Professor Shaomin Wu is a faculty member at the University of Kent's Kent Business School, where he holds the academic rank of Professor of Business/Applied Statistics. He earned an MSc and PhD in applied statistics and has extensive industry experience, including a five-and-a-half-year stint at a global manufacturer in Shanghai before moving to the UK in 2001. He has held roles as a postdoctoral researcher and lecturer before joining Cranfield University and later the University of Kent. His research focuses on recurrent event data analysis, machine learning, and reliability mathematics, with funding from the EPSRC and ESRC. His research projects include managing risk in warranty servicing policies, smart data analytics for local government, and sustainable supply chain demand forecasting. He teaches modules such as risk analysis, reliability engineering, and machine learning. Currently supervising PhD students in time series forecasting, explainable AI, and recurrent event data analysis, he also serves as a co-chair of international conferences, editorial board member, and external examiner for doctoral degrees. Notably, he ranks among the top 2% of global scientists by Stanford University. His work integrates machine learning with business analytics, resilience engineering, and environmental sustainability. Key contributions include IoT-driven resilience methodologies for smart grids and unmanned systems, as well as frameworks for corporate carbon disclosure and maintenance optimization under uncertainty.
David Dillenberger is a Professor of Economics at the University of Pennsylvania's School of Arts and Sciences, Department of Economics. He has been at Penn since 2008, following his PhD from Princeton University. His research focuses on microeconomic theory, particularly decision theory, with an emphasis on non-expected utility models, risk and time preferences, and social preferences. Education: PhD from Princeton University. His work bridges theoretical economics with behavioral insights, addressing topics such as stochastic impatience, mixture aversion, and consensus effects in group decision-making. Research interests include modeling decision-making under uncertainty, time preferences, and social behavior. Notable contributions include studies on time lotteries, stochastic impatience, and the dynamics of subjective information choice. Publications span top journals like the American Economic Review, Econometrica, and Theoretical Economics, reflecting a focus on foundational questions in economic theory. His recent work (2023–2024) explores caution in decision-making and allocation mechanisms under aversion to mixture outcomes. No scientific awards are listed, though his extensive publication record indicates significant scholarly impact. Teaching includes courses on strategic reasoning and fairness/altruism in the PPE program. Advising and grants: Details not provided in available texts. Active participation in interdisciplinary research through affiliations like the Penn SoNG (Social Norms and Behavioral Dynamics) initiative. Labs/Teams: Affiliated with the Department of Economics and the Master of Behavioral and Decision Sciences program at UPenn.
Hamsa Bastani is an Associate Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania, with a secondary appointment in Statistics and Data Science. She co-directs the Wharton Healthcare Analytics Lab and serves as an Associate Editor for Operations Research, M&SOM and OR Letters. Her academic journey began with summa cum laude graduation from Harvard in 2012 with an A.M. in physics and A.B. in physics and mathematics. She completed her PhD in Stanford's Electrical Engineering department under Mohsen Bayati, followed by a Herman Goldstine postdoctoral fellowship at IBM Research. Professor Bastani's research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications spanning healthcare operations, social good, and revenue management. Her work demonstrates particular expertise in sequential decision-making (bandits, reinforcement learning), learning from auxiliary data sources (transfer learning, meta-learning), and designing effective human-AI interfaces (interpretability, fairness). She has made significant contributions to understanding how AI systems affect and augment human behavior, with the goal of designing AI tools that help humans thrive. Her publications reveal a strong trend toward high-impact applications of machine learning in critical societal domains. A significant portion of her recent work focuses on healthcare applications, including optimizing health supply chains in low- and middle-income countries, designing clinical trial protocols, and creating targeted public health interventions. Another major theme examines the complex relationship between humans and AI systems, particularly how AI affects learning outcomes and decision-making processes. Her work frequently bridges theoretical advances with practical implementation, as evidenced by country-scale deployments in Greece and Sierra Leone. Wagner Prize for Excellence in Operations Research Practice (2021) Pierskalla Award for Best Paper in Healthcare (2021, 2019, 2016) Behavioral OM Best Paper Award (2021) Public Sector in OR Best Paper Award (2024) INFORMS Data Mining Best Paper Award (2022) Wharton Teaching Excellence Award (2019, 2020, 2021) Professor Bastani has advised numerous PhD students who have gone on to prominent positions, including Pia Ramchandani (Director of Responsible AI at PwC), Arielle Anderer (Assistant Professor at Cornell Johnson), and Kan Xu (Assistant Professor at ASU Carey). Her research has been supported by collaborations with national governments, including the Greek government where she co-designed Eva, the national-scale reinforcement learning system for targeted COVID-19 testing, and the Government of Sierra Leone where she improved patient access to essential medicines by nearly 20% via decision-aware learning. She has also conducted the first large field study deploying generative AI tutors in high school math classes. She leads the Wharton Healthcare Analytics Lab and serves on the Steering Committee for the Penn Center for Health Incentives and Behavioral Economics and on the statistics advisory committee for the AHA Food is Medicine Initiative. Outside academia, she serves on the Workday AI Advisory Board, demonstrating her commitment to translating academic research into practical applications.
