New course for spring 2021: Optimization Methods in Finance


Dec 15, 2020
New course for spring 2021: Optimization Methods in Finance By Shawn Hutchins

The Master of Industrial Engineering program at Rice University is offering the course Optimization Methods in Finance (INDE 567) for spring 2021.

The new course is open to undergraduate and graduate students in the Brown School of Engineering who have met the prerequisites Multivariable Calculus (MATH 212) and Introduction to Engineering Computation (CAAM 210).

Optimization models are playing an increasingly important role in financial decisions. They are tools used to analyze big data associated with complex global markets, define the best products and processes, and solve problems that constrain short-, mid-and long-term business goals. Many computational finance problems ranging from asset allocation to risk management, arbitrage and asset pricing, mean-variance models and portfolio optimization can be solved using modern optimization techniques.

This course, which is led by Leticia Velazquez, lecturer in the Master of Industrial Engineering program and program administrator in the Tapia Center at Rice University, discusses fundamentals of financial optimization and teaches students to apply models and algorithms for solving linear, quadratic, integer, and stochastic optimization models encountered in financial and data science applications.

To register for Optimization Methods in Finance, contact Dr. Leticia Velazquez at Spring registration is open from January 25 - 29, 2021. The deadline to add spring classes is Friday, February 5, 2021.

Before joining Rice in 2013, Velazquez was a professor in the Department of Mathematical Sciences and director of the Computational Science Program at the University of Texas at El Paso (UTEP). In teaching Optimization Methods in Finance, she will draw from her experiences in teaching and research. Velazquez specializes in developing, implementing, and analyzing large-scale nonlinear programming algorithms to support decisions by improving the accuracy of predictive solutions. She is currently using highly nonlinear problems to analyze epidemiological control strategies, such as those used in the COVID-19 pandemic and associated economic effects.

Working as a Sloan Research Fellow, Velazquez completed her postdoctoral fellowship in computational science and engineering at Harvard Medical School. She received a doctorate in computational and applied mathematics in 1999 and a professional master’s in electrical engineering in 1993, both from Rice University. In 1991, she earned a bachelor’s of science in electrical engineering from the University of Texas, El Paso.