Major Research Areas

Artificial intelligence (AI) is a broad field concerned with giving machines the ability to perform cognitive tasks traditionally attributed to humans. Today AI enjoys growing success in applications such as speech recognition, face recognition, cybersecurity, cloud computing, education, composing music and self-driving cars, fueled by new advances in machine learning such as deep learning. Although AI tools and learning resources have now become highly accessible to the general public, the future of many fields depends on current AI research and the correct application of AI algorithms. Moreover, many applications require higher accuracy than currently obtainable, scaling to massive datasets is in high demand, and new foundational problems continue to arise. Our current research includes using AI techniques of inference with machine and deep learning for (a) protein folding, (b) identifying genetic risks for complex diseases, (c) computer vision, (d) cybersecurity, (d) remote sensing, including satellite, aerial, and UAV for geospatial and intelligence operations, (e) management and security of multi-cloud systems, and (f) next-generation healthcare. The following faculty members involved in AI research: Badri Adhikari, Uday Chakraborty, Sharlee Climer, Lav Gupta, Mark Hauschild, Cezary Janikow and Sanjiv Bhatia.

Research in computer vision & graphics in the Department of Computer Science focuses on analyzing and processing visual data such as images, videos, point clouds, polygon meshes, and range/volume data. Computer vision research seeks high-level understanding from digital images and videos while solving challenging problems in object tracking, image clustering, image database indexing, digital terrain modeling, and image fusion. Computer graphics research aims to facilitate efficient visual communication via computational modeling, rendering, and animation of graphical data. The following faculty members involved in computer vision & graphics research: Sanjiv Bhatia and Henry Kang

The Cybersecurity and Networking Systems group at UMSL carries out research in diverse areas of cybersecurity and networking like the Internet, broadband and other networked systems, security of these networks and network applications, mobile and wireless networks and their security, mobile edge computing, network protocols and network security protocols, secure Internet of things, virtualized networks, cloud computing and management and security of cloud networks. For carrying out collaborative and meaningful research, Cybersecurity and Networking Systems Lab (CNSL) work closely with academia and industry. It focuses on cutting-edge research for providing innovative solutions for future networking and cybersecurity services. The Lab especially focuses on socially relevant research for improving the welfare of the local and global community. The faculties of involved with the lab together have many years of diversified experience. The current faculty members associated with the lab are: Jianli Pan, Abde Mtibaa, Lav Gupta and Ankit Chaudhary.

Advances in the biological sciences are accelerating at an unparalleled rate, producing massive amounts of data and providing unprecedented opportunities for major advances in medical and environmental domains. Sophisticated computer science, statistical, and mathematical tools are indispensable for reasoning about these data and unveiling hidden truths that are unreachable using experimental and theoretical approaches alone. Computational biology, biostatistics, and bioinformatics are overlapping fields aimed at filling this demand. More broadly, the adaptation and fusion of combinatorial optimization, artificial intelligence, statistics and applied mathematics, imaging, evolutionary computation, machine learning, mathematical programming, data mining and deep learning techniques provide potential for the production of novel specialized solutions. Our current research includes (a) network modeling, mixed integer linear programming, statistics, and clustering applied to combinatorial genetics, gene co-expression, combinatorial proteomics, and haplotype inference problems arising in the research of Alzheimer disease, psoriasis, hypertensive heart failure, vitamin D metabolism, and human population genetics, and (b) protein structure modeling and protein contact prediction using techniques like simulated annealing algorithm, deep learning techniques, and convolutional-neural networks.  The following ffaculty members involved in biological data research: Badri Adhikari and Sharlee Climer

Research in data science in the Department of Computer Science focuses on the design, analysis, and application of computational algorithms for data-centric problems arising in diverse domains. Machine learning and data mining approaches are developed and applied for prediction, classification, regression, and clustering. Linear and non-linear models and hybrid meta-heuristics are explored for data analytics. The following faculty members involved in data science-related research: Uday Chakraborty, Badri Adhikari, Sharlee Climer and Ankit Chaudhary

Research in evolutionary computation at our department focuses on the design, analysis, and applications of the genetic and evolutionary algorithms that can solve difficult problems in a robust and scalable manner. The following faculty members involved in evolutionary computation research: Uday Chakraborty, Mark Hauschild and Cezary Janikow.