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.
Ronald C. Lasky is a Professor of Engineering at Dartmouth College's Thayer School of Engineering and a Senior Technologist at the Indium Corporation. His academic roles include teaching courses such as ENGM 187: Technology Innovation and Entrepreneurship and ENGS 155: Intermediate Thermodynamics . Dr. Lasky holds a BS in Engineering Physics from Cornell University (1970), an MS in Applied Mathematics from Binghamton University (1974), and a PhD in Materials Science from Cornell University (1986). His research focuses on process optimization, electronic assembly, materials science, and environmental compliance. Notable contributions include work on lead-free solder assembly and Lean Six Sigma methodologies. He received the Member of Technical Distinction Award from the SMTA in 2021. Dr. Lasky actively engages with industry, exemplified by his collaboration with Galanz in China, exploring modern manufacturing practices and infrastructure. His insights on global supply chains and technological advancements highlight his dual academic and professional expertise.
Professor Catherine Greenhill is a faculty member at the School of Mathematics and Statistics, UNSW Sydney , where she serves as Professor and head of the Combinatorics group. Her academic career spans institutions including the University of Queensland, University of Oxford, University of Leeds, University of Melbourne, and Australian National University. D.Phil., University of Oxford (1996) M.Sc. (Research) in Combinatorics (1992) B.Sc. (Hons) in Pure Mathematics (1991) Her research focuses on the intersection of discrete mathematics , theoretical computer science , and probability , particularly in asymptotic combinatorics , probabilistic methods , and analysis of algorithms . Her work includes asymptotic enumeration of combinatorial structures and design of randomized algorithms for graph sampling and counting. Her recent publications (2025–2021) center on random graphs and hypergraphs , with key contributions to switch Markov chains , degree sequence analysis , and chromatic number bounds . These works reflect her expertise in probabilistic combinatorics and algorithmic complexity . Scientific Awards: Fellow of the Australian Academy of Science (2022) Christopher Heyde Medal in Pure Mathematics (2015) June Griffith Fellowship (2013) Hall Medal (2010) Advising and Grants: She has supervised numerous PhD/Masters students and secured multiple ARC Discovery Grants (2019–2021, 2014–2016, 2012–2014). Her grants address topics like hypergraph modeling, random discrete structures, and network analysis in illicit drug trafficking.
Joshua D. Angrist is the Ford Professor of Economics at the Massachusetts Institute of Technology, where he has been a faculty member since 1996. He is also a co-founder and director of MIT's Blueprint Labs and a Research Associate at the National Bureau of Economic Research. Angrist shares the 2021 Nobel Prize in Economic Sciences with David Card and Guido Imbens for their methodological contributions to the analysis of causal relationships. Angrist received his B.A. from Oberlin College in 1982 and completed his Ph.D. in Economics at Princeton University in 1989. Prior to joining MIT, he taught at Harvard University and the Hebrew University of Jerusalem. His academic journey began somewhat unconventionally, as he left high school early after 11th grade, worked for over a year, and only later discovered his passion for economics through an inspiring teacher at Oberlin. Angrist's research focuses on developing and applying innovative econometric methods to answer important economic questions using natural experiments. His work spans labor economics, education economics, and causal inference methodology. He is particularly known for his contributions to instrumental variables methods and the Local Average Treatment Effect (LATE) framework developed with Guido Imbens. His research explores the economics of education and school reform, the impact of social programs on labor markets, and the effects of immigration and regulation. His recent publications reveal a continued focus on causal inference methods applied to education policy questions, labor market issues, and health economics. The trend shows increasing sophistication in research design, with particular attention to addressing selection bias and developing methods for external validity. His work spans theoretical econometric contributions alongside empirical applications in education, labor markets, and health. Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (2021) Fama Prize for Graduate Education (2018) Fellow of the American Academy of Arts and Sciences Fellow of the Econometric Society Angrist is deeply committed to teaching and mentoring. He has developed influential econometrics textbooks including 'Mostly Harmless Econometrics' and 'Mastering Metrics' with Jörn-Steffen Pischke. At MIT, he teaches courses including Labor Economics I (14.661), Econometric Data Science (14.32), and Labor Economics and Public Policy (14.64). He emphasizes selecting UROP students who have mastered foundational economics through courses like 14.64 and 14.32. Beyond MIT, Angrist co-founded Avela, a software startup using cutting-edge research to help schools improve enrollment and operations. Angrist co-founded and directs MIT's Blueprint Labs, which brings together researchers from economics, computer science, and education to develop innovative solutions for educational challenges. Through Blueprint Labs and Avela, his work bridges academic research with practical applications in education policy and technology.
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.
