Teaching

At UC Merced:	Fall 2022: Math 292 (Special Topics: Control Systems and Reinforcement Learning) Spring 2022: Math 140 (Mathematical Methods for Optimization) Fall 2021: Math 180 (Intro to Statistical Learning) Fall 2020: Math 180 (Intro to Statistical Learning) Spring 2020: Math 292 (Special Topics: Deep Learning) Fall 2019: Math 32 (Probability and Statistics) Spring 2018: Math 224 (Functional Analysis) Fall 2017: Math 292 (Special Topics: Deep Learning) Spring 2017: Math 181 (Stochastic Processes) Fall 2016: Math 180 (Intro to Statistical Learning) Spring 2016: Math 32 (Probability and Statistics) Spring 2015: Math 181 (Stochastic Processes) Fall 2014: Math 180 (Intro to Statistical Learning) Spring 2014: Math 222 (Graduate PDE II) Fall 2012: Math 32 (Probability and Statistics) Spring 2012: Math 292 (Special Topics: Stochastic Processes) Fall 2011: Math 32 (Probability and Statistics) Spring 2011: Math 222 (Graduate PDE II) Fall 2010: Math 221 (Graduate PDE I) Fall 2009: Math 32 (Probability and Statistics) Fall 2009: Math 121 (Advanced ODE and Beginning PDE) Fall 2008: Math 121 (Partial Differential Equations)
At the University of Utah:	Spring 2019: Math 5010/6805 (Intro to Probability) Fall 2018: Math 5010/6805 (Intro to Probability)
At Claremont McKenna College:	Spring 2008: Math 182 (Partial Differential Equations) Spring 2008: Math 31 (Calculus II) Fall 2007: Math 111 (Ordinary Differential Equations) Fall 2007: Math 31 (Calculus II)
At Columbia University:	Spring 2007: APMA E4101 (Dynamical Systems) Fall 2006: APMA E4301 (Numerical PDE) Spring 2006: APMA E4990 (Special topics: numerical methods for nonlinear wave equations) Fall 2005: APMA E4200 (Partial Differential Equations)

Photo taken by Scott Stehle, School of Natural Sciences, UC Merced on Nov. 17, 2010. In case you are curious, I am giving an example of eigenvalues and eigenfunctions for a non-self-adjoint problem.

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