Cornell Financial Engineering Faculty Speakers : 2022 Future of Finance Conference Speaker Profiles
Cornell Financial Engineering Manhattan’s 2022 Future Of Finance Conference Speaker Profile: Sasha Stoikov Head of Research Cornell Financial Engineering Manhattan
I love to solve math problems, especially when they have practical applications. Lately, I have been working on high-frequency trading and music recommendation algorithms. What do these projects have in common? They use data – lots of it – to extract value. We live in a golden era of data. Tremendous opportunities and dangers are ahead. This is going to be very exciting!
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Cornell Financial Engineering Manhattan’s 2022 Future Of Finance Conference Speaker Profile: Andrew Chin Head of Quantitative Research & Chief Data Scientist For Alliance Bernstein
Andrew Chin is Head of Quantitative Research and Chief Data Scientist for AB. He is responsible for optimizing the quantitative research infrastructure, tools and resources across the firm’s investing platforms. Chin also leads the firm’s data science strategy. To harness big data and leverage machine learning to improve decision-making across the organization. From 2009 to 2021, Chin was chief risk officer. Where, in addition to his quantitative research responsibilities, he led all aspects of risk management and built a global team. To identify, manage and mitigate the various risks across the organization.
Moreover, Chin held various quantitative research. Risk management. And portfolio management roles in New York and London since joining the firm in 1997. Before joining AB, Chin spent three years as a project manager and business analyst in Global Investment Management at Bankers Trust. Lastly, Chin holds a BA in math and computer science, and an MBA in finance from Cornell University.
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Cornell Financial Engineering Manhattan’s 2022 Future Of Finance Conference Speaker Profile: Gordon Ritter 2019 Quant of the Year & Cornell Financial Engineering Adjunct Professor
Gordon Ritter completed his PhD in mathematical physics at Harvard University in 2007. His publications while at Harvard were in quantum field theory, differential geometry, quantum computation and abstract algebra, including a well-known simplicity theorem for Kac-Moody groups and a mathematically rigorous treatment of Euclidean QFT on Riemannian manifolds. Prior to Harvard he earned his Bachelor’s degree with honors in Mathematics from the University of Chicago.
Dr. Ritter currently teaches at Columbia, NYU, University of Chicago and the Baruch MFE program. His academic research is on portfolio optimization and statistical machine learning; his finance publications can be found in journals including Risk, the Journal of Portfolio Management. Journal of Financial Data Science, European Journal of Operations Research, and others.
He was named Buy-Side Quant of the Year in 2019!
(Researchers awarded the same honor in other years included Marcos Lopez de Prado, Alex Lipton, and Jean-Phillipe Bouchaud). In parallel with his teaching responsibilities, Dr. Ritter works full time in the industry. In 2019 he founded Ritter Alpha LP. A registered investment adviser running systematic absolute-return trading strategies across multiple asset classes and geographies, based on cutting-edge technology and rigorous applications of statistics and the scientific method to investment problems.
Prior to founding Ritter Alpha, he built a successful trading system from scratch at GSA Capital, a firm which won the Equity Market Neutral & Quantitative Strategies category at the Eurohedge awards four times. Prior to GSA, Dr. Ritter was a Vice President of Highbridge Capital Management (HCM) and a core member of the HCM statistical arbitrage group. Moreover, a small team sharing full discretion over systematic trading models that generated billions in profit for investors and directed trillions of dollars of trades across global equities, futures and options. Several senior members of the HCM statistical arbitrage group later joined Gordon at Ritter Alpha. Lastly, in his spare time Gordon enjoys scuba diving in Hawaii. In addition, is passionate about preservation of Native Hawaiian culture and marine ecosystems.
Cornell Financial Engineering Manhattan’s 2022 Future Of Finance Conference Speaker Profile: Andreea Minca Associate Professor Cornell University
Andreea Minca is an Associate Professor in the School of Operations Research and Information Engineering at Cornell University.
She holds degrees from Sorbonne University (PhD in Applied Mathematics) and Ecole Polytechnique (Diplome de l’Ecole Polytechnique).
In recognition of “her fundamental research contributions to the understanding of financial instability, quantifying and managing systemic risk, and the control of interbank contagion”, Andreea received the 2016 SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize. This award distinguishes contributions to the mathematical modeling of financial markets and is the highest early career distinction in the field of financial engineering and mathematics. Andreea is also a recipient of the NSF CAREER Award (2017), a Research Fellow of the Global Association of Risk Professionals (GARP) (2014), and an AXA Research Fund Awardee (2020). She serves on the editorial board of the SIAM Journal on Financial Mathematics.
She has held visiting appointments at Imperial College London, where she delivered the CFM-Imperial Distinguished Lectures in 2018, and at the London Business School, where she taught in the PhD program.
Research
Andreea Minca studies large systems under uncertainty, especially financial systems, and uses mathematical modeling to derive optimal policies that promote system stability. Her main work is on structural models for systemic risk, using networks to represent various types of interrelations. Furthermore, her work has seen publication in leading Financial Mathematics and OR journals such as Mathematical Finance, Finance & Stochastics, SIAM Journal of Financial Mathematics, Operations Research and Management Science.
In addition, her research relies on network analysis, stochastic analysis and game theory and led to a broad set of methodological advances. The study of financial contagion in large systems led to developments. Moreover, in the asymptotic analysis of diffusions on random graphs with inhomogeneous structures.
She pioneered the use of random graphs to model partial information about the interconnections in a financial system. Furthermore, she proposed a setup for optimization under uncertainty about network structure, with application to the management of financial crises.
Lastly, her work on bank runs led to the development of a theory on leverage under strategic uncertainty, when credit is provided by agents with heterogeneous beliefs. Moreover, this relies on the study of equilibria of games with a large number of heterogeneous players.
Current research interests
Her current interests include financial and economic network analytics. In addition, as well as clustering algorithms, which can be used to identify system vulnerabilities. Furthermore, she is interested in financial technologies. And in particular in the economic incentives and the design behind descentralized finance and stablecoins.
Recently she was selected as AXA Reseach Fund Awardee for her project to create technologies to mitigate risk in the wake of the Covid-19 Pandemic and strengthen global supply chains.
Lastly, on the methodological side, she is interested in reinforment learning in mean-field games.
Read more on her featured research on Complex Financial Systems.
Teaching interests
- Firstly, Financial Engineering.
- Secondly, Stochastic Modeling.
- Thirdly, Approximate Dynamic Programming.
- Lastly, Risk Management.
Teaching
- Financial Engineering with Stochastic Calculus I, ORIE 5600. Fall 2012, Fall 2013, Fall 2015, Fall 2016, Fall 2019, Fall 2020 (MEng) Syllabus
- Probability, Spring 2013, Spring 2014, Spring 2020, Spring 2021 (PhD) Syllabus
- Dynamic Programming (London Business School) Summer 2019 (PhD)
- Approximate Dynamic Programming, ORIE 6590, Spring 2019 (PhD)
- Financial Engineering with Stochastic Calculus II, ORIE 5610, Spring 2017(MEng) Syllabus
- Introduction to Engineering Stochastic Processes I (Undergraduate), Spring 2016
- Mathematics of Financial Systems (PhD), Spring 2015
- Risk Measures ORIE 6630, Fall 2014 (PhD) Syllabus
