Faculty working in this area
| Faculty | website | |
|---|---|---|
| Jeremy Blackburn | jblackbu@binghamton.edu | iDRAMA Lab |
| Weiyi Meng | meng@binghamton.edu | Database and Information Retrival Laboratory |
| Sujoy Sikdar | ssikdar@binghamton.edu | Sikdar's Group |
| Zhongfei (Mark) Zhang | zzhang@binghamton.edu | Multimedia Research Lab |
Highlights in this area
Jeremy Blackburn researches a better understanding of how people behave online. He is particularly
interested in “bad” 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 https://iDRAMA.cloud project.
Weiyi Meng 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.
Sujoy Sikdar researches the intersection of computer science, artificial intelligence, economics and social science in understanding individual and group preferences, how preferences are aggregated in systems composed of multiple agents, and designing algorithms to make good decisions for groups of heterogeneous agents. Some examples:
- Designing fair and efficient algorithms for group decision-making problems like fair division and voting.
- Learning and modeling preferences from data.
- Understanding human behavior in a variety of social contexts including in social media streams.
Zhongfei (Mark) Zhang 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.
