Prof. Sergio Ginebri is an Associate Professor at the Department of Jurisprudence, University of Rome Tre. He holds a PhD in Economics from Sapienza University of Rome and has extensive experience in fiscal policy, pension systems, and public finance research. His work focuses on topics like pension sustainability, social inequality, and the political economy of public debt. Education: PhD in Economics, Sapienza University of Rome (1988–1992) MA in Economics, University of Warwick (1987–1988) Laurea in Statistics and Demography, Sapienza University of Rome (1977–1984) Research Interests: Prof. Ginebri specializes in public policy, fiscal sustainability, and the social impacts of pension reforms. His research explores how demographic trends, wealth distribution, and political dynamics shape economic policies. Recent work includes analyses of longevity risks, pension equity, and fiscal policy integration in the EU. Key Projects: Coordinated studies on pension system sustainability (2002–2018) Developed models for forecasting public pension expenditure Contributed to EU tax policy observatories Awards: Recipient of the national PhD award (1994) and recognition for academic excellence in tax policy research (2001). Teaching: Teaches courses in political economy, public finance, and macroeconomics at undergraduate and graduate levels.
Professor Michael Ben-Gad is a Professor of Economics at City, University of London, where he has served since 2009. He previously held roles as Head of the Department of Economics (2010–2013) and Deputy Head (2008–2010). His research focuses on dynamic macroeconomics, taxation, public debt, immigration economics, and national security, with notable contributions to endogenous growth models and compositional data econometrics. He holds a PhD from the University of Chicago (1996) and has taught at institutions including the University of Houston and University of Haifa. Ben-Gad served on the U.S. National Academy of Sciences panel on immigration's economic and fiscal impacts (2014–2016) and received a 2017 Fellowship from the Royal Society of Arts (FRSA). His work spans academic journals (Economic Modelling, Journal of Economic Dynamics and Control) and media outlets (BBC, CNBC, Al Jazeera), addressing topics like Brexit economics, fiscal policy, and geopolitical challenges. He co-convenes the City Branch of Academics for Academic Freedom and is a founding member of London Universities' Council for Academic Freedom. His research grants include funding from HM Treasury/ESRC on fiscal policy and immigration (2011), the Israel Science Foundation (2007–2008), and the Samuel Ne'eman Foundation (2004). Professional roles include editorial board membership for Economic Affairs and membership of the ESRC Grant Assessment Panel (2021). He is proficient in Hebrew and contributes to policy debates on taxation, macroprudential frameworks, and defense economics.
Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.
Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.
Xin Guo is Professor and Department Chair of Industrial Engineering and Operations Research (IEOR) at UC Berkeley's College of Engineering, holding the Coleman Fung Chair in Financial Modeling. Her research bridges mathematical finance, stochastic control, and machine learning with applications in risk analytics and quantitative trading. Education: Ph.D. in Mathematics, Rutgers University (1999) Research Interests: Professor Guo's work centers on mathematical finance , stochastic games , and reinforcement learning . She develops theoretical frameworks for α-potential games and mean-field systems while applying signature methods and GANs to financial data. Her research addresses critical problems in portfolio optimization, fraud detection (e.g., Medicare analytics), and market forecasting, emphasizing the intersection of stochastic control with machine learning for real-world decision-making under uncertainty. Publication Trends: Recent work (2023-2025) shows increasing focus on multi-agent reinforcement learning through mean-field game theory, with applications spanning finance (corporate bonds, trading), healthcare (fraud detection), and transportation (rate forecasting). Key innovations include BSDE approaches for stochastic games, signature-based time series analysis, and theoretical guarantees for GAN training dynamics. Scientific Awards: Holds the prestigious Coleman Fung Chair in Financial Modeling, reflecting significant contributions to quantitative finance research. Advising and Grants: As IEOR Department Chair, Professor Guo mentors graduate students in stochastic modeling and financial engineering. Her research is supported by the Coleman Fung Endowment Fund, with collaborations spanning finance, healthcare, and transportation sectors through industry partnerships. Labs and Teams: Leads the Risk Analytics & Data Analysis Research (RADAResearch) Lab ( https://risklab.ieor.berkeley.edu/ ), which develops cutting-edge methodologies for risk assessment, data-driven decision-making, and game-theoretic solutions to complex systems. The lab fosters interdisciplinary work connecting mathematical theory with practical applications in FinTech and beyond.
