Artificial Intelligence: Knowledge Representation, Machine Learning, Data Mining, and Causal Discovery | College of Information Sciences and Technology
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Artificial Intelligence: Knowledge Representation, Machine Learning, Data Mining, and Causal Discovery

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About: The goal of Artificial Intelligence (AI) is to understand intelligence by constructing computational models of intelligent behavior. This entails developing and testing falsifiable algorithmic theories of (aspects of) intelligent behavior, including sensing, representation, reasoning, learning, decision-making, communication, coordination, action, and interaction. AI is also concerned with the engineering of systems that exhibit intelligence. Machine learning is concerned with the scientific study, exploration, design, analysis, and applications of algorithms that learn concepts, predictive models, behaviors, action policies, etc. from observation, inference, and experimentation and the characterization of the precise conditions under which classes of concepts and behaviors are learnable. Learning algorithms can also be used to model aspects of human and animal learning. Machine learning integrates and builds on advances in algorithms and data structures, statistical inference, information theory, signal processing as well as insights drawn from neural, behavioral, and cognitive sciences. Data mining is concerned with the applications of statistical machine learning for exploratory analysis and predictive modeling from large data sets. Causal discovery is concerned with algorithms for eliciting the underlying causal (as opposed to the merely predictive) relationships from observational and experimental data.

Areas of Strength: Some areas of strength in Artificial Intelligence in the college include: knowledge representation and inference; machine learning (especially, statistical machine learning, neural networks e.g., deep learning, learning predictive models from sequence data, spatial data, network data, temporal data, relational data); genetic algorithm/evolutional computation; fuzzy logic; eliciting causal effects from experimental and observational data, including temporal and relational data; and applications in bioinformatics, health informatics, social informatics, learning analytics, text analytics, image analytics, and computational discovery, among others.

Faculty: Chao-Hsien Chu, Lee GilesVasant Honavar, Sharon Huang, Ting-Hao Kenneth Huang, Dongwon Lee, Jessie Li, Prasenjit Mitra, David ReitterJames Wang, Suhang Wang, Shomir Wilson, Amulya Yadav, John Yen, Zihan Zhou