The Division of Mathematics and Computer Science provides access to “smart” classrooms with multimedia capabilities, well-equipped computer labs, an undergraduate research lab and computer science tutoring lab. Ongoing discussions with area industry leaders influence the ever-changing curriculum in computer science and computer information systems. The industry connection also helps provide training for computer science and CIS majors.
The Division of Mathematics and Computer Science offers three computer science degrees:
- A Bachelor of Science in Computer Science, accredited by the Computing Accreditation Commission of ABET, https://www.abet.org.
- A Bachelor of Arts in Computer Information Systems
- A Bachelor of Science in Cybersecurity
Bachelor of Science in Computer Science
The program in computer science offers students a strong foundation in computer science by following the Accreditation Board for Engineering and Technology accreditation standards. Students in the program also acquire a solid background in mathematics, which is particularly useful for students who elect to pursue graduate studies in computer science or work in research/engineering environments.
To provide a high-quality baccalaureate education with the depth and breadth necessary to prepare students for careers in computer science and to pursue graduate education in the field if they so choose. In keeping with the metropolitan mission of the University, graduates of the program will have the skills necessary to compete for jobs in the Upstate and contribute to the region's economic productivity.
Program Objectives and Expected Outcomes for Graduating Students
- Graduates of the Computer Science Program will possess the breadth and depth of knowledge sufficient to work and excel in the field of computer science or to pursue advanced study in computer science.
- Graduates will have an understanding of the fundamental principles and techniques of computer science that will enable them to adapt and solve problems in the continually evolving field of computer science.
- Graduates of the Computer Science Program will be effective communicators, problem solvers, critical thinkers and team workers within the computer science profession.
- Graduates of the Computer Science Program will understand the ethical and societal issues relating to the computer science profession and the use of computers in everyday life.
- Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
- Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
- Communicate effectively in a variety of professional contexts.
- Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
- Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
- Apply computer science theory and software development fundamentals to produce computing-based solutions.
Career opportunities for students earning a Bachelor of Science in Computer Science or a Bachelor of Arts in Computer Information Systems include software developer, system analyst, information technology manager, web application developer, database analyst, data architect, network analyst, network administrator, database administrator, security specialist, and system administrator.
The Division of Mathematics and Computer Science has established internship programs through the Career Center and with major corporations in the Upstate. Students in their senior year are strongly encouraged to seek internship opportunities at one of the many partnering corporations such as BWM Manufacturing, Michelin and Milliken.
Number of Majors Enrolled
Number of Graduates
A networking lab facilitates instruction in local and wide area networking, providing the resources for hands-on application of the theoretical concepts that are studied.
Classrooms & Walk-In Labs
Almost all of our classrooms are equipped with ceiling-mounted video-data projectors, internet connection, and document cameras. In addition, there are three computer classrooms, each equipped with 24 student stations, a laser printer, and an instructor’s station with software that enables the instructor to control the student computers in the classroom through the campus network. The G. B. Hodge Center, home of the Division of Mathematics and Computer Science, has two walk-in computer labs open to students while the building is open. Each has 12 Pentium personal computers equipped with Windows XP Professional, Office XP, and all of the mathematics and computer software used in classes.
Undergraduate Research Lab
Undergraduate students who assist professors in conducting research benefit tremendously. They graduate with enhanced: analytical and communication skills; knowledge of a specific discipline; work etiquette. The lab houses Mathematics and Computer Science students who are:
- Undergraduate Research Assistants supported by research grant funds
- Participating in an Internship
- Taking an Independent Study
The Computer Science faculty at the University of South Carolina Upstate actively pursue several areas of research, including:
- 3D Visualization of Complex Systems
- Artificial Intelligence
- Computer Architecture
- Computer Vision
- Data Mining
- Evolutionary Computation
- Intelligent Information Retrieval
- Middleware and Database Systems
- Machine Learning
- Mobile Computing
- Natural Language Processing
- Networking and Wireless Networking
- Software Engineering
Research grants are constantly sought after by the CS faculty for providing employment opportunites for undergraduate students and establishing hi-tech research facilities. For example, unique to the Upstate, is a state-of-the-art Robotics Lab located in the Stockwell Administration Building room 117 which houses six heavy duty, industrial robotic arms funded by the Staubli Corporation. We also have an Undergraduate Research Lab located in the Hodge room 247 which houses several top-of-the-line machines which are used by Mathematics and Computer Science majors engaging in research, independent studies or internships.
Some recent publications that highlight current research efforts by the USC Upstate Computer Science faculty include:
Chunyu Ai, Frank Haizhon Li and Kejia Zhang, "Detecting Isolate Safe Areas in Wireless Sensor Monitoring Systems", accepted by Journal of Tsinghua Science and Technology, 2017.
