Research Topics
Computational complexity theory focuses on classifying computational problems as 'easy' or 'hard' according to their resource usage. One of the roles of computational complexity theory is to determine the practical limits on what computers (and human beings) can and cannot do. For example, you may learn an undecidable problem named the Halting Problem in CMSC340. This means, no matter how smart a person is, one cannot find an algorithm to solve the Halting Problem.
Formal languages provide the theoretical underpinnings for the study of programming languages as well as the foundations for compiler design. They are important in such areas as the study of biological systems, data transmission and compression, computer networks, etc. In computational complexity theory, decision problems are typically defined as formal languages and complexity classes are defined as the sets of formal languages. In logic and foundations of mathematics, formal languages are used to represent the syntax of axiomatic systems.
In mathematics and computer science, computable analysis is the study of mathematical analysis from the perspective of computability theory. It was motivated by questions such as: which real numbers and real number functions are computable, and which mathematical tasks in analysis can be solved by algorithmic means? It is concerned with the parts of real analysis and functional analysis that can be carried out in a computable manner.
Quantum computing encompasses a broad study of computation grounded in quantum mechanics, including key phenomena such as superposition and entanglement. In this area, we focus on quantum computational models such as quantum finite automata, which illustrate how quantum behavior influences language recognition and the use of computational resources. These models clarify distinctions between classical and quantum approaches in complexity theory and support applications in secure communication, physical system simulation, and emerging computational architectures.
Artificial intelligence (AI) studies the development of computer systems capable of performing tasks that typically require human intelligence, such as learning, reasoning, perception, decision making, and language understanding. Modern AI encompasses machine learning, deep learning, large language models, and intelligent multi-agent systems. In this area, we investigate AI techniques for applications including medical image analysis, software engineering, and time series forecasting, with an emphasis on trustworthy, collaborative, and hybrid AI systems.
Cybersecurity focuses on protecting computer systems, software, and digital information from attacks, vulnerabilities, and unauthorized access. It encompasses secure software development, formal verification, automated software analysis, and vulnerability assessment. In this area, we investigate theoretical foundations for software correctness and security through formal methods and computational models, while also exploring the application of artificial intelligence to software analysis and vulnerability detection.
Selected Publications
2026
- [JOURNAL PAPER] J. Xie, C.-S. Lin, and H. B. Hunt III, "Two-Way Nondeterministic Finite Automata with Quantum and Classical States and Their Limits". Information and Computation, vol. 312, article 105489, 2026. [Article]
- [JOURNAL PAPER] J. Xie, C.-S. Lin, H. B. Hunt III, and R. E. Stearns, "On One-Way and Two-Way Cellular Automata: Bi-Productiveness, Rice-Style Theorems, and Succinctness". Theoretical Computer Science (Elsevier), accepted for publication, 2026. [Preprint]
- [JOURNAL PAPER] J. Xie, C.-S. Lin, H. B. Hunt III, and R. E. Stearns, "A Practical Extension of Computational Complexity Theory for Applications in Mathematics and Sciences". Theory of Computing Systems, vol. 70, article 15, 2026. [Full Text]
- [CONFERENCE PAPER] C.-N. Tsai and J. Xie, "A Hybrid Prompt Framework with Contrastive Semantic Alignment for Zero-shot Cross-lingual Classification". 2026 IEEE International Conference on Big Data (IEEE BigData 2026), accepted regular paper, 2026 (19.4% acceptance rate).
- [JOURNAL PAPER] J.-R. Sheu, J. Xie, C.-N. Tsai, C.-Y. Hsieh, and C.-S. Lin, "A Class-Wise Quantum Relational Calibration Network for Brain Tumor Diagnosis". Scientific Reports, vol. 16, article 24012, 2026. [Article]
- [JOURNAL PAPER] C.-C. Wang, F.-C. Liu, J. Xie, C.-M. Lai, and C.-S. Lin, "Exploring the Impact of Persona-Based Large Language Models on User Experience in Educational Settings". Education and Information Technologies, vol. 31, pp. 7329–7349, 2026. [Full Text]
- [UNDER REVIEW] J. Xie, C.-S. Lin, and H. B. Hunt III, "Direct Invalid-Computation Checking for Real-Time Reversible Circular One-Way Cellular Automata". Submitted to Information Processing Letters (Elsevier), 2026. [Preprint]
- [SUBMITTED] J. Xie and C.-S. Lin, "Adaptive Stopping of Noisy Quantum Syndrome Extraction under Feedback Costs". Submitted to the 2027 IEEE International Symposium on Circuits and Systems (ISCAS 2027), 2026.
- [IN PREPARATION] J. Xie, C.-S. Lin, and H. B. Hunt III, "Fixed-Policy Verification for Two-Way Nondeterministic Finite Automata with Quantum and Classical States". Manuscript in preparation, 2026.
- [IN PREPARATION] C.-N. Tsai, J. Xie, C.-M. Lai, and C.-S. Lin, "A Multi-Layer Cooperative Multi-Agent Framework for Time Series Forecasting with Large Language Models". Manuscript in preparation, 2026.
2025
- [JOURNAL PAPER] J. Xie, H. B. Hunt III, and R. E. Stearns, "On the Computational and Descriptional Complexity of Multi-Pattern Languages". Theoretical Computer Science, vol. 1030, article 115063, 2025. [Article]
- [JOURNAL PAPER] J. Xie, H. B. Hunt III, and R. E. Stearns, "Decision Problems Concerning L Systems". Theory of Computing Systems, vol. 69, article 34, 2025. [Full Text]
- [JOURNAL PAPER] C.-N. Tsai, J. Xie, C.-M. Lai, and C.-S. Lin, "Leveraging Intra- and Inter-References in Vulnerability Detection Using Multi-Agent Collaboration Based on LLMs". Cluster Computing, vol. 28, article 1004, 2025. [Article]
- [JOURNAL PAPER] J. Xie, C.-S. Lin, and H. B. Hunt III, "Parallel Communicating Finite Automata: Productiveness and Succinctness". Mathematics, vol. 13, no. 8, article 1265, 2025. [Article]
2024
- [JOURNAL PAPER] J. Xie, H. B. Hunt III, and R. E. Stearns, "Pumping Lemmas Can Be 'Harmful'". Theory of Computing Systems, vol. 68, no. 5, pp. 1339–1352, 2024. [Article]
- [JOURNAL PAPER] J. Xie, H. B. Hunt III, and R. E. Stearns, "On Productiveness and Complexity in Computable Analysis through Rice-Style Theorems for Real Functions". Mathematics, vol. 12, no. 20, article 3248, 2024. [Article]
2023
- [JOURNAL PAPER] J. Xie and H. B. Hunt III, "On the Undecidability and Descriptional Complexity of Synchronized Regular Expressions". Acta Informatica, vol. 60, pp. 257–278, 2023. [Article]
2017
- [PH.D. DISSERTATION] J. Xie, "Complexity Theoretic Parallels among Automata, Formal Languages and Real Variables including Multi-Patterns, L-Systems and Cellular Automata". Ph.D. dissertation, University at Albany, State University of New York, Albany, NY, 2017. [Dissertation]