Bio:

Ioannis Karatzas obtained his Apolyterion at the Ionideion Gymnaseion in Piraeus, his Diploma at the Technical University of Athens -- and a Ph.D. degree in Mathematical Statistics at Columbia University, under the supervision of Dr. Vaclav E. Benes (Bell Labs). He is the Eugene Higgins Professor of Applied Probability in the Department of Mathematics at Columbia. He has had a long-standing association with the research group of the investment firm INTECH at Princeton, NJ.

He works and publishes in Probability, Stochastic Control, Sequential Analysis, Mathematical Economics and Finance. He is considered one of the founding fathers of the field that came to be known as "Mathematical Finance". He and his co-authors established some of the first and fundamental results on free boundary problems in stochastic control; on singular control and its relations to optimal stopping; on dynamic allocation; on portfolio optimization in non-Markovian settings; on questions of equilibrium in economics and finance; on the study of markets under incompleteness, portfolio constraints or transaction costs; on stochastic games of control and stopping, and on strategic games; on stochastic portfolio theory, including a deep mathematical study of arbitrage; on backward stochastic equations; on the optimal detection of change-points; on systems of Brownian particles interacting through their ranks; and on many other topics.

He has co-authored with Steven E. Shreve the book "Brownian Motion and Stochastic Calculus" and the monograph "Methods of Mathematical Finance", both published by Springer-Verlag and both seminal references in their respective fields. His 25 former Ph.D. students are on the faculties of Universities such as Boston, Brown, Caltech, Columbia, CUNY, NYU, Oxford, Paris, Peking, Princeton, Reykjavik, Washington, California and Texas, or in various industrial positions. He built an enormously successful Master's program on the Mathematics of Finance at Columbia, now in its fifteenth year.

In addition to supervising graduate students and junior scholars directly, he has influenced many researchers of successive "generations" in the fields of Mathematical Finance and Stochastic Analysis, who have loved reading his extremely well and clearly written work or who have received helpful advice by interacting with him. There is an enormous amount of respect in the Stochastic Analysis and Mathematical Finance communities for the type of effort and energy he has put into educating future generations of scholars at enviable levels of scientific rigor, and one of the objectives of the conference is to show gratitude and honor that effort.

Bio:

Dilip Madan is Professor Emeritus of Mathematical Finance at the Robert H. Smith School of Business. Currently he serves as a consultant to Morgan Stanley, and Norges Bank Investment Management. He is a founding member and Past President of the Bachelier Finance Society.

He received the 2006 von Humboldt award in applied mathematics, was the 2007 Risk Magazine Quant of the year, received the 2008 Medal for Science from the University of Bologna, held the 2010 Eurandom Chair, was inducted into the Circle of Discovery of the College of Computer, Mathematical and Natural Sciences in 2014, and is the IAQF Financial Engineer of the Year 2021.

He has published over 200 papers and serves on the Advisory Board of Frontiers of Mathematical Finance and as a Director of the Scientific Association of Mathematical Finance.

Bio:

Professor Glasserman's research and teaching address risk management, quant finance, Monte Carlo simulation, statistics and operations. Prior to joining Columbia, Glasserman was with Bell Laboratories; he has also held visiting positions at Princeton University, NYU, and the Federal Reserve Bank of New York. In 2011-2012, he was on leave from Columbia and working at the Office of Financial Research in the U.S. Treasury Department, where he continues to serve as a part-time consultant.

Paul was named the 2020 Financial Engineer of the Year by the International Association for Quantitative Finance. His publications include the book Monte Carlo Methods in Financial Engineering (Springer, 2004), which received the 2006 Lanchester Prize and the 2005 I-Sim Outstanding Publication Award. Paul is a past recipient of the National Young Investigator Award from the National Science Foundation (1994 - 99), IBM University Partnership Awards (1998 - 2001), the TIMS Outstanding Simulation Publication Award (1992), the Erlang Prize (1996), an IMS Medallion from the Institute of Mathematical Statistics (2006), and a fellowship from the FDIC Center for Financial Research (2004). He received the 2004 Wilmott Award for Cutting-Edge Research in Quantitative Finance and Risk Magazine's 2007 Quant of the Year Award, and he received a U.S. patent for an option pricing method. He was named an INFORMS Fellow in 2008. He is also a recipient of the Dean's Award for Teaching Excellence (1994, 2000) and the Saul Gass Expository Writing Award (2016). Paul serves on the editorial boards of Operations Research, Mathematical Finance,  and Stochastic Systems.

Paul was senior vice dean of Columbia Business School in 2004-2008 and served as interim director of the Sanford C. Bernstein & Co. Center for Leadership and Ethics in 2005-2007. He chairs the Financial and Business Analytics center in Columbia’s Data Science Institute.

