The Department of Mathematics, Applied Mathematics and Statistics at Case Western Reserve University is an active center for mathematical research. Faculty members conduct research in algebra, analysis, applied mathematics, convexity, dynamical systems, geometry, imaging, inverse problems, life sciences applications, mathematical biology, modeling, numerical analysis, probability, scientific computing, stochastic systems and other areas.
The department offers a variety of programs leading to both undergraduate and graduate degrees in traditional and applied mathematics, and statistics. Undergraduate degrees are Bachelor of Arts or Bachelor of Science in mathematics, Bachelor of Science in applied mathematics, and Bachelor of Arts or Bachelor of Science in statistics. Graduate degrees are Master of Science and Doctor of Philosophy. The Integrated BS/MS program allows a student to earn a Bachelor of Science in either mathematics or applied mathematics and a master’s degree from the mathematics department or another department in five years. The department, in cooperation with the college’s teacher licensure program and John Carroll University, offers a program for individuals interested in pre-college teaching. Together with the Department of Physics, it offers a specialized joint Bachelor of Science in Mathematics and Physics.
Date posted: July 22nd, 2015
Tuesday, August 4, 2015 (2:00 p.m. in Yost 306)
Title: Stabilization of Rotor-Dynamic Vibrations by Means of Additive, Stochastic Noise
Speaker: Tyler Aust (Case Western Reserve University)
Advisor: Wojbor Woyczynski (Professor, Case Western Reserve University)
Abstract: Throughout the energy industry, rotating equipment serves a vital role. …Read more.
Date posted: July 15th, 2015
Monday, August 3, 2015 (10:00 a.m. in Yost 306)
Title: Channel Noise And Firing Irregularity In Hybrid Markov Models Of The Morris-Lecar Neuron
Speaker: Casey Bennett (Case Western Reserve University)
Advisor: Peter Thomas (Associate Professor, Case Western Reserve University)
Abstract: Using a stochastic version of the Morris-Lecar model neuron, a scaling method is introduced in which the ODE that propagates voltage is invariant, but the underlying Markov chain which controls the discrete channel states converges to progressively simpler stochastic descriptions. …Read more.