Database, Data Mining and Big Data

Faculty working in this area

Faculty Email 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
Nancy Guo nguo1@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.
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Dr. Guo develops AI/ML algorithms that utilize big data for prediction and decision-making. Her research includes software reliability and precision medicine. Her recent research focuses on developing and applying foundation AI models for biomarker and drug discovery for cancer treatment. Examples of her algorithms include Dempster-Shafer belief networks and non-negative matrix factorization/Monte Carlo Simulation for modeling multi-omics pathways and networks.