Research

Research in Computer Science Department

CS faculty members are conducting research in a variety of areas including algorithms, artificial intelligence and machine learning, big data and data science, bioinformatics, cloud computing, computer and information security,  networking, parallel, distributed and high performance computing,  systems, and software engineering, among others. CS research is supported by more than $5M in active research grants and contracts from various external funding agencies, such as National Science Foundation, National Institutes of Health, National Security Agency, Army Research Office, Air Force Office of Scientific Research, Office of Naval Research, and Department of Homeland Security. The strength of the department’s research is also evidenced in part by nine home-grown NSF CAREER awardees. Among the nine UTSA degree programs recognized in the 2019 Times Higher Education World University Rankings by subject, UTSA’s highest program ranking belonged to Computer Science being the 60th best in the nation and among the top 250 in the world. CS research program continues to move up in world ranking by NTU currently placed at #108 in 2021. The recent NSF’s FY’20 HERD survey placed UTSA CS department at the national rank of 57 in R&D expenditures, just below UC Berkeley, above UT Dallas, Rice, Duke, and Rutgers. CS faculty and the affiliated research entities - Institute of Cyber Security (ICS), Center for Infrastructure Assurance and Security (CIAS), AI Matrix Consortium, and Open Cloud Institute - have fostered and maintained close ties with research and education partners both inside, including UTSA’s School of Data Science (SDS) and the National Security Collaboration Center (NSCC), and outside of UTSA such as UT Health.

The faculty research can be broadly categorized into the following cross-cutting Computer Science research thrusts: 

  • Cybersecurity and Privacy
  • Data Driven Intelligence
  • Computing Foundations and Emerging Technologies
  • Networked Computer Systems
  • Software Systems

 

Cybersecurity and Privacy

 Cybersecurity has become a national imperative over the last decade. The number of cyberattacks has increased across all sectors both within the U.S. as well as the world in general.  Some estimates place the expected annual cost of cybercrime to exceed $10 trillion by 2025.  Given this, the importance of conducting research in various areas of cybersecurity to improve the cybersecurity posture of organizations and nations has increased in importance.  NIST has identified five core functions organizations need to concentrate on in their cybersecurity programs.  Each of these core functions provides a rich area for research to improve cybersecurity.  The core functions as outlined by NIST are Identify, Protect, Detect, Respond, and Recover.  Additionally, the introduction of any advances in technology also introduces new cybersecurity concerns.  In the last two decades we have seen cybersecurity issues in areas such as cloud computing, wireless networks, autonomous vehicles, cryptocurrencies, artificial intelligence, big data, and the Internet of Things.  Faculty members in the CS Department involved in various areas of cybersecurity and privacy include: 

  • Rajendra Boppana: Computer network security, information security.
  • Murtuza Jadliwala: Privacy Enhancing Technologies; Applied Cryptography; Cryptocurrencies & Blockchains; Incentive Mechanisms for Security; Adversarial Machine Learning; Activity Recognition
  • Turgay Korkmaz: Computer Networks; Computer and Information Security
  • Jianwei Niu: Software Engineering; Cyber Security; Privacy Compliance ; Data Science; Formal Methods
  • Paul Rad – knowledge Representation, Probabilistic Decision Making, Reinforcement Learning, AI security
  • Ravi Sandhu: Access Control Models and Systems; Secure Cloud Computing; Secure Cloud Enabled IoT/CPS; Holistic Security
  • Rocky Slavin: Mobile and IoT Privacy; Software Engineering; Security Patterns; Computer and Information Security; Natural Language Processing
  • Wei Wang: Programming Languages and Compilers; Software Reliability and Security; Runtime Environments
  • Xiaoyin Wang: Software Engineering; Program Languages and Analysis; Software Security; Natural Language Processing
  • Gregory White: Whole-community cybersecurity, automated information sharing, detection of intrusive cyber activities
  • Mimi Xie: Self-powered IoT devices; Software and hardware co-design of embedded system; Security and reliability of emerging memory technologies; Design and optimization of computer

 

