جست‌وجوی استاد و پژوهشگر

Alejandro Calixtoمشاهده پروفایل

پژوهشگر

• Integrated Pest Management
• Invasive Species
• Applied Entomology
• +۶ مورد دیگر

Alejandro Calixto serves as Director of Cornell University's Integrated Pest Management Program (Cornell IPM) within the College of Agriculture and Life Sciences. He leads interdisciplinary teams in entomology, plant pathology, and pesticide safety to develop ecologically resilient pest management solutions for agricultural and urban environments. His educational background includes: Ph.D. in Entomology from Texas A&M University (2005-2008) M.Sc. in Entomology from Texas A&M University (2002-2004) B.Sc. in Biology from Universidad de los Andes, Bogotá, Colombia (1991-1997) Calixto's research centers on invasive species ecology and management, particularly exotic insects in agricultural and urban settings. His work integrates molecular techniques, field experiments, and ecological modeling to develop sustainable IPM strategies. Current projects focus on making IPM accessible to marginalized communities and developing predictive decision-support systems using big data analytics to reduce unnecessary pesticide applications. His publication portfolio demonstrates consistent contributions to entomological science, with recent work emphasizing web-based decision tools and invasive species impacts. Key themes across his research include: Development of predictive agricultural models Ecological impacts of invasive ant species Integration of information technology in pest management Community ecology approaches to pest suppression Professional recognition includes: Dow AgroSciences Innovators Award (2016) Texas AgriLife Extension Superior Service Award (2011) Entomological Society of America Graduate Student Award (2007) Calixto actively engages in extension work through Cornell AgriTech, developing tools that help producers determine optimal pest management timing. His international experience spans projects in Brazil, Colombia, Mexico, and New Zealand. Though not formally teaching at Cornell, he annually instructs an applied entomology module in Colombia's field ecology program.

Scott A. Dellanaمشاهده پروفایل

استاد

• Operations Management
• Supply Chain Management
• Quality Management
• +۷ مورد دیگر

Scott A. Dellana is a Professor in the Department of Marketing and Supply Chain Management at the College of Business, East Carolina University. He has previously served as Interim Chair of his department and Chair of the Department of Decision Sciences, demonstrating strong leadership within the college. His research spans key areas in operations and supply chain management, with a strong emphasis on quality management, quality control in service and supply chain contexts, and mathematical modeling for forecasting and process analysis. His work bridges engineering and business disciplines, reflecting his dual background. The published research of Scott Dellana appears in top-tier journals such as International Journal of Operations & Production Management , Interfaces , Journal of Quality Technology , and Computers & Operations Research , indicating a sustained and impactful scholarly trajectory. His work often integrates quantitative methods with real-world operational challenges. Scott Dellana is a member of prestigious honor societies including Chi Epsilon, Tau Beta Pi, and Beta Gamma Sigma, reflecting excellence in both engineering and business. While no specific awards are listed, his publication record and leadership roles signify high professional recognition. He has advised academic initiatives and contributed to curriculum development through his administrative roles. Though specific grants are not mentioned, his research in applied operations suggests engagement with practical and industry-relevant problems. His prior experience in defense and manufacturing sectors likely informs collaborative and funded research directions. While no formal lab or research team is mentioned in the text, his focus on applied modeling and quality systems suggests potential involvement in interdisciplinary research centers or industry partnerships within the College of Business.

Franca Ruhwedelمشاهده پروفایل

استاد

• Finance
• Accounting
• Corporate Governance
• +۴ مورد دیگر

Prof. Dr. Franca Ruhwedel is a Professor of Finance & Accounting at the Rhine-Waal University of Applied Sciences in Kamp-Lintfort, Germany. She teaches courses in Financial Accounting , Management Accounting , Corporate Finance , and Corporate Governance within the International Business Administration (Bachelor) and International Management and Psychology (Master) programs. Prior to joining Rhine-Waal in 2013, she served as a Professor of Accounting and Controlling at FOM University of Applied Sciences in Essen (2007–2013). Her practical experience includes leadership roles in thyssenkrupp Group 's Corporate Mergers & Acquisitions department (2005–2007). Education : PhD in Business Administration from Ruhr-University Bochum (2003), with a dissertation titled Eigentümerstruktur und Unternehmenserfolg . Studied Business Administration at Westfälische Wilhelms-University Münster (1994–1999). Research Interests : Focuses on Corporate Governance , Audit Quality , Pro-Forma Earnings , and Strategic Controlling , with a strong emphasis on regulatory frameworks (e.g., ESMA guidelines ) and empirical analysis of financial practices. Publications Trends : Her work spans audit committee effectiveness, big data applications in finance, and the impact of regulatory changes on financial reporting. Key collaborations include studies with the German Stock Institute and analyses of DAX/MDAX companies. Scientific Awards : Ernst-Zander-Preis (Ruhr-University Bochum, 2004) DAI-Hochschulpreis (Frankfurt/M., 2004) Advising and Professional Roles : Supervises over 30 theses on topics like Behavioral Finance and Green Hydrogen Production . Holds supervisory board positions at United Internet AG , VTG AG , and Biofrontera AG (chair of audit committees).

