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Hello, and welcome to my website! My name is Adam Telatovich, and I am in my 5th year as a graduate student in Penn State’s math department, planning to graduate with my PhD in May 2018. I am currently on the job market and will be attending the annual Joint Math Meetings in San Diego this January. If you are an employer, please feel free to contact me for an interview — I would be happy to meet with you.

Contact information:

Adam E. Telatovich, (646) 764-3158, aet156@psu.edu, Office 430 McAllister Building, Dept of Mathematics, Penn State University, University Park PA 16802

Here is a brief summary of my research and teaching background.

I have taught four full years of university-level mathematics at Penn State University as a graduate TA. I taught five different courses — college algebra 1 and 2, linear algebra, differential equations, and multivariable calculus — to over 500 students in total. I received two departmental awards for outstanding graduate teaching and was recently nominated for the prestigious university-wide Harold F. Martin award for outstanding graduate teaching. I enjoy teaching. I wanted to be a teacher since the 8th grade, so I have been living my dream for the past four years. I enjoy interacting with students and sharing the fruits of my learning. You can find some unsolicited reviews from former students on ratemyprofessor.

My research centers on the areas of numerical analysis, differential equations and probability. I study numerical methods for solving stochastic (ordinary) differential equations (SDE’s). Kloeden and Platen have a great reference book on this subject. This area of study is relatively new and builds on the well-studied numerical analysis of ordinary differential equations. Since the solution of an SDE is a stochastic process, one can look at path-wise “strong” approximations of the solution, or “weak” moment approximations of the solution. There are thus two notions of “approximate solution” for SDE’s, whereas for ODE’s there is only one. How do you analyze the convergence of solutions? There are Ito Taylor formulas, analogous to the familiar Taylor formula, which we can use. I study “operator splitting” methods, analogous to the well-known Strang splitting methods which Gilbert Strang came up with in the 1960’s. He had the idea to split a complicated ODE system into two simpler ODE systems and solve them back-to-back. I focus on the Langevin equation and the equation for dissipative particle dynamics as my main examples for applying the methods. Operator-splitting methods form a class of methods with different convergence rates. Generally, the higher the order, the more complicated the method. Testing the accuracy of the methods is computationally expensive, but fortunately they can be done in parallel, and I frequently submit jobs to the Penn State cluster. I have one paper published from an REU experience, one paper under review, and one paper in progress.

In my free time I like to spend time with my family, play piano and play tennis.