Welcome to the Department of Computer Science at UWL. Our stellar programs attract oustanding students from around the world who work closely with our faculty to advance state-of-the-art research in computing technologies. We attribute our success to a strong tradition of collaborative research, close working relationships with local industries, state-of-the-art facilities and a dedicated committment to student achievement.
We offer bachelor of science degrees in computer science and computer engineering as well as a master's degree in software engineering. Our MSE program is unique within the UW system and offers graduates very desiriable employment opportunities. We also offer a very popular dual-degree program that awards students a BS in Computer Science and a Master's in Software Engineering within a condensed time frame of only five years. If you have any questions please contact us.
MICS 2020 in Milwaukee Wisconsin was held remotely due to the COVID-19 pandemic. Several UWL faculty and student articles were accepted for presentation including a joint presentation entitled "DuctTape - A High-level RISC-V Assembler" by Dr. Elliott Forbes and Justin Severeid. Their work is available on the MICS 2020 Youtube channel as DuctTape - A High-level RISC-V Assemble.
Their presentation also attracted the attention of the international RISC-V Foundation as they re-posted the presentation on their web site. The RISC-V Foundation (www.riscv.org) was founded in 2015 to build an open, collaborative community of software and hardware innovators based on the RISC-V ISA and later entered into a collaborative agreement with the Linux Foundation.
MICS 2020 in Milwaukee Wisconsin was held remotely due to the COVID-19 pandemic. Several UWL faculty and student articles were accepted for presentation including a paper entitled "RoloBox: An Image-Aware Mobile Application using the AWS Ecosystem" by Hui Li. Hui Li is a graduate student in the MSE program and is supervised by Dr. Kenny Hunt. This paper explores how the AWS ecosystem can be incorporated into undergraduate course or lab work by showing its use in the context of a much larger capstone effort. His presentation is available on the MICS 2020 Youtube channel as RoloBox: An Image-Aware Mobile Application using the AWS Ecosystem.
MICS 2020 in Milwaukee Wisconsin was held remotely due to the COVID-19 pandemic. Several UWL faculty and student articles were accepted for presentation including a paper entitled "Developing an Autonomous Driving Model Based on Raspberry Pi" by Yuanqing Suo, Dr. Mao Zheng, and Dr. Song Chen. Yuanqing Suo is a graduate student in the MSE program and is supervised by Dr. Mao Zheng. This paper presents a software system for autonomous mobile navigation based on the well-known Rasberry Pi. Her presentation is available on the MICS 2020 Youtube channel as Developing an Autonomous Driving Model Based on Raspberry Pi.
MICS 2020 in Milwaukee Wisconsin was held remotely due to the COVID-19 pandemic. Several UWL faculty and student articles were accepted for presentation including a paper entitled "Three Focused Artificial Intelligence Assignments Based On Children's Games" by Dr. John Marist. This paper presents assignments based on three children's games for an entry-level artificial intelligence class, each focused on a specific topic of the standard introductory algorithmic AI curriculum.
MICS 2020 in Milwaukee Wisconsin was held remotely due to the COVID-19 pandemic. Several UWL faculty and student articles were accepted for presentation including a paper entitled "Graph Traversal for Procedural Fantasy Map Generation" by Dr. Kenny Hunt. This paper describes a novel application domain for teaching graphs, graph traversal, and spanning tree algorithms. A technique for generating randomized fantasy maps backed by a randomized graph data structure is presented. Terrain elevation is generated via bread-first search, roads are generated using Prim’s minimum cost spanning tree, and continents are segmented from surrounding seas using breadth-first search as well. This presentation is available on the MICS 2020 Youtube channel as Procedural Fantasy Map Generation.
The department will be offering a new undergraduate degree program in Computer Engineering starting in Fall 2020. Exceptional candidates in all areas of Computer Engineering and Computer Science, or closely related fields will be considered, but of particular interest are candidates specializing in the areas of embedded systems, Internet of Things (IoT), VLSI design, controls, and networking. The successful applicant will have the opportunity to make significant contributions to all aspects of this new program including curricular development, spearheading new research agendas, and contributing to accreditation efforts.
The position is listed at HigherEdJobs, and on the UWL job postings site. The position will be open until filled. Applications recieved by January 20, 2020 will be included in the first review. Questions may be directed to the department chair Prof. Hunt.
Laik Ruetten spent the summer months of 2019 as an REU researcher developing a technique for controlling a swam of robotic drones. His research was recognized with a Best Paper award at the IEEE CCWC 2020 conference in January, 2020. The abstract of his paper is included below.