Data Driven Intelligence 

The volumes of data produced by humans and machines today significantly outpace humans' ability to absorb, evaluate, and make complicated decisions based on that data. Artificial intelligence/machine learning is the foundation of all computer learning and the future of all complex real-world decision-making under uncertainty. Our research covers a wide range of topics of this fast-evolving field, advancing how machines reason, learn, predict, plan, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of data driven intelligence. This broad area studies artificial intelligence/machine learning theory (such as algorithms and optimization), big data (data management, computation, and analysis), statistical learning (such as inference, graphical models, and causal analysis), deep learning (such as adversarial learning, explainability, and knowledge representation), reinforcement learning, symbolic reasoning, as well as diverse hardware implementations of ML. Members of the Computer Science Data driven Intelligence group at UTSA include:

  • Kevin Desai - Computer Vision, Virtual/Augmented/Mixed Realities, Collaborative Virtual Environments
  • Amanda Fernandez – Deep Learning, Computer Vision, Adversarial Learning
  • Matthew Gibson - Computational Geometry, Medical Image Segmentation
  • Mitra Hosseini: Requirements Engineering; Privacy Engineering; Natural Language Processing
  • Sumit Jha - Artificial Intelligence, Quantum Computing, Logic and Automata Theory, Emerging Hardware Architectures
  • Murtuza Jadliwala - Applied Cryptography, Cryptocurrencies & Blockchains, Incentive Mechanisms for Security; Adversarial Machine Learning
  • Dhireesha Kudithipudi - Brain-Inspired AI, Energy Efficient Machine Intelligence, Computer Architecture
  • Panos Markopoulos - machine learning, data analysis, and communications
  • Jianwei Niu - Software Engineering, Cyber Security, Privacy Compliance, Data Science, Formal Methods
  • John Quarles – Human Computer Interaction, Virtual/Augmented/Mixed Realities, Game Design
  • Sushil Prasad: Parallel and Distributed Computing; Parallel Algorithms and Data Structures; Data Intensive Computing over Geospatial and Polar Datasets; Parallel Software Systems; Energy-efficient Deep Learning Models for Edge Devices
  • Paul Rad – knowledge Representation, Probabilistic Decision Making, Reinforcement Learning, AI security
  • Jianhua Ruan – Bioinformatics, Computational Systems Biology, AI for Life Sciences
  • Rocky Slavin - Mobile and IoT Privacy, Software Engineering, Security Patterns, Computer and Information Security, Natural Language Processing
  • Kay Robbins – Visualization, Computational Biology, Scientific Applications
  • Wei Wang: Cloud Computing; Software Engineering; Applied Artificial Intelligence; Parallel Computing; Compilers; Computer Architecture; Software Reliability
  • Xiaoyin Wang: Software Engineering; Program Languages and Analysis; Software Security; Natural Language Processing

 

Computing Foundations and Emerging Technologies

Computers are powerful problem-solving devices, but they are not infinitely powerful.  As computational power improves over time, we are faced with computational challenges that are growing in both size and complexity.  To face these growing challenges, researchers must determine which challenges have solutions that can be computed efficiently, and which tasks cannot be computed efficiently.  Answers to these questions can change over time as new technologies are developed in hardware (e.g., quantum and cloud computing) and software (e.g., programming languages and compilers).  CS Researchers in the department approach these critical needs along several fronts such as algorithms, quantum computing, computer architecture, cloud computing, and parallel and distributed systems.

  • Matthew Gibson-Lopez – Algorithms, Computational Geometry
  • Sumit Jha – Artificial Intelligence; Quantum Computing, Logic and Automata Theory, Parallel Algorithms and Architectures
  • Dhireesha Kudithipudi: Brain-Inspired AI; Energy Efficient Machine Intelligence; Computer Architecture
  • Palden Lama: Cloud Computing; Distributed Systems; Operating Systems; Sustainable Computing
  • Jianwei Niu: Software Engineering; Cyber Security; Privacy Compliance ; Data Science; Formal Methods
  • Sushil Prasad: Parallel and Distributed Computing; Parallel Algorithms and Data Structures; Data Intensive Computing over Geospatial and Polar Datasets; Parallel Software Systems; Energy-efficient Deep Learning Models for Edge Devices
  • Wei Wang – Programming Languages and Compilers; Software Reliability and Security; Runtime Environments
  • Xiaoyin Wang: Software Engineering; Program Languages and Analysis; Software Security; Natural Language Processing
  • Mimi Xie – Self-powered IoT devices; Software and hardware co-design of embedded system; Security and reliability of emerging memory technologies; Design and optimization of computer architecture
  • Timothy Yuen – Computer Science Education
  • Dakai Zhu – Embedded Systems, Real-Time Systems, Parallel and Distributed Systems

