Yaroslav I. Molkov, Ph.D.
Assistant Professor of Applied Mathematics
Indiana University - Purdue University Indianapolis
My teaching experience during my academic career consists of three parts. First, I instructed students during master training at the University of Nizhniy Novgorod in 1996. I taught Theory of Oscillations and Waves and supervised corresponding lab studies. The second part of my teaching experience is the co-mentoring a number of PhD and MS students during my work in the Institute of Applied Physics RAS and my postdoctoral training and research in Drexel University College of Medicine. Now I am teaching basic courses for undergraduate students in the Department of Math at IUPUI.
Students I co-mentored in the past are:
I am currently co-mentoring the following students.
In Fall 2011 I am teaching Analytic Geometry and Calculus I. This course is the first in a sequence of 3 courses for Math, Science and Engineering majors.
It is my basic research strategy to get students involved in every aspect of the study being conducted, including, but not limited to, the development of a problem, formalization of known experimental facts, running of simulations, performing analysis, and writing sections of a paper. By assisting with the completion of these tasks the students can identify areas in which they excel. Although this requires a significant initial effort on my part I find that it provides a great reward after some time has passed. Students benefit from such an approach by obtaining skills in a wide range of disciplines that will benefit them throughout their academic careers.
I enjoy working with students and hope to have more teaching experience in the near future. It is my goal to develop at least two classes based on my research experience. One class, which is intended for undergraduate students, is "Theoretical foundations for reconstruction of dynamical systems from time-series". This class will include a wide range of approaches from areas such as differential equations, dynamical systems, nonlinear dynamics, statistics, information theory, reverse problems and artificial neural networks.
It may be appropriate for this class to have an extension titled: "Approaches to neurophysiologic data interpretation and assimilation", for biologically inclined students. Because neurophysiologic data is at most represented by recordings of integrated or individual neural activity, i.e. by time-series, which are in general non-stationary, noisy, not continuous and irreproducible, handling this data is a challenging task that demands additional knowledge of experimental conditions, the specificity of a preparation used, electrophysiology in general, etc.
Another class I plan to develop for graduate students in the field of Bioinformatics and/or Neuroscience is "Mathematics of neural control of breathing" which unites our current understanding of the mammalian central respiratory system, its "microstructure" and interactions with other structures as it seen from the point of view of applied mathematics.
In addition, I am also very enthusiastic about teaching basic undergraduate mathematical courses. I believe that the best way to learn mathematics is when its presentation includes examples from real life. I consider very important to stimulate student's interest in the course and, thus, as a part of interactive teaching process I am going to present different applications of every particular topic. I hope that the diversity of my research interests can create a basis for the increased student's motivation.