Muhammad Waqas is a researcher affiliated with COMSATS University Islamabad , where he holds a position in the Department of Meteorology under the School of Applied Sciences and Humanities . His academic collaborations span institutions like Bahria University, National University of Technology, and University of Bahrain, indicating a multidisciplinary approach. Research interests include Mechanisms for integrating fuzzy logic and machine learning in health monitoring Application of deep learning to medical imaging and clinical diagnostics Development of smart sensors for wearable technology in biomechanics Analysis of social media data for public health surveillance and sentiment analysis Investigation of digital citizenship and ICT leadership in educational contexts Trends in his 15 most recent publications (2025-2024) reveal a focus on medical diagnostics (e.g., monkeypox, breast cancer), smart infrastructure (e.g., sensor placement, structural health monitoring), and social media analytics for health and behavioral insights. These works leverage machine learning , fuzzy systems , and multi-objective optimization .
Dr. Joris Demmers is an Associate Professor and Head of the Marketing Department at the Faculty of Economics and Business , University of Amsterdam. His research focuses on consumer behavior, privacy in digital environments, environmental sustainability in marketing strategies, and the intersection of technology with consumer engagement. He leads academic initiatives in marketing while exploring interdisciplinary topics like corporate greenwashing detection and data governance. Key Research Themes: Consumer preferences for eco-labels and corporate social responsibility Privacy risks vs. behavioral incentives in financial and social media contexts Visual content analysis and machine learning applications in marketing Data-sharing dynamics and ethical marketing practices Recent Trends in Publications: Recent work emphasizes greenwashing detection (e.g., GreenScreen dataset), psychological drivers of financial self-disclosure, and the role of digital tools in customer journeys. Earlier studies delve into consumer data-sharing motivations and ethical privacy frameworks. Awards and Activities: No awards explicitly mentioned. Active in teaching and ancillary academic activities related to marketing strategy and digital media. Lab/Team Affiliations: No specific labs or teams listed, but collaborates across disciplines, including molecular biology studies on aging mechanisms (possibly through interdisciplinary projects).
David Frazier is a Professor in the Department of Econometrics & Business Statistics at Monash University, specializing in simulation-based inference, financial econometrics, and nonparametric/semiparametric modeling. He teaches ETC 1010: Data Modeling and Computing. His research focuses on robust statistical methods, Bayesian computation, and model misspecification. Key projects include 'Consequences of Model Misspecification in Approximate Bayesian Computation' (2020-2025) and 'Loss-based Bayesian Prediction' (2020-2025). Recent work addresses forecasting in misspecified models, weak identification in econometric frameworks, and robust variational Bayes techniques. His contributions align with UN Sustainable Development Goals related to economic and environmental sustainability. Projects: 4 active/funded projects with ARC, Brown University, and international collaborators. Publications: Over 37 peer-reviewed articles in journals like the Journal of the American Statistical Association and Journal of Econometrics. Research interests include advancing Bayesian methodologies for complex models, with applications in asset pricing and economic forecasting. His work emphasizes reliability in statistical inference under model uncertainty and computational efficiency.
Giuseppe Colangelo is an Associate Professor of Law and Economics at the University of Basilicata and holds adjunct roles at LUISS Guido Carli (Rome, Italy) and the Transatlantic Technology Law Forum (TTLF). He specializes in innovation policy, competition law, and economic analysis of legal frameworks. His research bridges EU and U.S. antitrust approaches, focusing on digital markets, data governance, and transnational litigation. Education includes a Law degree from LUISS Guido Carli, an LL.M. in Competition Law from Erasmus University Rotterdam, and a Ph.D. in Law and Economics from LUISS. He has been a TTLF Affiliate since 2017. Key research interests include AI-driven collusion, antitrust implications of data accumulation, and interoperability policies. Over 15 publications analyze topics like anti-suit injunctions in SEP litigation, EU-US regulatory divergence in digital platforms, and privacy-antitrust intersections. His work emphasizes transatlantic comparative law and policy recommendations for addressing market power in tech sectors.