Aaron Roth is the Henry Salvatori Professor of Computer and Cognitive Science at the University of Pennsylvania, affiliated with the Department of Computer and Information Science in the School of Engineering and Applied Science. He holds a secondary appointment in the Department of Statistics and Data Science at the Wharton School and is associated with several research centers including PRiML, the Warren Center for Network and Data Sciences, and the AMCS program. He received his PhD from Carnegie Mellon University under Avrim Blum and was a postdoc at Microsoft Research New England. His research focuses on algorithms and machine learning, particularly in private data analysis, fairness in machine learning, game theory, mechanism design, and learning theory. His work bridges theoretical computer science with societal concerns, advocating for ethically aware algorithm design. He co-authored the book The Ethical Algorithm with Michael Kearns, which explores how to embed social values like privacy and fairness into algorithmic systems. His recent publications show a strong trend toward uncertainty quantification, multicalibration, conformal prediction, and fairness in reinforcement learning and high-dimensional settings. He frequently publishes in top-tier venues such as STOC, FOCS, ICML, NeurIPS, and COLT, often with a focus on rigorous theoretical foundations with practical implications. Hans Sigrist Prize Presidential Early Career Award for Scientists and Engineers (PECASE) Alfred P. Sloan Research Fellowship NSF CAREER award Google Faculty Research Award Amazon Research Award Yahoo Academic Career Enhancement award Roth has advised numerous PhD students and postdocs, many of whom now hold academic or industry research positions. He is also an Amazon Scholar at AWS and has served in advisory roles for companies like Apple, Facebook, Leapyear, and Spectrum Labs. He has been active in organizing workshops and tutorials on differential privacy, fairness, and adaptive data analysis, and has given keynotes at major conferences and institutions worldwide. He leads research groups and collaborates widely across Penn, focusing on responsible AI, privacy, and algorithmic fairness. His lab produces foundational work on calibration, unlearning, privacy-preserving learning, and equitable decision-making systems.
Pauli Murto is a Professor and Head of the Department at Aalto University School of Business, Department of Economics. His research spans microeconomic theory, information economics, and game theory, with a focus on strategic decision-making under uncertainty. Aalto University School of Business, Espoo, Finland Member of Helsinki Graduate School of Economics Research Interests: Dr. Murto's work examines strategic timing in economic decisions, information aggregation in games, auction theory, and investment behavior under uncertainty. His publications address topics like: Common value auctions and affiliated signals Stepwise investment under multi-dimensional uncertainty Equilibrium delay and neighborly coordination Irreversible investment in oligopolistic markets Publications (2002–2024): His research appears in top journals like Review of Economic Studies , Theoretical Economics , Journal of Economic Theory , and RAND Journal of Economics , often collaborating with scholars such as Juuso Välimäki and Chang-Koo Chi. Contact: Available at pauli.murto@aalto.fi or +358 40 353 8174. Office located in Room V308, School of Business building, Aalto University.