Meng Han, Chunyu Ai, Forrest Wong Lybarger, Yingshu Li, and Zhuojun Duan, "Time Constraint Influence Maximization Algorithm in the Age of Big Data", International Journal of Computational Science and Engineering, 2017.
Weitian Tong, Scott Buglass, Jeffrey Li, Lei Chen, Chunyu Ai, "Smart and Private Social Activity Invitation Framework Based on Historical Data from Smart Devices", accepted by 10th EAI International Conference on Mobile Multimedia Communications, July 13-14, 2017, Chongqing China.
Schwartz, A. & Hetzel, M. “The Impact of Fault Type on the Relationship Between Code Coverage and Fault Detection.” Proceedings of the 11th International Workshop on Automation of Software Test (AST 2016), May 2016.
Schwartz, A. & Do, H. “Cost-effective Regression Testing through Adaptive Test Prioritization Strategies.” Journal of Systems and Software 115 (2016): 61-81.
Chunyu Ai, Meng Han, Jinbao Wang, and Mingyuan Yan, "An Efficient Social Event Invitation Framework based on Historical Data of Smart Devices", The 9th IEEE International Conference on Social Computing and Networking, Oct. 8-10, 2016, Atlanta.
Mingyuan Yan, Chunyu Ai, Meng Han, Zhipeng Cai, and Yingshu Li "Data Aggregation Scheduling in Probabilistic Wireless Networks with Cognitive Radio Capability", IEEE Global Communication Conference (GLOBECOM 2016), Dec. 4-8, 2016, Washington, DC.
Jinbao Wang, Zhipeng Cai, Chunyu Ai, Donghua Yang, Hong Gao, and Jianzhong Li, "Differentially Private k-Anonymity: Achieving Query Privacy in Location-Based Services", International Conference on Identification, Information and Knowledge in the Internet of Things for 2016, October 20-21, 2016, Beijing.
Feng Gu, Rida Syeda, and Chunyu Ai, "Geo-referenced image data assimilation for wildfire spread simulation", In Proceedings of the 49th Annual Simulation Symposium (ANSS '16). Society for Computer Simulation International, San Diego, CA, USA, Article 11 , 8 pages.
Feng Gu, Mohammad Butt, Chunyu Ai, Xiaoke Shen, and Jiehao Xiao. "Adaptive particle filtering in data assimilation of wildfire spread simulation". In Proceedings of the Conference on Summer Computer Simulation (SummerSim '15), Society for Computer Simulation International, San Diego, CA, USA, 1-10, 2015.
Wei Zhong, Jieyue He, Xiujuan Chen, and Yi Pan. "Multi-level Clustering Support Vector Machine Trees for Improved Protein Local Structure Prediction," International Journal of Data Mining and Bioinformatics, vol. 9(2), Pages 172-198, 2014.
Chunyu Ai, Wei Zhong, Mingyuan Yan, Feng Gu, "A Partner Matching Framework for Social Activity Communities", Computational Social Networks, Vol. 1.1, 2014.
Chunyu Ai and Frank Haizhon Li. "Isolate Safe Area Detection for Rescue in Wireless Sensor Networks. " In Proceedings of the 9th International Conference on Wireless Algorithms, Systems, and Applications, Vol. 8491. Springer-Verlag New York, Inc., New York, NY, USA, 718-728, 2014.
Chunyu Ai, Wei Zhong, Mingyuan Yan, Feng Gu, "Partner Matching Applications of Social Networks". In Proceeding of International Computing and Combinatorics Conference. Springer International Publishing, 2014, pp. 647-656.
Ning Tian, Longjiang Guo, Meirui Ren, Chunyu Ai, "Implementing the Matrix Inversion by Gauss-Jordan Method with CUDA", In Proceedings of the 9th International Conference on Wireless Algorithms, Systems, and Applications,Vol. 8491. Springer-Verlag New York, Inc., New York, NY, USA, pp. 44-53, 2014.
Ning Tian, Longjiang Guo, Chunyu Ai, Meirui Ren, Jinbao Li, "GPU Acceleration of Finding Maximum Eigenvalue of Positive Matrices", In International Conference on Algorithms and Architectures for Parallel Processing (pp. 231-244). Springer International Publishing.
Shufang Du, Longjiang Guo, Chunyu Ai, Jinbao Li and Meirui Ren, "GPU Acceleration of Finding Frequent Patterns over Large Biological Sequence", In 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS), Hsinchu, Taiwan December 16-19, 2014.
