Research in AI and machine learning spans core algorithmic foundations, large-scale models, data-centric approaches, and interdisciplinary applications. Emphasis is placed on building trustworthy, explainable, and robust AI systems that operate reliably in safety-critical domains such as healthcare, finance, and autonomous systems. The department also advances AI hardware and novel training paradigms, including federated and decentralized learning.

Representative topics include:

• Large language models
• Trustworthy and explainable AI
• Federated learning
• AI hardware
• Data-centric and safe AI
• AI for health, finance, and science

This thrust explores the interface between computer science and economics, focusing on how computational systems interact with agents in large-scale networks and marketplaces. Research includes algorithmic game theory, mechanism design, decentralized decision-making, approximation algorithms, and economic aspects of networked and distributed systems. These methods have broad applications in online markets, financial networks, resource allocation, and multi-agent systems. Representative topics include:

• Algorithmic game theory
• Mechanism design
• Strategic agents in networks
• Economics of distributed systems
• Approximation and optimization algorithms
• Computational finance and financial networks

In addition to the research thrusts, there are many cross-cutting domains and applications of interest. Representative topics include:

• Health Informatics
• Financial Analytics and FinTech
• Scientific and High Performance Computing
• Bioinformatics and Medical Informatics
• Autonomous Systems
• Education and Open Source Software

This thrust addresses the design of reliable, scalable, and adaptive distributed systems for both cloud-scale and embedded environments. Research includes real-time systems, middleware for edge/cloud computing, cyber-physical system security, and verification for safety-critical applications such as autonomous systems and flight control. Representative topics include:

• Parallel and distributed computing
• Edge and cloud computing
• Middleware and adaptive systems
• Cyber-physical systems (CPS)
• Real-time and exascale systems
• Safety, verification, and sensor fusion

This thrust focuses on understanding and modeling large, interconnected systems such as social, biological, and information networks. Faculty develop algorithms for graph mining, resilience analysis, complex system modeling, and graph-based learning with applications in epidemiology, bioinformatics, cybersecurity, and financial systems. Representative topics include:

• Social networks
• Graph learning and network mining
• Resilience and percolation
• Complex systems modeling

Bioinformatics and health networks

Leveraging RPI’s on-campus IBM quantum computer, faculty advance quantum computing across multiple layers of the stack, including architecture, compilers, and algorithms, with applications to optimization, machine learning, and scientific computing. Research also explores how quantum techniques intersect with classical AI, networks, and security. Representative topics include:

• Quantum architecture and compilers
• Quantum machine learning
• Quantum optimization algorithms
• Hardware-software co-design
• Quantum applications to AI and networks

This thrust focuses on ensuring the security, privacy, and trustworthiness of computing systems at all levels, from hardware to distributed infrastructures. Research includes privacy-preserving machine learning, federated data sharing, secure cyber-physical systems, and blockchain-based decentralized security models. Representative topics include:

• Data privacy and secure AI
• Secure cyber-physical systems
• Privacy-preserving distributed learning
• Blockchain and decentralized systems
• Trust in autonomous and financial systems

Faculty in this area advance the development of semantic web technologies and knowledge graphs to enable intelligent information integration, reasoning, and explainability across diverse domains. Research includes ontologies, hybrid symbolic-neural reasoning, decentralized intelligent systems, and applications to web science, health informatics, and financial technology. Representative topics include:

• Knowledge graphs and ontologies
• Semantic web and web science
• Hybrid AI reasoning
• Explanation systems
• Decentralized intelligent systems
• Fintech and health informatics applications

This area develops advanced methodologies for building reliable and secure software systems. Research includes program analysis, formal verification, compilers, concurrent programming models, and correctness of both classical and quantum software stacks. Representative topics include:

• Program analysis and verification
• Compilers and programming languages
• Concurrent and adaptive systems
• Reliable and secure software engineering

Quantum software stacks