Bas Donkers is a full Professor of Marketing Research at the Department of Business Economics within Erasmus School of Economics (ESE), Erasmus University Rotterdam. Affiliated with ERIM (Erasmus Research Institute of Management) since 2000, he holds a prominent position in the field of consumer behavior and marketing analytics. His research examines consumer decision-making from a behavioral perspective, building on advanced market research and machine learning techniques to generate groundbreaking insights. His research interests center on consumer behavior , choice modeling , and marketing analytics , with significant contributions to healthcare decision-making and financial investment contexts. Donkers has published extensively in leading journals including Journal of Marketing Research, Marketing Science, and Journal of the Academy of Marketing Science. His recent work demonstrates a clear trajectory toward integrating machine learning with traditional choice modeling, particularly in healthcare applications (35% of recent publications) and digital consumer behavior (25%), with growing emphasis on AI-driven decision support systems. ERIM Top Article Junior Award (2017) ERIM postdoc fellowship (2002) Donkers has supervised 13 PhD candidates to completion, serving as promotor or co-promotor on diverse topics spanning retirement planning, charitable giving, healthcare choice modeling, and digital marketing analytics. His research has been supported through ERIM frameworks and collaborative projects with healthcare institutions. He actively coordinates academic events including the Invitational Choice Symposium and regularly presents at specialized research seminars. As a core member of ERIM's Marketing Group, Donkers contributes to the institute's research infrastructure focused on behavioral decision modeling and choice experimentation. His work bridges theoretical marketing research with practical applications in healthcare policy and financial services, maintaining strong connections with industry partners through ERIM's business engagement initiatives.
Andrea Maurino is a Full Professor at the University of Milano-Bicocca and leads the Insid&s LAB. His research focuses on data quality, knowledge graphs, machine learning, and their applications in healthcare, finance, urban planning, and organizational analysis. He explores cutting-edge techniques like Large Language Models (LLMs) for decision support systems and semantic annotation of tabular data. Key research interests include improving data quality frameworks for large RDF datasets, developing enterprise knowledge graphs for organizational insights, and applying AI to social media analysis and hate speech detection. His work bridges theoretical advancements with real-world applications such as smart city mobility prediction and nutritional strategies for healthy aging. Notable contributions include scalable tools like ABSTAT-HD for knowledge graph profiling and the 3d-clost mobility prediction model. Maurino’s interdisciplinary approach integrates data science with fields like psychology (ICD-11 decision support) and environmental science (ESG activity detection in financial texts). His lab collaborates on projects like Food NET, combining nutrition science with social network analysis. While no formal awards are listed here, his prolific publication record reflects sustained innovation in data-driven methodologies.
Professor Stephen Roberts holds the Royal Academy of Engineering / Man Group Chair in Machine Learning at the University of Oxford. He is affiliated with the Oxford-Man Institute and Somerville College. With a DPhil in machine learning and a physics background, his research spans environmental science, financial systems, and geophysics. He co-leads the Machine Learning Research Group and directs the EPSRC Centre for Doctoral Training in Autonomous, Intelligent Machines and Systems (AIMS). His academic journey includes prior faculty roles at Imperial College London before joining Oxford in 1999. Key research interests include tidal analysis using AI, climate modeling, geospatial data interpretation, and financial algorithm design. He has pioneered tools like RTide for coastal flooding prediction and developed machine learning frameworks for environmental and economic applications. Education: DPhil in Machine Learning, Physics undergraduate degree Affiliations: Oxford-Man Institute, Somerville College, EPSRC AIMS CDT Key Projects: SWOT mission data corrections, Antarctic bedrock mapping, carbon footprint reduction in ML His work bridges disciplines, applying ML to solve complex problems in climate science, finance, and geology. Awards include Fellowship of the Royal Academy of Engineering and IET. Current focus areas include improving climate model accuracy and fostering interdisciplinary training through the AIMS program.
Dr. Jeff Jacob is a Professor of Economics at Bethel University, affiliated with the College of Arts and Sciences and the College of Adult and Professional Studies. His academic career began at Bethel in 2007, following roles at leading Indian economic policy think tanks focusing on international trade. He holds degrees from St. Stephen's College, Delhi School of Economics, and Southern Methodist University (SMU), including a Ph.D. in Economics (2006). Teaching focuses on economics and business analytics across undergraduate, adult, and MBA programs, covering courses like Data Mining, Econometrics, and Managerial Economics. His pedagogy emphasizes critical thinking, real-world problem-solving, and ethical considerations rooted in Christian faith principles. Research interests span applied econometrics, economic development, business analytics, and the economics of religion. Notable studies include analyses of democracy-growth linkages, social networks’ impact on homeownership, and the role of institutions in trade and geography. His work often employs panel data methods to explore complex socioeconomic questions. Publications include high-impact papers on topics like corruption reforms, drug policy effects, and sexual regulation trends. He has received grants for course development and academic initiatives, and maintains membership in Omicron Delta Epsilon. Presentations at conferences like APEE and the Southern Economic Association highlight his contributions to public choice theory and development economics.
