Finance/Financial Engineering

Finance/Financial Engineering

Faculty Research Interests

convex optimization, robust optimization, combinatorial optimization, computational finance, complex systems, systemic risk, information theory

algorithms for linear, quadratic, semidefinite, convex and general nonlinear programming, network flows, large sparse systems, and applications in robust optimization, finance, and imaging

quantitative finance, derivatives valuation, volatility models, risk management, philosophy of modeling

financial engineering and risk management; Markov decision processes and duality based on information relaxations; machine learning for operations research

quantitative finance, asset pricing, derivatives, risk measures, real estate, applied probability, empirical finance

behavioral finance, portfolio choice, asset pricing, and risk management when investors are not fully rational; applied probability topics such as stochastic control and optimal stopping

financial Engineering: (i) derivatives pricing, e.g. employee stock options, exchange-traded funds, credit derivatives, (ii) optimal dynamic/static strategies for hedging, trading, and risk management.

stochastic control, mathematical finance/financial engineering, stochastic analysis

algorithmic/program trading, machine learning/statistical modeling, computational/mathematical finance, statistical signal processing, optimization