Shufang Du, Longjiang Guo, Chunyu Ai, Meirui Ren, Hao Qu, and Jinbao Li, "GPU Acceleration of Finding LPRs in DNA Sequence Based on SUA Index", 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC), Austin, TX, 2014, pp. 1-8.
Schwartz, A. & Do, H. “A Fuzzy Expert System for Cost-Effective Regression Testing Strategies,” Proceedings of IEEE International Conference on Software Maintenance (ICSM), September, 2013.
Renda Wang, Longjiang Guo, Chunyu Ai, Jinbao Li, Meirui Ren and Keqin Li, "An Efficient Graph Isomorphism Aglorithm Based on Canonical Labeling and its Parallel Implementation on GPU ", the 15th IEEE International Conference on High Performance Computing and Communications (HPCC 2013), Zhangjiajie, China, November 13015, 2013.
Xinjing Wang, Longjiang Guo, Chunyu Ai, Jinbao Li, Zhipeng Cai, "An Urban Area-Oriented Traffic Information Query Strategy in VANETs", In Proceedings of the 8th international conference on Wireless Algorithms, Systems and Applications (WASA' 13), Springer-Verlag, Berlin, Heidelberg, 2013, pp. 313-324.
Chunyu Ai, Yueming Duan, Mingyuan Yan, and Jing He, "Area Query Processing Based on Gray Code in Wireless Sensor Networks", Tsinghua Science and Technology, Vol.17, Issue 5, 2012.
Chunyu Ai, Yueming Duan, Mingyuan Yan, and Jing He, "Area Query Processing Based on Gray Code in Wireless Sensor Networks", Tsinghua Science and Technology, Vol.17, Issue 5, 2012.
Yingshu Li, Chunyu Ai, Zhipeng Cai, and Raheem Beyan, "Sensor scheduling for p-percent coverage in wireless sensor networks", Cluster Computing, pp. 27-40, March, 2011.
Yingshu Li, Chinh Vu, Chunyu Ai, Guantao Chen, and Yi Zhao, "Transforming Complete Coverage Algorithms to Partial Coverage Algorithms for Wireless Sensor Networks,"IEEE Transactions on Parallel and Distributed Systems, pp. 695-703, April, 2011.
Longjiang Guo, Meirui Ren, Jinbao Li, Yong Liu, and Chunyu Ai, "H-cluster: A Novel Efficient Algorithm for Data Clustering in Sensor Networks," Journal of Communications, Vol 6, No 2 (2011), 168-178, Apr 2011.
Wei Zhong, Rick Chow, and Jieyue He. “Clinical Charge Profiles Prediction for Patients Diagnosed with Chronic Diseases Using Multi-level Support Vector Machine,” Expert Systems with Applications: An International Journal, 31 Pages, Vol. 39 No. 1, 2012. (Acceptance Rate of Papers: ~15%)
Sebastian van Delden and Michael Umrysh. 2011. Visual Detection of Objects in a Robotic Work Area using Hand Gestures. In Proceedings of the IEEE International Symposium on Robotics and Sensor Environments, Montreal, Canada, Pp 237-243. September 17-18, 2011.
Sebastian van Delden and Nicole Tobias. 2010. A Novel Approach to 3D Contour Recovery using Structured Light Mounted to a Robotic Manipulator. In Proceedings of the IASTED International Conference on Robotics and Applications, Cambridge, MA, Pp 167-173, Nov 1-3, 2010.
Sebastian van Delden. 2010. Computer Science Meets Industrial Robotics: A Visual Servoing Project for a Computer Vision Course. In the Journal of Computing Sciences in Colleges. Volume 25, Number 6. Pages 85-92. Select papers from the 15th Annual Northeast Meeting of the Consortium for Computing Sciences in Colleges. Hartford University. (Acceptance Rate: 42%)
Sebastian van Delden. 2010. Industrial Robotic Game Playing: An AI Course. In the Journal of Computing Sciences in Colleges. Volume 25, Number 3. Pages 134-142. Select papers from the 25th Annual Eastern Meeting of the Consortium for Computing Sciences in Colleges. Villanova University, Pennsylvania. January 2010. (Acceptance Rate: 50%)
Jieyue He, Fang Zhou, Wei Zhong, and Yi Pan. Gene Subsets Extraction Based on Mutual Information based Minimum Spanning Trees Model. International Journal of Computational Biology and Drug Design (IJCBDD), vol. 2(2), pp.187-203, 2009
Wei Zhong, Rick Chow, Jieyue He, Richard Stolz, and Marsha Dowell. Multi-Level Support Vector Machines for Classifying Large Chronic Disease Datasets. Proceedings of the 2009 International Conference on Bioinformatics & Computational Biology (BIOCOMP'09), vol. 1, pp. 370-375 (Acceptance Rate of Regular Papers: ~27%)
Sebastian van Delden and Frank Hardy. 2009. Robotic Eye-in-hand Calibration in an Uncalibrated Environment. In the Journal on Systemics, Cybernetics and Informatics. Volume 6, Number 6. Pages 67-72.
