Research in Computer and Information Science
Impactful solutions to real-world challenges.
Groundbreaking Projects
Our faculty are committed to pushing the boundaries of scientific research at the intersection of technology and engineering. Their work is dynamic and collaborative, applying computer science to solve complex problems across academic disciplines.
Faculty Research Areas in Computer and Information Science
Research on use of machine learning techniques in analysis of microarray data, bioinformatics, and computational biology. Funding by NIH and NSF.
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Our research sits at the intersection of cybersecurity and artificial intelligence, investigating how AI systems can be used to detect, analyze, and defend against emerging threats, as well as understanding the security implications of increasingly capable AI models.
We are particularly interested in how general-purpose intelligence emerges in neural networks, how these advances reshape the threat landscape, and in developing principled approaches for deploying AI safely in security-critical settings.
Faculty:
- Dr. Bocheng Chen
Research on a number of areas in future computer networking field, including wireless sensor/mesh networks, peer-to-peer networks, socialized content sharing, cyber-physical systems, internet of things, cloud/edge computing, crowdsourcing, computer networking for big data, computer networking for AI, and AI for computer networking. Funding by NSF.
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Research on hardware architecture and compilers for CPU-GPU heterogeneous system, and high-performance computing. Current work focuses on optimizing GPU/HSA memory architecture and accelerating computer vision applications on mobile GPUs. Funding by industries and NSF.
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Analysis and retrieval of audio, image, and video data using pattern recognition and machine learning techniques. Funding by NSF.
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Research areas include adversarial machine learning, blockchain security, wearable device security, and privacy-preserving machine learning.
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Research lies at the intersection of Cybersecurity and Human-Computer Interaction, with a focus on understanding and improving users' security, privacy, trust, and interaction with emerging technology and tools.
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Our research focuses on computer vision, foundation models, and multimodal learning, with an emphasis on developing robust and generalizable AI systems for human-centric tasks. Our work explores multimodal foundation models like vision-language models (VLMs), aiming to adapt and improve them for robust and generalizable human-centric understanding in areas such as human pose estimation and human-object interaction.