Pyridine macrocycles have useful applications due to their ability to complex with metals. A library of substituted pyridine macrocycles exists along with how modifications at Carbon 4 impact compound reactivity. Despite literature about similar pyridine macrocycle structures, little is known about how an iodo-substituted pyridine macrocycle will alter the properties of the compound when complexed to Copper. To understand the fundamental characteristics of an Iodo-substituted pyridine macrocycle, the ligand is synthesized followed by electronic environment analysis via 1H NMR. Ultraviolet-Visible Spectroscopy is used to verify ligand complexation with Copper (II) metal followed by X-ray diffraction to determine metal binding nature of the complex. Cyclic Voltammetry analysis is used to support the theory that the iodo functional group behaves as an electron withdrawing group. This compound serves as a comparison to explain the results of the Chloro-substituted pyridine macrocycle as well as a gateway molecule for the synthesis of new pyridine macrocycles.

The TCU and UNTHSC School of Medicine requires its students to participate in service learning with various non-profit partner organizations in the community. Our team's goal is to make the volunteer sign-up process easier and more convenient for med students, to automate the tracking of students' hours, and to ease the burden on faculty in charge of managing the entire process. We aim to accomplish these goals with a web application that will streamline the volunteer scheduling and hour-tracking process for students and faculty.

The TCU Computer Science Department has launched an AlphaGo research project. Currently, it can only be used by those directly involved with the project, and only at certain computers on campus. In addition, the interface for conducting research is difficult to use. Our goal is to make this project more widely accessible to students and faculty alike, whether they wish to help in research, or simply want to learn to play Go. We have developed a web application for the project that allows users to play against various Go AI agents, as well as allowing researchers to train new AI. In addition, our site allows various admin functions to control and edit users and AI agents alike.

This is a brief report on a comprehensive assessment of AlphaZero-type algorithms from the viewpoint of optimal play. This study does not join an already crowded field in seeking to enhance the efficiency of these algorithms, but sets sights on more conceptual questions and more quantitatively precise results. In particular, we show that the AlphaZero-type algorithms tend to behave more conservatively when winning and more aggressively when losing. We illustrate our results with a specific example on the 7x7 board.

A common way to evaluate the performance of players in two-player games is to have them play against other players. If the player wins more games than other players, then it is said to be more capable; in other words, the strength of a player is measured relatively. In this project, we seek a way to evaluate the performance of players in terms of absolute. In recent years, self-play reinforcement learning has given rise to capable game-playing agents in a number of complex domains such as Go and Chess. We perform an analysis of a self-play agent using scaled-down versions of Go on a generic platform to measure the strength of the agent via our developed methods.