Research

Vision

The Machine Learning Laboratory research focuses on building human-machine collaborative AI systems that can learn context-aware and explainable models that are robust to both noise and limited supervision. We develop deep multi-source learning techniques including reinforcement, few/zero-shot, relational/graph and self-supervised learning. We explore multi-source learning strategies including knowledge-enhanced learning (prompts, interdependency structure, data augmentation), multimodal learning (text, image, video, audio, sensor), multi-model learning (ensemble, federated), and hybrid learning (data-driven, physical-model-based). We develop our research in the context of natural language processing, time-series prediction, and control. Our research spans applications such as AI ethics, computer vision, cyber security and healthcare.

Research Topics

• Multisource Learning for Natural Language Information Extraction
• Multisource Learning for Timeseries Prediction
• Multisource Learning for Control

Applications

• Cyber Security
• Computer Vision
• Healthcare
• AI Ethics
• Learning from Interconnected Data

Research Sponsors

• Intelligence Advanced Research Projects Activity (IARPA)
• National Science Foundation (NSF)
• Department of Energy (DoE)
• National Institute of Health (NIH)
• Commonwealth Cyber Initiative (CCI)
• Siemens
• Adobe
• Virginia Tech