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Curriculum Vitae

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Bio

Andreea Minca is an Associate Professor in the School of Operations Research and Information Engineering at Cornell University. She holds degrees from the University Pierre et Marie Curie - Sorbonne (PhD in Applied Mathematics) and the 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) and a Research Fellow of the Global Association of Risk Professionals (GARP) (2014). 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. She is particularly interested in structural models for systemic risk, using networks to represent various types of interrelations among financial institutions. Her work has been published in leading Financial Mathematics and OR journals such as Mathematical Finance, Finance & Stochastics, SIAM Journal of Financial Mathematics, Operations Research and Management Science.

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 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 the financial system. She proposed a setup for optimization under uncertainty about network structure, with application to the management of financial crises.

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. This relies on the study of equilibria of games with a large number of heterogeneous players.

Her current interests include machine learning and complex financial data , in particular developing methods of data aggregation in network structures as well as clustering algorithms, which can be used to identify system vulnerabilities. She is interested in the economic incentives and the design behind descentralized finance and stablecoins . On the methodological side, she is interested in approximate dynamic programming approaches to learning in large scale (mean-field) games.

Read more on her featured research on Complex Financial Systems.


Teaching Interests

Financial Engineering, Stochastic Modeling, Stochastic Control, Approximate Dynamic Programming, Risk Management.


Teaching

  • 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
  • Financial Engineering with Stochastic Calculus I, ORIE 5600, Fall 2012, Fall 2013, Fall 2015, Fall 2016, Fall 2019 (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
  • Probability ORIE 6510, Spring 2013, Spring 2014 (PhD) Syllabus
  • Introduction to Mathematical Finance, ORIE 4600, Fall 2011 (Undergraduate)



Contact Information

Andreea Minca
acm299 "at" cornell.edu
Cornell University
222 Rhodes Hall
Ithaca, NY 14853

(607) 255-9133 - phone
(607) 255-9129 - fax