Associate Professor Sam Kirshner is a faculty member at the University of New South Wales within the School of Information Systems and Technology Management . His research focuses on behavioral decision making , algorithmic impact on operations , and artificial intelligence applications in business contexts. PhD in Management Science from Queen’s University, Canada Teaching expertise in data visualization, predictive analytics, and AI ethics Co-author of Business Analytics: A Management Approach Member of the Ethical AI Advisory His research explores how psychological distance and construal level theory influence decisions in supply chains, technology management, and consumer behavior. Recent work examines ChatGPT's decision biases , algorithm aversion , and sustainable operations under financial constraints. Key publication trends show focus areas: AI ethics and human-AI collaboration Behavioral supply chain analysis Temporal/spatial psychological distance effects CO2 forecasting with sparse data Consumer behavior in digital platforms Virtual reality and cognitive processing Supervision roles include mentoring 2 PhD students and 6 honors students, contributing to the next generation of scholars in business analytics and technology management.
J. Eric Bickel is a Professor at The University of Texas at Austin, serving as Director of the Operations Research & Industrial Engineering (ORIE) and Engineering Management programs. He holds a courtesy appointment in the Department of Petroleum and Geosystems Engineering and directs the Center for Engineering & Decision Analytics (CEDA). His academic background includes a PhD and MS in Engineering-Economic Systems from Stanford University and a BS in Mechanical Engineering from New Mexico State University. His research focuses on decision analysis under uncertainty, addressing topics like probabilistic modeling, climate engineering, risk management, and applications in sports and energy sectors. His work has been featured in major media including The New York Times and Wall Street Journal , and his climate engineering research was endorsed by Nobel Laureates as a top climate change response strategy. Professor Bickel has extensive industry experience, having previously served as Senior Engagement Manager and Co-Director of Client Education at Strategic Decisions Group (SDG), where he remains on the Board of Directors. His consulting spans oil/gas, energy trading, and financial services sectors. He has received recognition as a Fellow of the Society of Decision Professionals and contributed to the Copenhagen Consensus on Climate Project. His teaching extends to executive education through Texas Executive Education and McCombs School of Business. Research highlights include novel methods for probabilistic dependence modeling, value-of-information analysis in shale reservoirs, and critiques of risk assessment tools like heat maps. His climate engineering work emphasizes economically viable solar radiation management strategies.
Petter N. Kolm serves as a Clinical Professor of Mathematics and Program Director at New York University, with his office located in Warren Weaver Hall (520). He can be contacted at petter.kolm@nyu.edu or 212-998-4855, and holds an editorial board position at the Journal of Portfolio Management. His academic qualifications include: Doctorate in Mathematics from Yale University M.Phil. in Applied Mathematics from the Royal Institute of Technology in Stockholm M.S. in Mathematics from ETH Zurich Dr. Kolm's research centers on quantitative finance, with primary focus areas including quantitative trading strategies, delegated portfolio management, financial econometrics, risk management, and optimal portfolio strategies. His work integrates advanced mathematical modeling with practical investment applications, bridging theoretical frameworks and real-world market dynamics through rigorous empirical analysis. Analysis of his 15 most recent publications reveals consistent emphasis on portfolio optimization techniques—particularly Bayesian methods and the Black-Litterman model—alongside significant contributions to algorithmic trading systems, factor-based equity portfolio construction, and machine learning applications for financial sentiment analysis. His scholarly output demonstrates evolution from foundational portfolio theory toward contemporary computational finance challenges. As Program Director, Dr. Kolm oversees academic programming and likely mentors graduate students in quantitative finance, though specific advisee details are not documented. His prior industry role at Goldman Sachs Asset Management provided direct experience in developing hedge fund strategies, informing his applied research approach. Dr. Kolm's professional trajectory includes significant industry engagement through his tenure in Goldman Sachs' Quantitative Strategies Group, where he developed quantitative investment systems. His current academic leadership position leverages this practical experience to shape quantitative finance education and research at NYU.