Lily L. Tsai is the Ford Professor of Political Science at the Massachusetts Institute of Technology (MIT) and founder/director of the MIT Governance Lab (MIT GOV/LAB). She previously chaired the MIT Faculty and focuses her research on governance, accountability, and political participation in developing contexts, particularly Asia and Africa. Her research explores how decentralization, democratic reforms, informal institutions, and economic development influence public goods provision. She also investigates the dual impacts of social capital and civil society on political outcomes, and analyzes political behavior in authoritarian and transitional systems through surveys, interviews, and field experiments. Recent publications span topics including collective governance, vaccine compliance in Uganda, anticorruption strategies in China, and the role of informal institutions in rural China. Tsai’s work appears in journals like American Political Science Review and World Development . Dogan Award for best book in comparative research (2007-08) She leads interdisciplinary projects such as the Compass Initiative (SHASS Education Innovation Fund) and The Science of Respect with MIT’s Brain and Cognitive Sciences department. MIT GOV/LAB, which she founded in 2014, emphasizes real-time empirical collaboration between researchers and practitioners to address governance challenges.
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.
Amitabha Bagchi is a Professor in the Department of Computer Science and Engineering at IIT Delhi. His research spans data algorithmics, probability, networks, and theoretical computer science, with applications in distributed systems, social networks, and AI-driven platforms. He has published extensively in leading venues such as SIGMOD, VLDB, ICDE, AAAI, and KDD, often collaborating with students and researchers on problems involving graph algorithms, fairness, and large-scale data analysis. Research Interests: His primary research interests include Data Algorithmics, Probability and Networks, Theoretical Computer Science, Distributed Algorithms, Graph Algorithms, and Machine Learning Theory. He investigates algorithmic foundations for real-world problems such as food delivery optimization, social network analysis, and efficient data structures for streaming and large graphs. Publication Trends: Recent publications focus on fairness in gig economy platforms, efficient solvers for graph Laplacians, generalization in neural networks, and temporal graph querying. His work combines theoretical rigor with practical impact, often involving GPU acceleration, distributed computing, and data-aware algorithm design. Scientific Service: Editor, Algorithms (2020–present) Editor, Journal of Discrete Algorithms , Elsevier (2006–2018) Guest Editor, special issue on Algorithms for Shortest Paths in Dynamic and Evolving Networks , Algorithms (2021) Volume Editor for proceedings of ESA, ATMOS, COCOON, and others Conference Leadership: He has served on numerous program committees and as chair for conferences including ESA (Engineering Track, 2016), ATMOS (2020), and ICALP (2019). His involvement spans algorithmic engineering, transportation optimization, and theoretical computer science forums. Teaching: He currently teaches COL863: Special Topics in Theoretical Computer Science on concentration inequalities and their applications. He has previously taught advanced courses in algorithms and data structures.
Tomaso Aste is a Professor of Complexity Science at the Department of Computer Science, University College London (UCL). He founded the Financial Computing and Analytics group and co-founded the UCL Centre for Blockchain Technologies. His work bridges complex systems, data science, and finance, with applications in blockchain, fintech, and market modeling. Education: PhD in Physics (Politecnico di Milano, 1994); Laurea in Physics (University of Genoa, 1990) Prior Appointments: Reader at University of Kent's School of Physics; Associate Professor at Australian National University's Applied Mathematics His research focuses on data-driven modeling of complex systems , particularly financial systems, complex networks, and statistical physics. He has pioneered information filtering networks and topological machine learning methods for financial applications, including portfolio optimization, risk assessment, and cryptocurrency analysis. Recent publications highlight his expertise in financial time-series analysis, blockchain technology, and AI-driven modeling. Articles explore topics like limit order books, cryptocurrency market fragility, and topological neural networks. His work has influenced regulatory technology (RegTech) frameworks and digital economy strategies. Scientific Awards : Marie Curie Individual Fellowship University of Genoa graduate study specialization Fellowship Bacheflor Boncompagni-Ludovisi Foundation Fellowship Awarded fellowships from European Commission and academic foundations support his interdisciplinary research. He has held editorial roles at journals like Philosophical Magazine and Granular Matter , and contributed to professional societies including American Physical Society and Australian Research Council panels. Teaching & Academic Leadership : Co-created four UCL Master's programs: Financial Risk Management, Computational Finance, Financial Technologies, Emerging Digital Technologies Coordinates executive training on AI, blockchain, fintech, and regtech for regulators and private firms Teaches graduate-level courses in Data-Driven Modeling, Data Science, and Advanced AI
Avi Turetsky is an Adjunct Professor at the Weatherhead School of Management (Case Western Reserve University) and a Research Fellow in private equity. Currently a Partner and Co-Head of the Quantitative Research Group (QRG) at Ares Management, he oversees original research, quantitative software development, and tools production teams. Previously served as Chief Operating Officer for The Riverside Company's Europe Fund and holds advisory roles at EDHEC and INSEAD . Research Focus: Turetsky’s work bridges private equity practice and quantitative finance, examining distributional patterns in investment outcomes, competency frameworks for financial professionals, and mathematical models for performance evaluation. His recent publications explore robust statistical methods in portfolio construction, direct alpha calculations for skill assessment, and sector-based value creation strategies in private equity-owned companies. Key Article Trends: His research spans 2016–2023, emphasizing quantitative methodologies in private equity, including skew analysis , value creation metrics , and competency clustering . Topics range from alliance changes in intercollegiate athletics to mathematical modeling of investment professional performance. Leadership & Teams: At Ares Management, Turetsky co-leads the QRG, managing cross-functional teams in Original Research , Quantitative Software Development , and Tools Production . He collaborates with investment teams to integrate quantitative strategies into decision-making processes.