Elizabeth Atkinsonمشاهده پروفایل

استادیار

• Statistical Genetics
• Population Genetics
• Genomic Analysis of Admixed Populations
• +۵ مورد دیگر

Elizabeth Atkinson is an Assistant Professor in the Department of Molecular and Human Genetics at Baylor College of Medicine, where she leads the Atkinson Lab. Her research focuses on developing statistical and computational tools to improve the study of the genetic basis of neuropsychiatric traits in admixed populations. She is affiliated with the Computational and Integrative Biomedical Research Center and the Jan and Dan Duncan Neurological Research Institute at Baylor. Dr. Atkinson's research spans three primary areas: (1) applied and analytic work in statistical genetics; (2) investigations into human population structure and evolutionary history; and (3) the development of scalable computational methods and tools for population-aware genetic analysis. Her work emphasizes neuropsychiatric traits with a particular focus on admixed American populations, though many of the tools she develops are broadly applicable across phenotypes and populations. She plays a leadership role in multiple international consortia, including the Psychiatric Genomics Consortium (PTSD working group), Neuropsychiatric Genetics in African Populations (NeuroGAP), and the Latin American Genomics Consortium (LAGC). These collaborations support the development of globally representative resources for gene discovery and offer trainees valuable experience working with large-scale, richly phenotyped cohorts. Her recent publications reveal a strong focus on improving genomic analysis in diverse populations, with particular emphasis on local ancestry inference, cross-ancestry genetic studies, and addressing disparities in genomics research across ancestries. She has developed methods like Tractor that use local ancestry to enable the inclusion of admixed individuals in GWAS. International meta-analysis of PTSD genome-wide association studies Genetic structure correlates with ethnolinguistic diversity in eastern and southern Africa Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS Low-coverage sequencing cost-effectively detects variation in underrepresented populations Dr. Atkinson mentors several trainees including postdoctoral associates, graduate students, and bioinformatics analysts. Her lab members work on diverse projects spanning statistical method development, population genetic analyses, and psychiatric genetics research. She is actively recruiting for postdoctoral positions to lead research projects in statistical and population genomics across diverse human populations.

Ahmad Abd Rabuhمشاهده پروفایل

مدرس ارشد

• Business Analytics
• Financial Technologies (FinTech)
• Digital Transformation
• +۱۲ مورد دیگر

Dr. Ahmad Abd Rabuh is a Senior Lecturer (Assistant Professor) in Business Analytics at Oxford Brookes Business School, Oxford Brookes University. He is a member of the Analytics, Information Systems and Operations (AISO) group and serves as subject coordinator for AISO, contributing to academic leadership and curriculum development. He is also affiliated with the Artificial Intelligence and Data Analysis Network (AIDAN) and is a fellow at the Oxford Regions, Innovation and Enterprise Lab (ORIEL). His educational background includes a PhD, MBA, and BSc, and he holds the status of Fellow of the Higher Education Academy (FHEA). His teaching spans undergraduate and postgraduate programs such as BSc in Accounting and Finance, MSc in Management and Business Analytics, and the Oxford Brookes MBA, covering modules like Business Intelligence, Data-Driven Decision Making, and Managing the Digital Business. Dr. Abd Rabuh’s research focuses on the transformative role of big data analytics, AI/ML, open banking, and distributed ledger technologies in FinTech and InsureTech. He explores how data science can enhance services in public, SME, and private sectors—including urban planning, financial inclusion, and healthcare. His work emphasizes open data and breaking platform silos to democratize access to financial services. The available publication highlights his interest in analyzing shifts in information systems research using text analytics. His recent research impact includes developing a deep learning model for parametric crop insurance in developing countries (pilot in Colombia) and analyzing traffic emissions in Bristol to identify urban bottlenecks. He is currently enhancing CRM software for SMEs with advanced analytics and machine learning capabilities. Fellow of the Higher Education Academy (FHEA) He supervises undergraduate, postgraduate, and MBA dissertations on topics like digital transformation in banking, ESG finance, and organizational design in defense firms. He has contributed to the validation of the MSc in Management and Business Analytics under the IDEAS framework. His consultancy work includes enhancing ProspectSoft CRM with analytics layers. There is no mention of external grants, but his Knowledge Transfer Partnership project indicates applied research collaboration. Dr. Abd Rabuh is actively involved in research centers such as ORIEL and professional networks including the Association of Information Systems (AIS) and the UK FinTech Academics Network (UK FAN). He regularly participates in key conferences like ECIS and Credit Scoring & Credit Control.