Tomasz Strzalecki is a Professor in the Department of Economics at Harvard University. His research centers on decision theory, with a focus on ambiguity aversion , temporal preferences , stochastic choice , and bounded rationality . He earned his PhD in Economics from Northwestern University in 2008. Education: PhD in Economics (2008), Northwestern University His scholarly work spans theoretical and applied economics, including key contributions to random utility models , dynamic decision-making , and neuroeconomic modeling . Recent publications, such as Stochastic Choice Theory (2025) and Variational Bayes and non-Bayesian Updating (2024), reflect his ongoing exploration of Bayesian inference and behavioral deviations. Earlier work in Econometrica and American Economic Review established foundational models for choice aversion , time inconsistency , and ambiguity evaluation . Tomasz’s research has been published in top journals like Econometrica , American Economic Review , and Proceedings of the National Academy of Sciences , covering themes such as probabilistic sophistication , decision timing , and collective action in development economics. His co-authors include prominent economists like Drew Fudenberg, Mira Frick, and Larry Epstein.
Amitai Shenhav is an Associate Professor at the University of California, Berkeley, specializing in Cognitive Neuroscience. His research explores the neural and computational mechanisms underlying motivation, affect, decision-making, and cognitive control, as detailed on the Shenhav Lab website . Ph.D., Harvard University Key research themes include: Explaining motivated behavior through affective gradients Modeling decision-making with mutual inclusivity and value integration Investigating cognitive control allocation under varying motivational contexts Understanding neural dynamics in target-distractor interactions Recent publications (2025–2024) highlight his work on value-based decision-making, effort allocation, and computational models of cognitive control. These studies often bridge behavioral experiments with neural recordings and theoretical frameworks. Scientific contributions include: NSF CAREER Award (2021) for research on motivation in cognition He mentors students and collaborators in his lab, focusing on psychophysiological experiments, computational modeling, and neuroeconomic paradigms. His work intersects with psychology, neuroscience, and artificial intelligence, particularly in attention training applications.
Philipp Afeche is a Professor of Operations Management and Statistics at the Rotman School of Management, University of Toronto. His research bridges operations and marketing/economics, focusing on revenue management, pricing strategies, and service design in congestion-prone systems like healthcare and transportation. He holds a BA from the University of St. Gallen and MS/PhD degrees from Stanford University. Afeche has been recognized with the 2014 Best Paper Award (MSOM) and the 2018 Roger Martin Teaching Award. Education: BA, University of St. Gallen, Switzerland MS, Stanford University, USA PhD, Stanford University, USA Research Interests: Afeche explores optimization challenges in dynamic service systems, including pricing under uncertainty, strategic customer behavior in queues, and platform design for shared mobility systems. His work integrates queueing theory, game theory, and empirical analysis to address real-world operational inefficiencies in healthcare delivery and transportation networks. Recent studies focus on ride-hailing market mechanisms and bipartite matching systems. Awards: 2014 Best Paper Award, Manufacturing & Service Operations Management 2018 Roger Martin Award for Excellence in Teaching Grants & Editorial Roles: Editor for Management Science and Operations Research, with funding reviews for agencies in Canada, Hong Kong, Israel, and the US. Past chair of the Service Management SIG for MSOM Society. Labs/Teams: Active in Rotman's Operations Management group and collaborates with industry partners on supply chain optimization and revenue management projects.
George Skiadopoulos is a Professor of Finance at the University of Piraeus (Department of Banking and Financial Management) and Queen Mary University of London (School of Economics and Finance). He serves as Director of the Institute of Finance and Financial Regulation (IFFR) and holds an Honorary Senior Visiting Fellowship at Bayes Business School, City University of London. His research focuses on asset pricing, commodities, financial derivatives, climate finance, and ESG integration. He has published in prestigious journals like Management Science and Journal of Financial and Quantitative Analysis, and his work influences policy at institutions like the European Securities Markets Authority (ESMA). Education: PhD in Finance from the University of Warwick, M.Sc. in Mathematical Economics from LSE, and a Ptychion in Economics from Athens University of Economics and Business. He has advised financial institutions globally and received grants from the Chicago Mercantile Exchange and others. His notable award is the 2018 German Finance Association best paper prize for work on transaction costs and stock returns. He has also contributed to executive training and policy discussions on climate-related financial risks.
