Portrait of Qidi Peng
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  • Degrees
    PhD, Applied Mathematics, Lille 1 University of Science and Technology
    MS, Applied Mathematics, Lille 1 University of Science and Technology
    BS, Statistics, Wuhan University
  • Research Interests
    Statistical inferences, Stochastic differential equations, Stochastic modeling, Simulation, Machine learning, Approximation theory, Graph theory

Hired in July 2012, Qidi Peng is a research assistant professor at the Institute of Mathematical Sciences at Claremont Graduate University. His research interests are in multifractional process, statistical inferences, Malliavin calculus, stochastic differential equations, stochastic modeling, simulation, machine learning, approximation theory, and graph theory.

In October 2015, Peng hosted the special session on Stochastic Modeling and Statistical Inference at the American Mathematical Society (AMS) Fall Western Sectional Meeting at California State University, Fullerton. In addition, Peng has given invited talks at the Southern California Applied Mathematics Symposium, Claremont Mathematics Weekend, the American Mathematical Society’s Joint Mathematics Meetings, and more.

Peng has been an editorial board member of the Operation Research and Applications: An International Journal since 2014 and was previously a referee for Applications and Applied Mathematics: An International Journal. In 2011, Peng worked as a mathematics and statistics consultant at the SOFT SOLUTIONS Company in Villeneuve d’Ascq, France, analyzing observed data in order to forecast the supermarket inventory sales.

Peng is fluent in several programming languages, including C++, MATLAB, R, Maple, Scilab, SAS, and SPSS.

Co-authored with Asuman G. Aksoy, Monairah Al-Ansari and Caleb Case. “Subspace Condition for Bernstein’s Lethargy Theorem.” Turkish Journal of Mathematics, accepted. [arXiv] (2016).

Co-authored with Sanjutka Bhownick, Sunil Kumar, John D. Matyjas and Moein Parsinia. “Gender Assignment for Directional Full-Duplex FDD Nodes in a Multihop Wireless Network.” AdHocNets 2016, accepted.

Co-authored with Sixian Jin and Henry Schellhorn. “Estimation of the pointwise Hölder exponent of hidden multifractional Brownian motion using wavelet coefficients. Statistical Inference for Stochastic Processes.” [arXiv: long version]

Co-authored with Sixian Jin and Henry Schellhorn. “A Representation Theorem for Smooth Brownian Martingales.” Stochastics 88 no. 5 (2016): 651–79, [arXiv].

Co-authored with Sixian Jin and Henry Schellhorn. “Fractional Hida-Malliavin Derivatives and Series Representations of Fractional Conditional Expectations.”  Communications on Stochastic Analysis 9 no. 2 (2015): 213–38. [arXiv: long version].

Co-authored with Chiu-Yen Kao, Henry Schellhorn and Lu Zhu. “A New Algorithm to Simulate the First Exit Times of a Vector of Brownian Motions, with an Application to Finance.” Journal of Applied Probability and Statistics 10 no. 2 (2015): Pages 41–65. [arXiv].

Statistical Learning
Financial Time Series
Discrete Mathematical Modeling
Stochastic Processes