• Theoretical Computer Science
• Property Testing
• Approximation Algorithms
• +۸ مورد دیگر

Yuichi Yoshida is a Professor at the National Institute of Informatics (NII), affiliated with the Principles of Informatics Research Division. He also holds a concurrent position as Senior Researcher at Preferred Networks. His academic career at NII spans from Assistant Professor (2012–2015), Associate Professor (2015–2022), to full Professor since 2022. He serves as Vice Director of the Global Research Center for Big Data Mathematics at NII and has held advisory and research roles at Preferred Infrastructure and the Ministry of Education, Culture, Sports, Science and Technology (MEXT). Ph.D. in Informatics, Kyoto University, 2012 Master of Informatics, Kyoto University, 2009 Bachelor of Engineering, Kyoto University, 2007 His research focuses on theoretical computer science, particularly property testing , approximation algorithms , sublinear-time algorithms , and constraint satisfaction problems . He investigates how theoretical insights can be applied to real-world graphs, with recent emphasis on average sensitivity of algorithms and Lipschitz continuity in combinatorial optimization . His work bridges theory and practical scalability in large network analysis and machine learning. The most recent 15 publications (2022–2025) demonstrate a strong trend in algorithmic stability, spectral methods for hypergraphs, influence propagation in networks, and learning-augmented algorithms. Key venues include FOCS, SODA, ICALP, and NeurIPS, reflecting high impact in theoretical computer science and machine learning. Topics such as Lipschitz continuity, average sensitivity, spectral sparsification, and sublinear algorithms recur, indicating a cohesive research program on robust and efficient computation. Best Paper Award, AISTATS (2018) MEXT Commendation for Science and Technology – Young Scientists’ Prize (2017) Inoue Research Award for Young Scientists (2014) KDDI Foundation Award (2024) Funai Information Technology Award (2024) Yuichi Yoshida advises PhD students and hosts numerous postdocs, RAs, and international interns. He leads major research projects funded by JSPS and JST, including Grants-in-Aid for Scientific Research (S) and PRESTO programs. His service includes program committee roles for ICML, NeurIPS, SODA, and ICALP. He has co-authored a book on Property Testing and contributed to encyclopedias on big data technologies. He leads research teams on algorithm desensitization and large-scale graph algorithms, and has been program co-chair for GRADES-NDA'23. His lab fosters international collaboration, hosting interns from top universities worldwide.

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Can Wang is a Senior Lecturer in Computer Science at Griffith University's School of Information and Communication Technology on the Gold Coast Campus, Australia. She has held this position since January 2020, having previously served as a Lecturer at the same institution from May 2017. Dr. Wang is a member of the Institute for Integrated and Intelligent Systems (2018-2024) and has extensive academic experience including a Postdoctoral Fellowship at Data61, CSIRO (2014-2016) and a Research Associate position at the University of Technology Sydney (2013). PhD in Computer Science from University of Technology Sydney, Australia (2009-2013) Master of Science from Wuhan University, China (2007-2009) Bachelor of Science from Wuhan University, China (2003-2007) Dr. Wang's research focuses on designing models and algorithms for heterogeneous and interdependent behavioral data to make predictions and recommendations. Her work aims to propose new frameworks and theories in data mining and machine learning while applying AI and big data analytics to multidisciplinary domains including social media, tourism, drug discovery, hospital admission, and medical mining. She has published over 80 high-quality publications with real applications and deployments. Her recent publications (2023-2025) demonstrate a strong focus on heterogeneous network analysis, recommendation systems (particularly point-of-interest recommendations), fake news detection, and the integration of Large Language Models with traditional recommendation approaches. Her work spans both theoretical advancements in machine learning algorithms and practical applications across healthcare, social media, and tourism domains, showing her commitment to bridging the gap between academic research and real-world problems. Dr. Wang actively supervises doctoral students, serving as Principal Supervisor for projects related to Large Language Models analysis, multi-media data summarization, and point-of-interest recommendation systems. She also serves as PROGRAM DIRECTOR for the Graduate Certificate in Data Science, Master of Data Science, and Master of Information Technology programs, and as PROGRAM ADVISOR for multiple undergraduate and graduate programs including various Bachelor of Science combinations and Cyber Security certificates. Her funded research includes significant projects such as the NSF-CSIRO collaboration on 'Towards Interpretable and Responsible Graph Modeling for Dynamic Systems' (2024-2027), multiple Griffith Sciences Advancing Women's Research Success Grants (2021-2024), and projects focused on patient readmission prediction, social media analysis during pandemics, and tourism market analysis in Queensland.

