Kay Giesecke is Professor of Management Science & Engineering at Stanford University.
He is the Founder and Director of Stanford's Advanced Financial Technologies Laboratory, the Director of the Mathematical and Computational Finance Program and a member of the Institute for Computational and Mathematical Engineering. He has been on the Stanford faculty since 2005, and has held visiting positions at Cornell, UCLA, and the International Monetary Fund. He serves on the Governing Board and Scientific Advisory Board of the Consortium for Data Analytics in Risk and the Council of the Bachelier Finance Society. He is the founder and organizer of Stanford’s annual AI in Fintech Forum, which regularly draws over 300 delegates. Kay is Editor of Management Science (Finance Area) and Associate Editor for Operations Research, Mathematical Finance, Journal of Financial Econometrics, SIAM Journal on Financial Mathematics and several other leading journals.
Kay is a financial technologist whose research agenda is driven by significant applied problems in areas such as investment management, risk analytics, lending, and regulation, where data streams are increasingly large-scale and dynamical, and where computational demands are critical. He develops and analyzes statistical machine learning methods to make explainable data-driven decisions in these and other areas as well as efficient numerical algorithms to address the computational issues arising in this context. The methods and tools support, for example, sound and transparent investment, trading, lending and servicing decisions, measuring and controlling risk, surveilling financial markets, and assessing the impact of climate change on markets and institutions.
Kay is Founder, Chairman and Chief Scientist of Infima Technologies, a venture-backed capital markets SaaS company that provides transformative AI solutions to fixed-income market participants, helping them uncover attractive opportunities and avoid risks. Infima’s technologies are based on Kay’s pioneering academic work on large-scale deep learning for borrower behavior.
Kay has published more than 50 research articles in leading academic journals. He has co-authored several US patents, some of which underpin commercial investment analytics systems widely used in the financial industry. Kay’s research has won several prizes including the JP Morgan AI Faculty Research Award (2019), the SIAM Financial Mathematics and Engineering Conference Paper Prize (2014), the Fama/DFA Prize for the Best Asset Pricing Paper in the Journal of Financial Economics, and the Gauss Prize of the Society for Actuarial and Financial Mathematics of Germany (2003). His research is supported by the National Science Foundation and several leading financial institutions.
Kay has supervised 24 doctoral dissertations, with graduates accepting faculty positions at Wharton, UC Berkeley, Oxford, NYU and other leading research universities or taking leadership positions at firms such as Goldman Sachs, JP Morgan, Google, and Morgan Stanley.
Kay is an award-winner teacher who has developed innovative new courses at the intersection of finance and technology. He is the Faculty Lead for Financial Analytics MS track. He has created and led several successful executive education programs that have attracted industry leaders from across the world.
Kay advises several venture-backed financial technology startup companies and has been a consultant or advisor to banks, investment managers, software companies, governmental agencies, and supranational organizations.
Kay received his doctorate in 2001 from Humboldt Universität zu Berlin where he was a fellow of the Deutsche Forschungsgemeinschaft.