Sean Cao serves as Associate Professor (with tenure) at the Robert H. Smith School of Business, University of Maryland, where he is Director and Co-founder of the AI Initiative for Capital Market Research. He also holds an affiliation as professor at Harvard Business School's D 3 Institute. His academic journey began with a Ph.D. from the University of Illinois at Urbana-Champaign. Dr. Cao's research focuses on the intersection of artificial intelligence and capital markets, with particular expertise in how machine learning transforms financial analysis, corporate disclosure practices, and investment decision-making. His work examines the evolving relationship between human analysts and AI systems, blockchain applications in financial reporting, and the strategic adaptation of corporate communications for machine readership. He has pioneered research on the "AI divide" among investor groups and developed frameworks for human-AI collaborative stock analysis. His publication portfolio spans top journals including Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, and Management Science. The research demonstrates consistent thematic progression toward increasingly sophisticated AI applications in finance, with recent work exploring distributed ledger technologies for auditing, machine learning for extracting private information from disclosures, and the economics of greenwashing in ESG funds. His studies frequently combine textual analysis with traditional financial metrics to uncover novel market insights. Fama-DFA Prize from Journal of Financial Economics for best paper in capital markets and asset pricing Michael J. Brennan Award from Review of Financial Studies Deloitte Initiative for AI and Learning award for developing trustworthy AI for social equity PanAgora Asset Management's Dr. Richard A. Crowell Memorial Prize Multiple best paper awards from Midwest Finance Association, Global AI Finance Conference, and Asian Finance Association Dr. Cao has delivered over 200 invited research talks at major institutions including the Central Bank of Japan, Central Bank of Thailand, and U.S. Securities and Exchange Commission. He serves as Guest Associate Editor for Management Science and has co-chaired Review of Financial Studies conferences on FinTech and Machine Learning. His educational initiatives include a widely adopted free AI textbook for finance and accounting that has been implemented at universities worldwide including Indiana University, UT Dallas, and University of Minnesota. As Director of the AI Initiative for Capital Market Research, Dr. Cao leads a multidisciplinary team exploring practical AI applications in finance. The initiative has secured significant funding including a $150,000 grant from GRF CPAs & Advisors. His research group maintains strong industry connections through partnerships with regulatory bodies, financial institutions, and technology companies, facilitating the translation of academic research into practical financial applications.
Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .
Celestine Mendler-Dünner is a Principal Investigator at the ELLIS Institute in Tübingen, co-affiliated with the Max Planck Institute for Intelligent Systems and the Tübingen AI Center. She leads the Algorithms and Society research group, focusing on machine learning in social contexts and the role of prediction in digital economies. Her work bridges theoretical machine learning with practical societal impact, developing tools for safe, reliable, and equitable AI ecosystems. Her educational background includes a PhD from ETH Zurich in collaboration with IBM Research, followed by an SNSF postdoctoral fellowship at UC Berkeley hosted by Moritz Hardt. She was previously a group leader at the Max Planck Institute for Intelligent Systems before joining the ELLIS Institute. Mendler-Dünner's research spans several interconnected themes including performative prediction (where predictions change the behavior they aim to predict), algorithmic collective action (how participants can steer AI systems toward common goals), and the role of LLMs in social science research. Her work combines theoretical foundations with practical implementations, addressing challenges in interactive machine learning, optimization in dynamic environments, and context-specific evaluation of AI systems. She particularly examines how algorithmic predictions mediate services and platforms at societal scale, exploring concepts of economic power in digital markets. Her publication record shows a clear evolution from system-aware machine learning algorithms (including foundational work on IBM Snap ML) toward increasingly sociotechnical questions at the intersection of machine learning, economics, and policy. Recent work focuses on measuring performative power in digital economies, evaluating LLMs as risk scores, and developing frameworks for algorithmic collective action in recommender systems and labor markets. Among her notable recognitions are the ETH Medal for her dissertation, the IBM Research Division Award, the Fritz Kutter Award, and the IBM Eminence and Excellence Award. She is an ELLIS Scholar, a fellow of the Elisabeth-Schiemann-Kolleg, and affiliated with several prestigious research programs including the International Max Planck Research School for Intelligent Systems and the Max Planck ETH Center for Learning Systems. ETH Medal (dissertation award) IBM Research Division Award Fritz Kutter Award IBM Eminence and Excellence Award SNSF Early Postdoc Mobility Fellowship Mendler-Dünner actively mentors the next generation of researchers, advising PhD student Patrik Wolf and supervising research interns including Joachim Baumann, Haiqing Zhu, and Anna Badalyan, as well as Master's student Dorothee Sigg. She serves as core faculty for the International Max Planck Research School and associated faculty for the Max Planck ETH Center for Learning Systems. Her group has secured significant research funding through fellowships and institutional support, enabling work on projects like Powermeter (measuring search engine influence) and Snap ML (resource-efficient machine learning library with over 1 million PyPI downloads). She leads the Algorithms and Society research group, which examines machine learning as part of broader sociotechnical ecosystems. The group explores human-population interactions with algorithmic systems and incorporates these insights into learning system fundamentals. Current projects include investigating economic incentives in digital platforms, developing tools for systematic LLM evaluation in social science contexts, and creating frameworks for collective action in algorithmic systems. Mendler-Dünner also co-organizes the Algorithmic Collective Action workshop at NeurIPS 2025, demonstrating her leadership in emerging research directions at the AI-society interface.
Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML
Alexandra Livada is a Professor at the Department of Statistics within the School of Information Sciences and Technology at Athens University of Economics and Business (AUEB). She holds office at two locations: 12 Codringtonos Street, 2nd Floor and 76 Patision Street, Antoniadou Wing, 3rd Floor in Athens, Greece. Her contact information includes email livada@aueb.gr and phone number +30 210-8203521. Dr. Livada earned her BA and MA in Economics from Athens School of Economics and Business followed by a PhD in Economics from Essex University, UK in 1988. Her academic career has spanned several decades with extensive teaching experience at both undergraduate and postgraduate levels. Her research interests encompass a diverse range of quantitative fields: Quantitative economics and applied econometrics Time series analysis and forecasting techniques Income distribution and inequality measurement Applied financial econometrics Business cycles analysis Medical statistics Index numbers and official statistics Professor Livada's publication record demonstrates consistent scholarly productivity across multiple disciplines, with a noticeable trend toward interdisciplinary work connecting economics with healthcare and social policy. Her recent research shows increasing focus on income inequality across different geographic regions, economic sentiment during crises, and the intersection of medical conditions with statistical analysis. Her scholarly contributions have been recognized through numerous citations in leading journals and books. She has served as a referee for prestigious journals including the European Journal of Political Economy, Journal of Public Economics, and Journal of Insurance, Mathematics and Economics. Professional service highlights include: Member of multiple project teams Marie-Curie project supervisor External evaluator for the Greek State Scholarship Foundation (IKY) External evaluator for the Social Sciences and Humanities Research Council of Canada Co-author of the book "Index Numbers and Official Statistics" Professor Livada maintains an active research agenda with collaborations spanning economics, statistics, and medical fields, demonstrating the interdisciplinary nature of contemporary quantitative research.
Rizwan Ahmed is a Senior Lecturer in Accounting and Finance at the University of Kent 's Kent Business School . He serves as Director of Studies for the Department for Accounting and Finance, with prior experience as an Assistant Professor at institutions like the University of Birmingham, Cardiff Business School, and Birkbeck University of London. His research focuses on Sustainable Finance , FinTech , and Corporate Governance , with funding from the Economic and Social Research Council (ESRC) for collaborative projects. Fellowship of Higher Education Academy (HEA-UK) Associate Fellow Member, Chartered Institute of Securities and Investment (CISI-UK) His publications in journals like Energy Economics and International Journal of Finance and Economics examine intersections between climate risk, financial markets, and regulatory frameworks. He supervises PhD research in areas including Sustainable Finance, Climate Finance, and Corporate Governance in the financial sector. Research Trends Recent articles analyze climate change impacts , energy market spillovers , and ESG performance under sanctions, utilizing empirical methods and data from the UK, China, and emerging economies. Methodological innovations include cross-quantilogram analysis and portfolio optimization frameworks. Scientific Awards Fellowship of Higher Education Academy (HEA-UK) Associate Fellow Member, Chartered Institute of Securities and Investment (CISI-UK) Rizwan also acts as a peer reviewer for journals including International Review of Financial Analysis and Technological Forecasting & Social Change .