Sidharth Kumarمشاهده پروفایل

دانشیار

• High-performance computing
• Data visualization
• Scalable algorithms
• +۷ مورد دیگر

Sidharth Kumar is an Associate Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC), where he leads research in high-performance computing and data visualization. He joined UIC in August 2023 after previously working at the University of Alabama at Birmingham. His research focuses on developing scalable algorithms and data structures for data-intensive applications, intersecting HPC, visualization, databases, and machine learning. Education: Ph.D. in Computing (2016) from the University of Utah's Scientific Computing and Imaging Institute, advised by Valerio Pascucci. Bachelor of Technology in Information and Communication Technology (2009) from DAIICT, Gandhinagar, India. Research Interests: Dr. Kumar's work centers on parallel I/O, GPU acceleration, big data processing, and scientific visualization. His projects include: 1) Exascale data management systems, 2) GPU-accelerated web visualization, 3) Declarative analytics frameworks, and 4) Topology-driven analysis for neuroscience and virology. He develops solutions for memory-constrained environments and heterogeneous systems. Publication Trends: Recent works (2023-2025) demonstrate strong focus on GPU-accelerated databases (Datalog optimizations), parallel communication algorithms (all-to-all collectives), memory-efficient visualization techniques (speculative raycasting), and applied topological analysis (brain networks, virus taxonomy). His publications consistently appear in top-tier HPC and visualization venues. Awards & Honors: Best Paper Awards: IEEE HiPC (2019), ISC Hans Meuer (2020), LDAV (2023) Honorable Mention: PacificVis (2025) Poster Awards: SC23 Finalist, HiPC SRS (2021) NSF EPSCoR Research Fellow (2022) Grants & Advising: NSF PPoSS Large: Declarative Analytics ($960K PI) NSF SHF: Scalable I/O Runtime ($300K PI) NSF EPSCoR: Relational Algebra ($265K PI) Advises 6 PhD students in HPC and visualization research Lab & Service: Leads a research team working on exascale computing challenges. Serves on technical committees for SC, ISC, IPDPS, and HiPC conferences. Teaches courses in Database Systems, Algorithms, and Data Visualization.

Valentina Rotondiمشاهده پروفایل

استاد

• Behavioral Economics
• Demography
• Gender Inequalities
• +۷ مورد دیگر

Valentina Rotondi is a Professor at the Department of Business Economics, Health and Social Care (DEASS) at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). She holds affiliated research positions at the Swiss School of Public Health, Milan Center for Neurosciences (NEUROMI), and the University of Oxford's Department of Sociology and Nuffield College. Her work bridges behavioral economics, demography, and public health, with a focus on gender inequalities, digital technologies, and socioeconomic impacts of crises. PhD in Economics from Catholic University of Milan (2016) MSc in Political Economy and International Institutions from University of Pavia (2011) BA in International Cooperation and Development from University of Pavia (2009) Rotondi's research explores: Digital Transformation: Impact of digital technologies on health, well-being, and gender equality Demographic Dynamics: Parenthood's effects on productivity and social capital Trust Mechanisms: Evolution of interpersonal trust and trust in science during crises Behavioral Insights: Using experimental and computational methods to study cooperation Her recent publications analyze cooperation in post-pandemic contexts, mobile technology's role in development, and maternal brain changes. Methodologically, she combines behavioral experiments, neuro-economics, and big data analytics. Rotondi also teaches 'Unusual Data Analysis' and co-directs the 'Integral Economics' Certificate program at SUPSI.