Ming Yuan is a Professor in the Department of Statistics at Columbia University and serves as Associate Director of the Data Science Institute. His research focuses on high-dimensional statistics, machine learning, and statistical methodology with applications in genomics, finance, and imaging. Yuan holds a Ph.D. in Statistics from the University of Wisconsin-Madison (2004) and a B.S. in Electrical Engineering from the University of Science and Technology of China (1997). Education: 2004 Ph.D., Statistics, University of Wisconsin-Madison 2003 M.S., Computer Science, University of Wisconsin-Madison 2000 M.S., Probability and Statistics, University of Science and Technology of China 1997 B.S., Electrical Engineering, University of Science and Technology of China Research Interests: Dr. Yuan’s work bridges theoretical and applied statistics, emphasizing scalable methods for high-dimensional data. Key areas include tensor decomposition, covariance estimation, and statistical machine learning. His contributions to methods like sparse inverse covariance estimation and matrix/tensor completion have found applications in finance, genomics, and image analysis. Publications: His recent work explores tensor-based methods for high-dimensional analysis and develops optimal algorithms for compressed sensing. Articles often address statistical theory and computational challenges in modern data science, reflecting a balance between foundational and applied research. Awards: 2025 JASA Theory & Method Invited Discussion Paper 2024 William F. Sharpe Award (JFQA) 2018 Medallion Lecturer (Institute of Mathematical Statistics) 2014 Guy Medal in Bronze (Royal Statistical Society) 2007 Leo Breiman Junior Award Professional Activities: Yuan has served as Co-Editor of The Annals of Statistics (2019–2021) and Program Secretary for the Institute of Mathematical Statistics (2018–2021). His work integrates interdisciplinary collaborations, particularly in biomedical imaging and financial econometrics.
Mondher Feki is an Associate Professor of Management at Université Paris-Saclay, focusing on IS/IT management and digital transformation. His research explores blockchain applications in supply chains, robotic process automation (RPA), robo-advisors, and generative AI. He teaches Management Information Systems and Data Management courses. Currently serving as a reviewer for journals like Business & Information Systems Engineering and conferences like HICSS and ECIS. Active member of the Association Information & Management (AIM). Key research areas include digital transformation strategies, blockchain's operational impact, AI-driven financial advisory systems, and RPA implementation in insurance sectors. His work emphasizes technology adoption challenges and strategic alignment in business contexts. Recent publications analyze blockchain use in French retail logistics, RPA benefits for Allianz France, and IS quality's strategic role in firm performance. Conference contributions address robo-advisors in crowdfunding and pandemic-driven higher education innovation. Maintains active academic service roles in peer review and conference organization.
Professor Bartosz Witkowski is a faculty member at the Warsaw School of Economics (SGH), serving as Rector’s Representative for the SGH Doctoral School and Director of the Institute of Econometrics. He holds a PhD in quantitative methods and focuses on econometrics, particularly panel data and applied econometrics. His research explores financial systems, banking regulation, economic growth, and education quality's impact on development. Collaborations include work with the National Bank of Poland, World Bank, and government ministries. His research interests include applied econometrics , panel data analysis , and financial stability mechanisms , with a focus on European and emerging markets. Recent studies address banking sector dynamics, crisis impacts on financial systems, and the role of education in economic catch-up processes. He has authored/co-authored over 20 articles in high-impact journals, focusing on topics like banking regulation, economic convergence, and policy effectiveness. His teaching includes courses on quantitative methods for institutions such as the World Bank. Led the Institute of Econometrics, managing research and training programs. No specific grants or awards mentioned, though his work underscores practical applications of econometric models in policy and industry.
Ka Ho Chow is an Assistant Professor in the Department of Computer Science at the University of Hong Kong, part of the School of Computing and Data Science. He holds a PhD from Georgia Institute of Technology and was previously a research scientist at IBM Research. His research focuses on the intersection of machine learning, cybersecurity, and scalable systems, emphasizing trustworthy AI and defense against security/privacy threats in federated learning, large language models, and visual recognition systems. Key achievements include IBM PhD Fellowship (2022) and Croucher Scholarship (2021). Education: PhD in Computer Science from Georgia Tech (2020), advised by Prof. Ling Liu. His work spans algorithmic optimization, infrastructure resilience, and adversarial machine learning. Current research explores attack-resilient solutions for centralized/federated learning and AI system vulnerabilities. Recent articles highlight innovations in federated learning security, gradient inversion attacks, backdoor detection, and privacy-preserving techniques. He has openings for PhD students interested in AI security and trustworthy systems. His lab collaborates on projects involving blockchain fraud detection (ZipZap), facial recognition privacy (Personalized Masks), and graph neural network robustness. Awards: IBM PhD Fellowship (2022), Croucher Scholarship (2021). Active in guiding PhD candidates and advising on microservices cloud migration (Atlas/SCAD systems). Research outputs include over 30 peer-reviewed papers spanning cybersecurity, AI ethics, and distributed learning frameworks.
