Faculty working in this area
| Faculty | website | |
|---|---|---|
| Jeremy Blackburn | jblackbu@binghamton.edu | |
| Weiying Dai | wdai@binghamton.edu | |
| Madhusudhan Govindaraju | mgovinda@binghamton.edu | |
| Weiyi Meng | meng@binghamton.edu | |
| Yingxue Zhang | ||
| Zhongfei (Mark) Zhang | zzhang@binghamton.edu |
Highlights in this area
researches a better understanding of how people behave online. He is particularly
interested in 鈥渂ad鈥 behavior and has studied how cheating spreads like a disease in
a social network of gamers, mis- and disinformation, online extremism and memes. As
part of this broader work, he is building practical tools and systems for large-scale
data collection and analysis with the project.
researches medical imaging, healthcare bioinformatics, biomedical image processing, functional magnetic resonance imaging (fMRI), machine learning and pattern recognition. She co-directs the Center for Advanced Magnetic Resonance Imaging Sciences (CAMRIS). She is working on the aging-related brain patterns, imaging biomarkers for schizophrenia and diabetes, formation of brain folding patterns, automatic sleep stage learning, and LLM and deep learning on fMRI image registration and image reconstruction.
researches distributed systems, cloud computing, big data, and high-performance computing.
researches database and information retrieval systems. He leads the Database and Information Retrieval Laboratory. He is working on entity mention detection and named entity recognition (NER) from social media streams, source selection in distributed information retrieval, top-N query processing and sentiment analysis.
researches:
- Spatial-temporal data science, AI, with applications on urban intelligence and smart cities.
- Human behavior analysis and human decision making analysis with data-driven AI approaches including imitation learning and offline reinforcement learning.
Accordingly, she is currently working on theoretical research on offline reinforcement learning, and applications on contrastive learning, model pretraining and offline reinforcement learning related to smart cities.
researches machine learning and artificial intelligence, data mining and knowledge discovery, multimedia indexing and retrieval, computer vision and image understanding, and pattern recognition. Accordingly, he is currently working on several projects in these areas including LLM compression, multimodal data learning, out of domain learning, learning with noise and novelty learning.