• Educational Technology
• Data Mining in Education
• Artificial Intelligence in Education
• +۳ مورد دیگر

Hakan GÜLDAL is an Assistant Professor at the Faculty of Education, Trakya University, with a research focus on educational technology and data mining applications in education. He holds degrees in Computer Engineering (BSc, MSc, PhD) and teaches courses in Database Management Systems and Programming Languages . BSc, MSc, PhD in Computer Engineering from Trakya University Assistant Professor since 2018 Specializes in machine learning for educational analytics His research explores technology acceptance models, cloud-based learning systems, and classification algorithms applied to educational datasets. Recent work examines chatbots as educational tools and predictive modeling of student performance. Publications span 2010-2024, with a focus on Cloud computing in education Machine learning for student assessment Learning management system analysis He contributes to international conferences and journals in educational technology. Teaching includes foundational courses in database systems and programming languages, reflecting his technical background.

Zhengwen Huangمشاهده پروفایل

مدرس ارشد

• Evolutionary Algorithms
• Gene Expression Programming
• Machine Learning
• +۷ مورد دیگر

Dr Zhengwen Huang serves as Senior Lecturer in Digital Systems and Microprocessors within the Department of Electronic and Electrical Engineering at Brunel University London's College of Engineering, Design and Physical Sciences. His research focuses on evolutionary computation methodologies applied to complex engineering systems. His core research expertise includes: Evolutionary Algorithms and Gene Expression Programming Data Engineering for Big Data Analytics Systems Modelling and Optimization in Smart Grid Applications High-Performance Computing Implementations Recent publications demonstrate consistent application of gene expression programming to data-driven system engineering (2017-2019) with interdisciplinary extensions into low-power circuit design and biomedical signal processing (2020). Dr Huang actively supervises PhD research on CNN integration with Gene Expression Programming, Schema Theory applications, AI solutions for smart grids, and digital twin development for power systems. His work is supported by projects including urban bio-waste conversion into sustainable materials and zero-defect manufacturing strategies. He is affiliated with the IMM research group and maintains collaborative relationships with Dr Alireza Mousavi and Prof Maozhen Li, as evidenced by co-authored publications in IEEE Transactions.

Julia Gluesingمشاهده پروفایل

استاد پژوهشی

• Business and Organizational Anthropology
• Global Collaboration and Networks
• Innovation Diffusion
• +۵ مورد دیگر

Julia Gluesing is a business and organizational anthropologist who serves as Research Professor in the Department of Industrial and Systems Engineering at Wayne State University and as Adjunct Faculty in the Global Executive Track Ph.D. Program. She is also Adjunct Professor of Anthropology and has held leadership roles such as Associate Director of the Institute for Information Technology and Culture (IITC) and Co-Director of the Global Executive Track Ph.D. in Industrial and Systems Engineering. Education: Ph.D. in Cultural Anthropology, Wayne State University, 1995 M.A. in Communication, Michigan State University, 1985 B.A. in French and Russian, University of California, Davis, 1971 (Phi Beta Kappa) Diplôme de Français Avancé, Université de Montpelier, France Research Interests: Her scholarship centers on culture and identity, global collaboration and networks, innovation diffusion, and qualitative methodology. She integrates insights from anthropology, international business, economics, finance, and political science to understand how engineering organizations operate across cultures and geographies. Publication Trends: Across more than 60 peer-reviewed works, Julia’s research has evolved from foundational ethnographic studies of global virtual teams to cutting-edge analytics combining text mining, social network analysis, and anthropological theory. Recent themes include innovation governance, environmental decision-making, and ethical challenges in globally networked organizations. Awards & Honors: Best Paper Award, International Management Division, Academy of Management (2010) Most Innovative Session Award, Organizational Behavior Division, Academy of Management (2000) Election to Phi Kappa Phi and Phi Beta Kappa honor societies Grants & Sponsored Research: Julia has served as Principal Investigator or Co-PI on six NSF grants totaling well over $2 million, including the flagship “Digital Diffusion Dashboard” project ($426k, 2005–2010) and the Michigan Alliance for Clinical and Translational Science evaluation ($1.8 million segment; total $20 million). Industry partners have included Ford, GM, Chrysler, Visteon, Motorola, Procter & Gamble, Bosch, and many others. Teaching & Advising: She teaches doctoral courses such as Qualitative Research Design and Methods, Global Leadership of Technical Organizations, and Management of Technology Change, and advises leadership projects in the Ford Engineering Management Master’s Program (EMMP). While specific advisee names are not listed, she mentors executives in both the Global Executive Track Ph.D. and EMMP cohorts. Labs & Teams: Julia founded Cultural Connections, Inc. , a global research and consulting firm, and co-founded the Collaborative Innovation Networks (COINs) Conference series, serving as local organizer for COINsDetroit 2017. She maintains active affiliations with the American Anthropological Association, EPIC, ION, and the Society for Applied Anthropology.