Anna Mathia Klawonn is an Associate Professor affiliated with three units at Aarhus University: the Danish Research Institute of Translational Neuroscience (DANDRITE), the Department of Biomedicine, and the Department of Molecular Biology and Genetics - Neurobiology. As a group leader at DANDRITE, she explores neural circuits and immune-to-brain signaling mechanisms regulating affective states through transgenic strategies and neurocircuitry techniques. Neuroscience Neuroimmunology Immune-to-Brain Signaling Affective Disorders Her research focuses on understanding how brain circuits and glial cells (microglia and astrocytes) contribute to affective states in both health and disease. Current projects investigate mechanisms in major depressive disorder and Parkinson's disease, emphasizing prostaglandin signaling, nicotinic receptor function, and striatal neuron modulation. Recent publications highlight her work in molecular neuroscience, neuropharmacology, and behavioral neuroscience. Key themes include cholinergic transmission in motivation, neuroimmune interactions in aversion, and reward/aversion circuitry. Her studies employ advanced neurocircuitry methods and transgenic models. In teaching, Klawonn is course responsible for the 3rd-semester Neuroscience course (10 ECTS) in the medical bachelor program. She actively engages in didactic development, frequently speaking about student motivation, flipped learning, and challenge-based learning. Klawonn leads the Klawonn Group at DANDRITE, with lab and office spaces in the Skou Building (Høegh-Guldbergs Gade 10, Aarhus C). Her work involves collaborations across neuroscience, neuroimmunology, and affective disease research.
Nicolas Davidenko is an Associate Professor in the Department of Psychology at the University of California, Santa Cruz (UCSC). He leads the High Level Perception Lab, focusing on behavioral and computational studies of human perception, particularly face recognition, spatial orientation, and visual ambiguity. His work emphasizes 'top-down' processes like attention and expectations. Davidenko holds a Ph.D. in Psychology from Stanford University (2006), an M.S. in Statistics from Stanford (2004), and an A.B. in Mathematics from Harvard (1998). His research explores how humans perceive and interpret complex visual information, including studies on illusions, virtual reality, and misophonia. He has developed parametric models of faces to study memory encoding and drawing accuracy. Notable achievements include a Top-10 Finalist placement in the 2015 Best Illusion of the Year Contest for his 'Mind-controlled motion' research. He teaches courses such as PSYC 121 (Perception), PSYC 139K (Face Recognition), and advanced cognitive research seminars. Davidenko also runs the CSASS Matlab Workshops, training researchers in statistical tools. His lab includes graduate students and postdocs, with alumni like Jennifer Day (Ph.D. ’19) and Pat Samermit (Ph.D. ’18). Recent projects include investigations into vection in VR environments, cross-sensory modulation of aversive sounds, and time perception in virtual reality. His work bridges cognitive psychology, neuroscience, and computational modeling, contributing to understanding how perception shapes human interaction with the environment.
Jim Luedtke is a Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on operations research, integer programming, and stochastic optimization methods for solving discrete and uncertain decision problems. Educational Background: BS in Industrial Engineering from University of Wisconsin-Madison MS in Operations Research from Georgia Institute of Technology PhD in Industrial and Systems Engineering from Georgia Institute of Technology Postdoctoral Research at IBM T.J. Watson Research Center His work spans applications in power systems optimization, healthcare analytics, and network design, with particular emphasis on developing cutting-edge algorithms for chance-constrained and multistage stochastic programming problems. Recent publications demonstrate strong focus on Benders decomposition techniques, Lagrangian dual methods, and distributionally robust optimization frameworks. Scientific Awards: NSF CAREER Award (2010) for "Risk Management via Stochastic Programming: Models, Computation, and Applications"