Sebastian van Delden and Wei Zhong. Effective Integration of Autonomous Robots into an Introductory Computer Science Course: A Case Study. Journal of Computing Sciences in Colleges. 6 pages, 2008.
Sebastian van Delden and Benjamin Overcash. 2008. Towards Voice-Guided Robotic Manipulator Jogging. In Proceedings of the 12th World Multiconference on Systemics, Cybernetics and Informatics. Volume 3. Pages 138-144. Orlando, FL. July 2008. (Acceptance Rate of Regular Papers: ~37%)
Wei Zhong, Gulsah Altun, Xinmin Tian, Robert Harrison, Phang C. Tai, and Yi Pan. Parallel Protein Secondary Structure Prediction Schemes Using Pthread and OpenMP over Hyper-Threading Technology. Journal of Supercomputing, vol. 41(1), pp. 1-16, July 2007.
Wei Zhong, Jieyue He, Robert. Harrison, Phang C. Tai, and Yi Pan. Clustering Support Vector Machines for Local Protein Structure Prediction. Expert Systems with Applications: An International Journal, vol. 32(2), pp. 518-526, February 2007.
Sebastian van Delden. Constructing a Simple Visually-Guided Robotic Part-Grasping System with Off-the-Shelf Components. The 18th IEEE International Conference on Tools with Artificial Intelligence (TAI 2006). Washington, DC. November 13-15th, 2006.
Y. Xiao; F. H. Li; Wu, K.; Leung, K.K.; Ni, Q.On optimizing backoff counter reservation and classifying stations for the IEEE 802.11 distributed wireless LANs, IEEE Transactions on Parallel and Distributed Systems (TPDS), Volume 17, Issue 7, July 2006 Page(s): 713 - 722.
Jerome Lewis. Using Predictor-Corrector Methods in Numerical Solutions to Mathematical Problems of Motion. Mathematics and Computer Education Journal, Vol. 39, No. 1, Winter 2005.
Wei Zhong, Gulsah Altun, Robert Harrison, Phang C. Tai, and Yi Pan Improved K-means Clustering Algorithm for Exploring Local Protein Sequence Motifs Representing Common Structural Property. IEEE Transactions on NanoBioscience, Vol. 4, No. 3, pp. 255-265, September 2005
Y. Xiao, H. Li, Voice and Video Transmissions with Global Data Parameter Control for the IEEE 802.11e Enhanced Distributed Channel Access IEEE Transactions on Parallel and Distributed Systems , Vol. 15, No. 11, pages: 1041-1053, November 2004. Top 3 of The Top 50 most Downloaded Articles for IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS) in 2004 from IEEE Xplore.
Below are the poster and presentations topics for USC Upstate Computer Science students, or math students with a Computer Science faculty member, at the Annual South Carolina Upstate Research Symposium.
- An Empirical Study of Missed Faults and Code Coverage
Daniel Puckett and Amanda Schwartz (Adviser)
- Distributed Applications and Security
Robert Atkins and Frank Li (Adviser)
- Investigating Fault Type and Code Coverage
Michael Hetzel and and Amanda Schwartz (Adviser)
- 4G LTE security
Andreas Baeuml and Frank Li (Adviser)
- Andoid App Testing: An Empirical Study
Sheldon Smith and Amanda Schwartz (Adviser)
- Intelligent Wireless Sensor Network Monitoring System
Joel Miller and Chunyu Ai (Adviser)
- A Robust Decision Support Model for Generating Clinical Knowledge of Patients Diagnosed with Chronic Diseases
Brett Michaud and Wei Zhong (Adviser)
- Visual Detection of Objects in a Robotic Word-Area using Hand Gestures
Alex Umrysh and Sebastian van Delden (Adviser)
- BrailleSC: The Possibilities are Endless
Cory Bohon, Tina Herzberg, and George Williams (Adviser)
- On Comparison of Two Survival Functions
Brent McCracken and Seunggeun Hyun (Adviser)
- Rule Mining the HCUP Dataset Using the Tukey Method
Daniel Hagerman and Wei Zhong (Adviser)