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.
The Computer Science Department invites the campus community to a lecture by faculty candidate Laxima Niure Kandel on February 13th at 11:00 in Wing 102. Kandels' talk is entitled "Exploiting Pervasive Commodity WiFi Devices for Continous Spatial Awareness and Security".
Laxima Niure Kandel is currently a Ph.D. candidate in the Department of Electrical and Computer Engineering at Stevens Institute of Technology, Hoboken, NJ. She received her MS degree in Telecommunication and Information Systems in 2008 from the University of Essex, Colchester, UK and her B.E. degree in Electronics and Telecommunication Engineering in 2007 from Priyadarshini College of Engineering and Architecture, Nagpur, India. Her research spans the areas of cybersecurity and wireless sensing and localization with applications in IoT and Cyber- Physical Systems.
ABSTRACT: Wireless networks are becoming increasingly prevalent and are playing an increasingly important role in our daily lives. As WiFi usage continues to surge and networks continually evolve, associated threats and security breaches continue to emerge. To make wireless networks resilient to malicious attacks, innovative physical layer (PHY) based security approaches have been gaining momentum recently. In the first part of this talk, I will discuss our new device authentication scheme that leverages artifacts embedded in the emitted signal as the source of device identity for preventing unauthorized access to wireless networks and services. Since our approach to bootstrap security of wireless devices is free from heavy cryptography, it has a lot of potential applications in resource-constrained devices including the Internet of Things (IoT) and Cyber- Physical Systems (CPS). The second part of this talk will introduce our research on self-calibrating indoor localization system using commodity WiFi chips. Unlike existing approaches, our proposed solution is completely software-based and does not require any cumbersome offline phase noise calibration efforts. This research easily integrates into IoT and CPS networks that require smart objects to precisely know their position or location in a given surrounding. Thus, the progress made in these two key areas will pave the way for more sustainable, reliable, and secure IoT and CPS frameworks. I will conclude this talk by discussing future work on these two topics.
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.
The embedded systems emphasis trains students to design, construct and program small-scale hardware systems for use across a wide variety of platforms. Our departmental "pick and place" machine is able to quickly populate a PC board once it has been configured and calibrated. This video shows the device populating a PCB measuring just over a square inch with 40 components (resistors, LEDs and MOSFETs) in just over a minute.
The UWL Computer Science Department will offer a new undergraduate degree in Computer Engineering beginning in Fall 2020. Computer engineering graduates can expect to receive very good starting salaries; placing among the highest in engineering (refer to the Burea of Labor Statistics for details). Computer engineers work at the hardware-software boundary and are able to move into either hardware or software positions.
Graduates can expect to fill a wide variety of in-demand jobs including roles in low-level software development (device drivers, firmware, operating systems and virtual machines) in addition to roles in hardware design (digital circuit design and verification, computer architecture, control systems and signal processing).
More information information about the program can be found under the Programs tab above.