Ariel Pakes is the Thomas Professor of Economics at Harvard University, part of the Faculty of Arts and Sciences and the Department of Economics. He previously held the Charles and Dorothea Dilley Professorship at Yale University and tenured roles at institutions including the University of Wisconsin and the Hebrew University of Jerusalem. His research focuses on industrial organization (I.O.), the economics of technological change, and econometric theory. Pakes has advised notable students who now lead economics departments globally and has received prestigious awards including the Frisch Medal (1986) and election to the American Academy of Arts and Sciences (2002). Education: Pakes earned his Ph.D. in Economics from Harvard University in 1980. His academic journey includes roles as a lecturer at Harvard and positions at the University of Jerusalem. Research Interests: Pakes has pioneered methodologies for analyzing I.O. models, including demand and cost system estimation. Recent work examines AI’s impact on pricing strategies, antitrust policy implications of market dynamics, and healthcare cost structures. His theoretical contributions include simulation estimators and frameworks for dynamic oligopolies. Awards: Frisch Medal (Econometric Society, 1986) Fellow of the Econometric Society (1988) Fellow of the American Academy of Arts and Sciences (2002) Best Graduate Student Advisor Award (Yale University, 1996) Advisory and Grants: Pakes has served on NSF panels, including the Economics Advisory Panel and Presidential Fellow Advisory Board. His consulting work spans government agencies and large firms. He edited the RAND Journal of Economics and co-chaired the AEA Census Advisory Panel. Labs/Teams: Collaborates with NBER as a Research Associate and contributes to interdisciplinary projects like the PNAS issue on 'Science, Technology and the Economy.'
Panos Ipeirotis is a Professor at the Leonard N. Stern School of Business at New York University, affiliated with the Department of Technology, Operations, and Statistics. He also serves as the George A. Kellner Faculty Fellow and is associated with the Center for Data Science and Computer Science departments at NYU. PhD in Computer Science (Columbia University, 2004) MSc in Computer Science (Columbia University, 2001) BSc in Computer Engineering & Informatics (University of Patras, 1999) His research spans crowdsourcing, machine learning, human-AI collaboration, online labor markets, and social media analytics. He pioneered human-machine loop systems that combine human and machine intelligence to achieve superior outcomes. His work has applications in data quality assurance, visual media search (e.g., Google Project Glass), and economic valuation of user-generated content. Recent publications focus on algorithmic fairness in hiring systems, occupational segregation analysis, and theoretical advancements in crowdsourcing consensus mechanisms. Earlier work includes foundational studies on data quality in crowdsourcing platforms, economic impacts of product reviews, and query optimization for text-centric tasks. 2015 Lagrange Prize in Complex Systems NSF CAREER Award SIGKDD Test of Time Award (2020) Multiple Best Paper awards (WWW 2011, KDD 2008, SIGMOD 2006) He has received significant grants, including a $1.5 million Google Research Grant (2013) for integrating crowdsourcing with machine learning algorithms. His work bridges computer science, economics, and social psychology, with implications for policy-making and business strategy.
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
Clifford Stein is a Professor of Industrial Engineering and Operations Research (IEOR) and Computer Science at Columbia University, and Associate Director for Research at the Data Science Institute. He holds a Ph.D. (1992), M.S. (1989), and B.S.E. (1987) from MIT and Princeton University, respectively. His research focuses on algorithms, combinatorial optimization, operations research, scheduling, and computational biology. A co-author of the best-selling textbook Introduction to Algorithms , Stein has published widely in top venues and holds prestigious awards like ACM Fellow and NSF Career Award. His work includes foundational contributions to minimum cut algorithms, scheduling theory, and network optimization, supported by NSF and Sloan Foundation grants. Stein has advised over 40 graduate and undergraduate students, many now in academia and industry.
