Technology Industry Expert
AI Researcher · Ph.D. Candidate, Old Dominion University
Lucas Hu is a Ph.D. candidate in Computer Science at Old Dominion University, specializing in large language models, symbolic regression, AI security, and semantic communication. His research in AI for Science explores applying machine learning to physics and fundamental sciences, while also investigating adversarial and backdoor attacks on efficient AI models. His work has been published at top venues including IEEE MILCOM.
Ph.D.
Computer Science
Doctoral candidate at Old Dominion University, advised by Dr. Yaohang Li
LLMs
Large Language Models
Research on LLMs with symbolic regression for scientific applications
IEEE
Published Researcher
Paper accepted at IEEE MILCOM 2025 on semantic communication systems
AI Safety
Security Research
Adversarial attacks, backdoor attacks, and robustness of efficient AI models
Lucas's core research areas in AI and computer science
Deep expertise in LLM research, including applying language models with symbolic regression to solve complex scientific problems in physics and beyond.
Research on adversarial attacks and backdoor attacks targeting neural networks, with a focus on ensuring the security and robustness of efficient AI models.
Pioneering work at the intersection of AI and physics — using machine learning to accelerate scientific discovery and automate research processes.
Published research on contrastive multi-hop semantic communication systems, advancing the integration of AI into next-generation networks.
Experience in remote sensing image analysis and object detection using convolutional neural networks, with published work on airplane detection.
Active reviewer for international conferences, science fair judge, and contributor to the broader AI research community.
Lucas's current core research topics
Applying large language models and symbolic regression techniques to physics and scientific discovery, bridging AI with fundamental science.
Research on adversarial attacks, backdoor attacks, and security of efficient AI models — ensuring AI systems are reliable and trustworthy.
Published at IEEE MILCOM 2025 on contrastive multi-hop semantic communication, advancing next-generation communication systems with AI.
Old Dominion University
Advisor: Dr. Yaohang Li
Harbin University of Science and Technology
Harbin University of Science and Technology
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