• Economic Statistics
• Energy Economics
• Electricity Markets
• +۵ مورد دیگر

Luigi Grossi is currently Associate Professor of Economic Statistics at the University of Verona , Department of Systems Engineering and Industrial Technologies (DISTI). He has previously served as Researcher in Economic Statistics at the University of Parma and has held visiting positions at Penn State University and the University of Warwick . His educational background includes a Degree in Economics and Business (110 cum laude) from the University of Parma (1992) and a PhD in Statistics from the University of Bologna (1997). His research spans Economic Statistics , Energy Economics , Electricity Markets , Risk Analysis , Data Mining , and Applied Econometrics . He has contributed extensively to the understanding of electricity price dynamics, environmental risk factors, and statistical modeling in energy markets. His work often involves advanced econometric and machine learning techniques applied to real-world energy and economic data. He has taught a wide range of courses including Statistics , Business Statistics , Economic Statistics , Statistics for Financial Markets , Data Analysis , Big Data and Data Analysis , and Data Mining across undergraduate, graduate, and PhD levels at universities including Parma, Bologna, Verona, and Piacenza. He has participated in several national and European research projects, including the CARERA project on CAP reform impacts on rural employment. He also served as a scientific consultant for the European Commission’s Joint Research Center and has been a referee for multiple high-impact statistical and environmental journals. Administratively, he has held roles such as Deputy Director of the Economics Faculty Library at Parma and member of PhD program scientific committees at both Bologna and Verona.

Adrian Geppمشاهده پروفایل

استاد

• Business Failure Prediction
• Fraud Detection
• Big Data
• +۳ مورد دیگر

Adrian Gepp is a Professor at the Bond Business School and a member of the Centre for Data Analytics at Bond University in Australia. His research focuses on applying statistical modeling and data analytics to economic and social challenges, including fraud detection, fintech, health analytics, and business failure prediction. Gepp has published extensively in peer-reviewed journals and conferences, with recent work spanning deep learning in portfolio management, ESG investing, and combating financial crime through AI. Research Interests : Big data, predictive modeling, financial statement fraud, car insurance fraud, quantum algorithms, and cross-border economic models. Professional Affiliations : The Actuaries Institute, American Accounting Association, and Association of Certified Fraud Examiners. Article Trends : His 2025 publications emphasize machine learning applications in finance, fraud analytics, and interdisciplinary data-driven solutions. Earlier works (2024) explore tourism forecasting and research methodology frameworks. Media & Activities : Regularly featured in media discussions on fraud detection, big data, and financial technology. Serves on editorial boards for journals like Accounting and Finance and Journal of Accounting Literature .

• Health Informatics
• Data Science
• e-Health
• +۵ مورد دیگر

Professor David Ford is a Professor of Informatics and Director of Population Data Science at Swansea University's School of Medicine. He has secured over £70 million in research funding, including roles as Principal Researcher for Administrative Data Research Wales (£7.2m ESRC award), Principal Investigator of the UK Multiple Sclerosis Register, Joint PI of the Family Justice Data Partnership, Deputy Director of HDR UK in Wales/Northern Ireland, and Chief Data Officer for the BREATHE Respiratory Health Research Hub. His research focuses on Health Informatics , Population Data Science , Privacy-Preserving Analytics , and e-Health Infrastructure . Key interests include secure data reuse, ethical governance frameworks, and applying big data to public health challenges like multiple sclerosis, COVID-19, and family justice systems. Ford's publications demonstrate a strong emphasis on health data linkage methodologies , real-world evidence generation , and applied epidemiology . Recent work explores COVID-19 vaccine safety/surveillance, genomic data integration, and mental health in vulnerable populations. His studies consistently leverage large-scale administrative datasets and emphasize ethical data stewardship. He co-founded and directs the SAIL Databank and SeRP (Secure e-Research Platform) , globally recognized infrastructures enabling secure curation and analysis of sensitive health data across research collaborations. His grants support critical national initiatives including the UK MS Register and Family Justice Data Partnership.