Intelligent robot swarms are increasingly being explored as tools for search and rescue missions. Efficient path planning and robust communication networks are critical elements of completing missions. The focus of this research is to give unmanned aerial vehicles (UAVs) the ability to self-organize a mesh network that is optimized for area coverage. The UAVs will be able to read the communication strength between themselves and all the UAVs it is connected to using RSSI. The UAVs should be able to adjust their positioning closer to other UAVs if RSSI is below a threshold, and they should also maintain communication as a group if they move together along a search path. Our approach was to use Genetic Algorithms in a simulated environment to achieve multi-node exploration with emphasis on connectivity and swarm spread.
The Midwest Instruction and Computing Symposium (MICS) is a regional conference dedicated to providing an educational experience to students and instructors at higher education institutions. The conference focuses on the teaching of computing and its use in learning processes of all disciplines, and the incorporation of the study of this technology in the curriculum. This years conference is located at MSOE, Milwaukee on April 3rd-4th.
Jonathan Bentz, this years keynote speaker, is a Solutions Architect at NVIDIA, where he leads a team focused on higher education and research computing. Jonathan obtained both his Ph.D. in physical chemistry and an M.S. in computer science from Iowa State University. Deep Learning and other AI approaches provide a new way to extract value and insights from vast amounts of data. This talk will shed light on the breadth of Deep Learning and other AI applications as well as the challenges yet in front of AI researchers.
Dr. Lei Wangs' article entitled "ARPAP: A Novel Antenna-Radiation-Pattern-Aware Power-Based Positioning in RF System" was accepted for publication in the highly ranked IEEE Transactions on Mobile Computing. A pre-print can be found at IEEE Xplore. The abstract is shown below.
Traditional power-based localization methods suffer from low accuracy in the practical application environment. The main challenges are the antenna directivity and fading effect. Conventional methods assume omnidirectional antenna directivity such that the solution is the intersections of multiple circle-shape contours. This strong assumption results in significant localization error in practical non-isotropic antenna applications. In this article, a novel antenna radiation-pattern-aware power-based positioning (ARPAP) scheme is proposed. It reduces the antenna directivity effect by including the antenna pattern into the localization system model. It reduces the bias error that introduced by power measurement through estimating the line-of-sight (LoS) component in received signal strength (RSS). Moreover, the error mode for the proposed ARPAP system, along with the theoretical limit, Cramer-Rao Bound (CRB), and bias of the proposed positioning system are derived. The Pearson correlation coefficient between the proposed error model and simulation result shows a high similarity score. The proposed positioning scheme and analytic error model are instantiated for the cellular network. Both analytical model and simulation results demonstrate the superiority of the proposed method over traditional methods.
Dr. David Mathias Dr. David Mathias presented his work in Evolutionary Computing and Genetic Algorithms at The University of Central Florida's Spring 2020 Seminar Series. The presentation was entitled "Evolving Cooperation in the Iterated Prisoner's Dilemma". The abstract of his presentation is given below.
The Prisoner’s Dilemma is a simple two-player game in which two accomplices have been captured by the police and are being interrogated separately. Each has two choices: to cooperate with their accomplice and refuse to confess; or to defect by confessing and implicating their accomplice. The reward (symmetrically, penalty) that each receives depends not only on their own action but also that of the other player. In the Iterated Prisoner’s Dilemma (IPD), the players play multiple rounds of the game allowing them to learn about their opponent’s actions and act accordingly.
The Iterated Prisoner's Dilemma has been studied in great detail in fields as diverse as economics, computer science, psychology, politics, and environmental studies. This is due, in part, to the intriguing property that its Nash Equilibrium is not a globally optimal solution. Many researchers have used evolutionary computation to evolve effective strategies for IPD. Typically treated as a single-objective problem, a player's goal is to maximize their total reward. Mittal and Deb created a multi-objective version of the game by including minimization of the opponent's reward as an additional objective.
Here, we explore the role of mutual cooperation in IPD player performance. We implement a multi-objective genetic algorithm in which each member of the population belongs to one of four sub-populations: selfish, communal, cooperative, and selfless, the last three of which use mutual cooperation as an optimization objective. Game play occurs among all members, without regard to sub-population, while crossover and selection occur only within a sub- population. Our evolved players are tested against a population of Axelrod's strategies and the Gradual strategy. Testing solely using self score, we find that players evolved with mutual cooperation as an objective perform very well. In some cases, our cooperative players completely dominate the competition. Thus, learning to play nicely with others is a successful strategy for maximizing personal reward.