Stanford University
Showing 101-120 of 524 Results
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Junting Duan
Ph.D. Student in Management Science and Engineering, admitted Autumn 2020
BioJunting Duan is a PhD candidate in the Department of Management Science and Engineering (MS&E) at Stanford University. Prior to joining Stanford, she received her B.S. in Mathematics and Applied Mathematics from Peking University in 2020.
Junting's research interests lie broadly in data-driven decision-making, focusing on statistical inference and machine learning, with applications to causal inference and finance. Her research develops new methodologies with rigorous statistical foundations that enable reliable decision-making with complex and imperfect data, and lies at the intersection of (1) statistical learning for high-dimensional data; (2) causal inference; and (3) machine learning for finance and risk management. Her work has been recognized through publications and revisions at top journals including Management Science and the Journal of Econometrics, as well as invitations to present at major conferences such as the American Economic Association Annual Meeting, the NBER-NSF Time-Series Conference, the NBER Forecasting & Empirical Methods Conference, and the INFORMS Annual Meeting.
Visit her personal website for more details: https://juntingduan.com. -
Charles (Chuck) Eesley
Professor of Management Science and Engineering
Current Research and Scholarly InterestsMy research focuses on the influence of the external environment on entrepreneurship. I investigate the types of environments that encourage the founding of high growth, technology-based firms. I build on previous literature that explains why entrepreneurs are successful and my major contribution is to demonstrate that institutions matter. I show that effective institutional change influences who starts firms, not just how many firms are started.
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Kathleen Eisenhardt
Stanford W. Ascherman, M.D. Professor in the School of Engineering, Emerita
Current Research and Scholarly InterestsTheoretical approaches: Cognition, complexity, learning, and organizational theories
Methods: Multi-case Theory Building as well as machine learning, simulation, and econometrics
Recent research: Business model design, strategy as "simple rules" heuristics, strategic interaction in novel markets and ecosystems, strategy in marketplaces, communities v. firm organizational forms