 

Networked Computer Systems

Networked computer systems, from embedded and mobile devices to large-scale  high-performance computers, data centers, and cloud computing, have become the backbone of our society’s IT infrastructure, supporting public services, business, scientific innovation, healthcare, and education.  The wide application of networked computer systems has made the research on their design, management, optimization, and applications important research areas. The faculty members of the department have a long history of conducting high-impact and innovative research in such systems. Their research involved the fundamental system designs, hardware, algorithms, and optimizations for low-power embedded and IoT devices, parallel, distributed and high-performance computing, cloud computing and data centers, and computer networks. Their research also involves systems support for a wide range of applications, such as web services, AI, big data, healthcare, earth science, virtual/augmented reality, robotics, and security and privacy.  Faculty members in the department involved in various areas of networked systems research include:

  • Rajendra Boppana: Computer Networks; Computer and Information Security
  • Sumit Jha: Artificial Intelligence, Quantum Computing, Logic and Automata Theory, Emerging Hardware Architectures
  • Turgay Korkmaz: Computer Networks; Computer and Information Security
  • Dhireesha Kudithipudi: Brain-Inspired AI; Energy Efficient Machine Intelligence; Computer Architecture
  • Palden Lama: Cloud Computing; Distributed Systems; Operating Systems; Sustainable Computing
  • Sushil Prasad: Parallel and Distributed Computing; Parallel Algorithms and Data Structures; Data Intensive Computing over Geospatial and Polar Datasets; Parallel Software Systems; Energy-efficient Deep Learning Models for Edge Devices
  • Paul Rad: Data Analytics and Artificial Intelligence; Decision Making and Autonomy; Machine Learning Algorithm ; Distributed Real-Time Computing
  • Ravi Sandhu: Access Control Models and Systems; Secure Cloud Computing; Secure Cloud Enabled IoT/CPS; Holistic Security
  • Wei Wang: Cloud Computing; Software Engineering; Applied Artificial Intelligence; Parallel Computing; Compilers; Computer Architecture; Software Reliability
  • Mimi Xie: Self-powered IoT devices; Software and hardware co-design of embedded system; Security and reliability of emerging memory technologies; Design and optimization of computer
  • Dakai Zhu: Embedded Systems; Real-Time Systems; Parallel and Distributed Systems; High Performance Computing

 

Software Systems

Software systems deliver novel computing technologies such as algorithms, intelligence, and analytics, and connect them to the end users. The department's research on software systems covers a variety of research topics towards enhancing software productivity, quality, user experience, scalability, etc. These research projects develop (1) novel features of programming languages and Integrated Development Environments (IDEs) to accelerate software development, (2) formal methods, program analysis and testing techniques to systematically reduce software defects and policy violations, (3) human-computer interfaces based on emerging platforms such as mobile devices and virtual/augmented reality, and (4) scalable software systems over big data.  

  • Kevin Desai: Computer Vision; Virtual/Augmented/Mixed Realities; Collaborative Virtual Environments
  • Mitra Hosseini: Requirements Engineering; Privacy Engineering; Natural Language Processing
  • Amanda Fernandez: Artificial Intelligence; Machine Learning; Computer Vision
  • Dhireesha Kudithipudi: Brain-Inspired AI; Energy Efficient Machine Intelligence; Computer Architecture
  • Jianwei Niu: Software Engineering; Cyber Security; Privacy Compliance ; Data Science; Formal Methods
  • John Quarles: Human-Computer Interaction; Virtual, Augmented, and Mixed Realities
  • Sushil Prasad: Parallel and Distributed Computing; Parallel Algorithms and Data Structures; Data Intensive Computing over Geospatial and Polar Datasets; Parallel Software Systems; Energy-efficient Deep Learning Models for Edge Devices
  • Jianhua Ruan: Bioinformatics; Computational Systems Biology
  • Rocky Slavin : Mobile and IoT Privacy; Software Engineering; Security Patterns; Computer and Information Security; Natural Language Processing
  • Wei Wang: Cloud Computing; Software Engineering; Applied Artificial Intelligence; Parallel Computing; Compilers; Computer Architecture; Software Reliability
  • Xiaoyin Wang: Software Engineering; Program Languages and Analysis; Software Security; Natural Language Processing