ capture. Publications: Over 378 peer-reviewed articles, with recent focus on AI-enhanced reconstruction algorithms and multi-frequency imaging. Scientific contributions include pioneering work in capacitively coupled tomography and tactile sensing systems. His work aligns with UN SDGs through innovations in health and sustainable industry practices.

Dev GangjeeView profile

Professor

• Intellectual Property Law
• Branding and Trade Marks
• Geographical Indications
• +11 more

Dev Gangjee is Professor of Intellectual Property Law at the University of Oxford's Faculty of Law and a Fellow at St Hilda's College. He previously held roles at the London School of Economics and has been a Rhodes Scholar. His research emphasizes Intellectual Property (IP), with focus on Trade Marks, Geographical Indications (GIs), Copyright, and the historical/political economy of IP systems. He advises governments, law firms, and international organizations on IP issues. Education: National Law School of India University, Oxford University (Rhodes Scholar) Teaching: Private law areas including Contract, Land, and Tort Law Awards: Teaching Prize (LSE), Student Welfare Award (Oxford) Research interests include data rights, AI impacts on IP, and collective innovation. He is Director of the Oxford Intellectual Property Research Centre (OIPRC) and serves on editorial boards of the Modern Law Review and Queen Mary Journal of Intellectual Property . He holds visiting professorships at institutions including the Munich Intellectual Property Law Centre and has held fellowships globally.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Ioannis Maniadis Metaxas is a lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. He teaches undergraduate and postgraduate courses including Artificial Intelligence, Machine Learning, and Machine Learning for Visual Data Analysis. His research focuses on: Unsupervised representation learning Computer vision systems Large vision-language models Deep clustering techniques Generative models for data augmentation E-commerce personalization systems Recent publications demonstrate consistent focus on efficient learning methods for visual data, particularly investigating unsupervised pretraining, cluster optimization, and model fine-tuning approaches. His work shows progression from foundational 3D data augmentation to cutting-edge vision-language model optimization.