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Bio:

Xunyu Zhou is the Liu Family Professor of Financial Engineering and the Director of Nie Center for Intelligent Asset Management at Columbia University in New York. His current research focuses on developing a foundational theory for continuous-time reinforcement learning and its applications to financial decision making. Previously, he has worked on quantitative behavioral finance, time inconsistency and stochastic control. 

Zhou is known for his work in indefinite stochastic LQ control theory and application to dynamic mean-variance portfolio selection, in asset allocation and pricing under cumulative prospect theory, and in general time inconsistent problems. He has addressed the 2010 International Congress of Mathematicians, and has been awarded the Wolfson Research Award from The Royal Society (UK), the Outstanding Paper Prize from the Society for Industrial and Applied Mathematics, the Humboldt Distinguished Lecturer and the Alexander von Humboldt Research Fellowship. He is both an IEEE Fellow and a SIAM Fellow. He was awarded Distinguished Faculty Teaching Award at Columbia University in 2023. 

Zhou received his PhD in Operations Research and Control Theory from Fudan University in China in 1989. He was the Nomura Professor of Mathematical Finance and the Director of Nomura Center for Mathematical Finance at University of Oxford during 2007-2016 before joining Columbia.

Bio:

Leif B. G. Andersen is the Global Co-Head of The Quantitative Strategies & Data Group (QSDG) at Bank of America, and is an adjunct professor at NYU’s Courant Institute of Mathematical Sciences and at CMU’s Tepper School of Business. He holds MSc's in Electrical and Mechanical Engineering from the Technical University of Denmark, an MBA from University of California at Berkeley, and a PhD in Finance from Aarhus Business School.

Having started his career as a robotics engineer at Bosch GBMH in Stuttgart, Leif moved to Finance in 1993 and has now worked for more than 30 years as a quantitative researcher in the global markets area.

He was IAQF’s Financial Engineer of the Year in 2023, and a recipient of Risk Magazine’s 2001 and 2018 Quant of the Year Awards. Leif has authored influential research papers and books in all areas of quantitative finance, including the popular 3-volume monograph Interest Rate Modeling, co-authored with Vladimir Piterbarg. He is an Associate Editor of Journal of Computational Finance and Mathematical Finance.

Bio:

Louis Scott has retired from the Federal Reserve Bank of New York and is running a consulting business.  He spent 17 years in investment banking with Morgan Stanley and UBS, and 10 years with the New York Fed.  His various roles included market risk models, market risk management, model risk management, quantitative analytics, and derivative pricing.  He also served as an adjunct professor for the Financial Engineering programs at NYU and Fordham.  Prior to moving into industry, he was a finance professor at University of Illinois and University of Georgia.

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Bio:

Chris started his career on Wall Street over 20 years ago after studying physics. He received his AB from Harvard and PhD from Stanford University, where his thesis research used general relativity to devise ways to observe the mass and angular momentum of black holes. In his first job on Wall Street at Morgan Stanley, he built a firmwide Value-at-Risk model that was the first to use an equity factor model and later created automated equity trading systems. Chris then gained significant hedge fund experience, serving as Chief Risk Officer for two prominent hedge funds: Eton Park and QVT. After the financial crisis, he joined Goldman Sachs where he was an MD primarily responsible for Credit Risk Modeling including Basel Capital and regulatory CVA,Liquidity Risk Modeling and Hedge Fund Risk. More recently, he branched into crypto, serving as the MD in charge of Market Risk and Quantitative Modeling for BlockFi. Now, he enjoys teaching finance to Masters students in Financial Engineering.

Bio:

Mikhail Smirnov is a Senior Lecturer in Discipline in the Department of Mathematics and was Director of the Mathematics of Finance program in 1998-2012. His research interests include Quantitative Portfolio Management, Quantitative Investment Strategies, and Risk Measurement. He holds a Ph.D. from Princeton University.

Bio:

Amal Moussa is a Managing Director at Goldman Sachs where she leads the Single Stocks Exotic Derivatives Trading team in New York. 

In addition to her work in Markets, Amal is an Adjunct Professor at Columbia University where she teaches a graduate course on volatility modeling in the Mathematics of Finance Masters program and in the Financial Engineering Masters program.

Amal has a Ph.D. in Statistics, obtained with distinction, from Columbia University. 
Prior to her Ph.D., Amal graduated with a Masters in Mathematical Finance from Sorbonne Université (former Paris VI) and a Grande Ecole engineering degree from Télécom Paris.

Amal is an advisory board member of the Center for Artificial Intelligence in Business Analytics and Financial Technology at Columbia University. She is an active member of the Women in Trading network at Goldman Sachs and she is involved in the mentorship of young students and professionals across the financial industry