## Remote Service Due to Covid-19

The department is fully remote at this time. Operating hours are Monday through Friday, regular business hours.

You can reach us in the following ways . . .

### Virtual Front Desk

Our virtual front desk uses the Zoom video conferencing tool. Zoom is available externally to all users at pdx.zoom.us. If you don’t have a laptop or desktop computer, you can use Zoom on your mobile device to join either session.

**Choose a time to access the virtual front desk by clicking on a Zoom meeting link below:**:

with Constance:

9:30-10:30AM

2:00-3:00PM (Cancelled November 19)

with Kathie:

10:30-11:30AM

1:00-2:00PM

(Students: please include your PSU ID number in all communication)**Undergraduate Advising Questions**: mathstatadvising@pdx.edu**Math Placement Test Questions**: mathplacement@pdx.edu**Course/class Instruction Questions**: Please email the instructor directly. Instructor email links are listed as part of the course information in the Course Schedule as well as in our Department Directory. **Graduate Advising Questions**: Please email the Graduate Program Adviser **Graduate Admissions/Application Questions**: mathstatgrad@pdx.edu **Other questions**: mthdept@pdx.edu

### Phone

Main department line: 503-725-3621 (Voicemail checked on a regular basis)

All phone numbers for department employees are listed in our Department Directory.

## Introducing our newest faculty - Dr. Dorcas Ofori-Boateng

Dr. Dorcas Ofori-Boateng joins the Mathematics + Statistics Department as Assistant Professor of Data Science. She will be part of the new Bachelor of Science in Data Science program starting in Fall 2020. Dr. Ofori-Boateng recently defended her dissertation at the University of Texas at Dallas.

## Upcoming events

### Urmas Yanis 501 mathematical literature and problems project…

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`2020-11-25 14:00:00``2020-11-25 15:00:00``Urmas Yanis 501 mathematical literature and problems project presentation``Urmas Yanis will give a talk on the following mathematical literature and problems project in partial fulfillment of requirements for the Master of Science in Mathematics Topic: “Overview and Demonstration of Supervised Deep Learning” Abstract: In this 501 project we shall give an overview of supervised deep learning by presenting three algorithms used in deep learning: backpropagation, used for computing a neural network's gradients, and the batch and stochastic gradient descents, used for updating a neural network's weights and biases. The aforementioned backpropagation algorithm will be the core of this presentation. We'll also demonstrate how a feedforward neural network coded in Python 3, not relying on Keras or TensorFlow, can achieve results above 95 percent of the MNIST and Fashion-MNIST databases. Under the direction of Dr. Mau Nam Nguyen``https://pdx.zoom.us/j/82904292252``Kathie Leck Fariborz Maseeh Department of Mathematics + Statistics``Kathie Leck Fariborz Maseeh Department of Mathematics + Statistics``America/Los_Angeles``public`### Mitchell Fennimore 501 statistical literature and problems…

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`2020-11-30 10:00:00``2020-11-30 11:00:00``Mitchell Fennimore 501 statistical literature and problems project presentation``Mitchell Fennimore will give a talk on the following statistical literature and problems project in partial fulfillment of requirements for the Master of Science in Statistics Topic: “The resolution of a fractional factorial experiment in terms of error correcting codes” Abstract: This statistical literature and problems project is based on “An application of information theory and error-correcting codes to fractional factorial experiments” Journal of statistical planning and inference, 92 (2001) Irad Ben-Gal, Lev B. Levitin. An isomorphism between the fractional factorial experiment and error correcting codes helps unravel the concept of a design’s resolution and the logic of alias structures. Once a resolution is determined by information theory’s H-optimization, inequalities from the study of error correcting codes provide optimality criteria for design selection. This criterion leads to a “paradoxical,” outcome: An optimal fractional factorial design can be maintained with a smaller experiment by increasing the number of levels. Under the direction of Dr. Robert Fountain``https://pdx.zoom.us/j/89263044111``Kathie Leck Fariborz Maseeh Department of Mathematics + Statistics``Kathie Leck Fariborz Maseeh Department of Mathematics + Statistics``America/Los_Angeles``public`### Aaron Maroni 501 mathematical literature and problems project…

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`2020-12-04 13:00:00``2020-12-04 14:00:00``Aaron Maroni 501 mathematical literature and problems project presentation``Aaron Maroni will give a talk on the following mathematical literature and problems project in partial fulfillment of requirements for the Master of Science in Mathematics Topic: “A Discussion on Reviving the Method of Particular Solutions in Solving Laplace Equation's Eigenvalues” Abstract: In this project, I will present a research paper published in SIAM Review, "Reviving the Method of Particular Solutions (MPS)" written by Betcke and Trefethen. This paper first addresses the deficiency of the original method from "Approximations and Bounds for Eigenvalues of Elliptic Operators" written by Fox, Henrici and Moler. It then proposes an improved MPS which allows computation of eigenvalues of the Laplace's equation with Dirichlet boundary conditions for much better accuracy over the original MPS. Furthermore, it also enables treatment of domains with multiple singular corners. Therefore, it has dramatically expanded the capability and robustness of the MPS method. I will present the work given by Betcke and Trefethen, and then develop new Python code to solve eigenvalues of the Laplace's equation on the domains with multiple singular corners. The new Python algorithms achieves even better performance than those reported in the original paper which unfortunately doe not share their code. The new Python code is thus attached in the Appendix for open access by other researchers. Under the direction of Dr. Bin Jiang``https://pdx.zoom.us/j/81597994369``Kathie Leck Fariborz Maseeh Department of Mathematics + Statistics``Kathie Leck Fariborz Maseeh Department of Mathematics + Statistics``America/Los_Angeles``public`