Reddi KOTHA is the Lee Kong Chian Professor of Strategy & Entrepreneurship at the Lee Kong Chian School of Business, Singapore Management University (SMU). He holds roles as PGR Coordinator for Strategy & Entrepreneurship and Innovation and Entrepreneurship Research Peak Lead. His academic journey includes a Ph.D. in Entrepreneurship from London Business School (2007) and an MBA from Babson College (2002). KOTHA’s research focuses on entrepreneurship, technology innovation, and strategic management, with notable contributions to understanding family firms, entrepreneurial training, and technology commercialization. He has been recognized with awards such as the Best Paper in Family Business Award (2006) and the Best Contribution by a Young Scholar (2008–2009) . His grants include significant funding for studies on startup scaling, innovation in Chinese firms, and venture capital research. KOTHA’s work bridges academic and practical insights, as seen in collaborations with organizations like RWDC Industries and SensorFlow. His research themes emphasize strategic innovation, entrepreneurial ecosystems, and the interplay between kinship networks and firm performance. He has contributed to high-impact journals like the Strategic Management Journal and Academy of Management Journal , with recent focus on post-financial crisis multinational strategies and the impact of managerial training on venture growth.
Dr. Mathieu Mercadier is an Assistant Professor of Business Analytics at Dublin City University Business School, Ireland. He serves as Programme Chair for the MSc and Graduate Diploma in Business Analytics. His research focuses on Business Analytics, Machine Learning, Financial Risk Management, and Sustainable Finance. Education: PhD in Economics from Université de Limoges, France, specializing in Machine Learning applied to Banking and Finance. He has ten years of industry experience as a Market Finance Consultant. Research interests include applying Machine Learning to banking risk, sustainable finance, and quantitative finance. His work spans statistical modeling for financial stability, algorithmic risk assessment, and ESG fund evaluation. Key trends in his articles involve quantum-enhanced machine learning for stock forecasting, systemic risk measurement, and pandemic impact analysis. No scientific awards listed. Advising and grants: No formal grants or student advisees mentioned. Active in curriculum development for business analytics programs. Engaged in international conferences and seminars. Labs/Teams: Not explicitly stated in provided data.
Iain Clacher is Professor of Pensions & Finance and Pro Dean for International at Leeds University Business School, University of Leeds. He is also the Director of the University-wide Centre for Financial Technology and Innovation, fostering interdisciplinary research in fintech, AI, and blockchain applications in finance. His work bridges academia, policy, and industry, with significant impact on pension governance, transparency, and sustainable finance. His research focuses on pensions, retirement decision-making, trustee governance, asset management costs, sustainable finance, and financial technology . He explores behavioural aspects of annuitization, discount rates in accounting, smart ledgers for collective savings, and AI in financial services. His work emphasizes real-world relevance, influencing regulatory standards and institutional practices. The trajectory of his recent publications reflects a strong shift toward interdisciplinary innovation , integrating finance with technology, sustainability, and policy. His research combines empirical finance with institutional analysis, covering topics from split-voting in equity funds to climate risk analytics in pension portfolios. The consistent themes include transparency, governance, and the application of emerging technologies to improve financial outcomes. Selected as a REF 2021 impact case study by Leeds University Business School Board member of the Institutional Disclosure Working Group (IDWG) of the Financial Conduct Authority (2018) Expert advisor to the European Economic and Social Committee on Pan European Pension Products Iain has advised major institutions such as the CERN Pension Fund, the Financial Conduct Authority, the Office for National Statistics, and the UN Principles for Responsible Investment. He has led high-impact projects funded by national and international bodies, including the £10m UK Centre for Greening Finance and Investment. His leadership in the Centre for Financial Technology and Innovation has secured industry partnerships with Barclays and Simudyne, and he has developed innovative educational initiatives like the Fraud Wars hackathon. He leads one of two climate and environmental risk analytics hubs under the UK Centre for Greening Finance and Investment, based in Leeds and London. The Centre for Financial Technology and Innovation brings together experts from computing, law, engineering, ethics, and business to advance research in fintech, with active projects in smart ledgers, AI in audit, and analytics in asset management.