Dr. Anwar Haque is an Associate Professor in the Department of Computer Science at Western University, Canada, and a Faculty of Science Distinguished Research Professor. He holds a Ph.D. in Electrical and Computer Engineering and an M.Sc. in Computer Science from the University of Waterloo. Prior to academia, he was Associate Director at Bell Canada. His research focuses on 5G networks, IoT, cybersecurity, AI, and autonomous systems, with over 100 peer-reviewed publications and $15M in collaborative grants. Dr. Haque leads the Western Information & Networking Group (WING) Lab and is the founder/CEO of Bamboo Innovations Inc., a tech startup developing socially responsible smart technologies. Leadership roles include industry expert-in-residence in the Faculty of Science, Undergraduate Chair of the Computer Science Department, and member of Western’s Senate. He has delivered over 30 keynote talks and media features include BBC Earth and The Globe and Mail. Awards include the IEEE CCECE Leadership Award and multiple grants from NSERC, MITACS, and Bell Canada. His work spans network reliability, smart grids, and cybersecurity, with industry partnerships like the Bell-Western 5G Research Centre.
Franz Heukamp is the Director General of IESE Business School at the University of Navarra and Professor in the Department of Decision Analysis. He holds the Antonio Valero Chair of Business Administration since 2016 and previously served as Vice Dean of the MBA program and Secretary General of IESE. His academic background includes a Ph.D. in Engineering from the Massachusetts Institute of Technology, an Ingénieur Civil des Ponts et Chaussées from Ecole Nationale des Ponts et Chaussées in Paris, and a Diplom-Ingenieur from Technische Universität München. Professor Heukamp's research focuses on behavioral decision-making with a special interest in neuroeconomics, examining how people make decisions under uncertainty, particularly regarding time-probability trade-offs. His work bridges business decision theory with neuroscience, as evidenced by his publications in journals like Management Science , European Neuropsychopharmacology , and British Journal of Pharmacology . In recent years, he has expanded his research interests to include artificial intelligence applications in management and sustainability. His recent scholarly output shows a progression from fundamental decision theory research to practical applications of AI in business leadership and management education. He has published extensively on risk preferences, intuition in decision making, and the neurological basis of economic choices, with increasing focus on how digital transformation and AI are reshaping management practices. As Director General, Heukamp has championed several strategic initiatives including internationalization efforts across North America, Latin America, and Central Europe, development of new degree programs like the Master in Management and Munich Executive MBA, and integration of digital learning technologies while maintaining the school's commitment to in-person educational excellence. Professor Heukamp is an active member of both the Institute for Operations Research and the Management Sciences and the Neuroeconomics Society, reflecting his interdisciplinary approach to business education and research. His leadership prioritizes supporting faculty research, advancing lifelong learning solutions for executives, and maintaining IESE's mission of service to society through academic innovation.
Felipe Csaszar is a Professor of Strategy and Chair of the Strategy Department at the University of Michigan's Ross School of Business. His research focuses on decision structures' impact on innovation, financial performance, and social outcomes, with particular attention to cognitive frameworks, organizational processes, and AI's role in decision-making. He holds a PhD and MA from the Wharton School, University of Pennsylvania. Education: PhD in Strategy, University of Pennsylvania (2009) MA in Strategy, University of Pennsylvania (2007) Research Interests: Strategic decision-making under AI integration Cognitive and structural drivers of innovation Organizational decision processes and design Formal modeling and empirical strategy research Editorial Roles: Senior Editor, Strategy Science and Management Science Former Editor, Organization Science Co-editor, Handbook of AI and Strategy Professional Experience: Prior role: Assistant Professor at INSEAD Previous career: CEO of an internet startup and Head of Research at an asset management firm Labs/Teams: Leading the Strategy Science division at INFORMS Co-chair of the SMS Behavioral Strategy division
Trine Krogh Boomsma is a Professor in the Department of Insurance and Economics at the University of Copenhagen's Department of Mathematical Sciences. Her research focuses on optimization under uncertainty with significant applications in energy systems, particularly electricity markets, renewable energy investments, and power system planning. PhD in Mathematics-Economics, Aarhus University (2003-2007) Visiting PhD at University of Duisburg-Essen (2004) Academic career includes positions at Risø National Laboratory for Renewable Energy and Imperial College London Her work spans stochastic programming, real options analysis, and dynamic programming to address energy sector challenges. Key areas include support schemes for renewables, market risk modeling, and operational optimization of hybrid conventional-renewable systems. Recent research explores policy impacts on investment decisions and advanced scenario generation techniques. Major publications (2012-2020) cover renewable energy policy frameworks, power plant valuation models, and sequential market bidding strategies. These works emphasize electricity market dynamics, investment risk quantification, and robust planning under uncertainty. She teaches linear programming, integer programming, and stochastic programming applications in operational analysis, contributing to energy economics education at the department.
