Research Projects
- Predictive Models from Ultra-High-Dimensional Longitudinal Data (funded in part by grants from the National Science Foundation and the National Institutes of Health).
- Continual Learning (funded in part by a grant from the National Science Foundation and the Huck Chair in Biomedical Data Sciences and Artificial Intelligence held by Vasant Honavar at Penn State University)
- Hyperdimensional representations (funded in part by a grant from the National Science Foundation and the Huck Chair in Biomedical Data Sciences and Artificial Intelligence held by Vasant Honavar at Penn State University)
- Generative models and their applications (funded in part by a grant from the National Science Foundation and the Huck Chair in Biomedical Data Sciences and Artificial Intelligence held by Vasant Honavar at Penn State University)
- Knowledge-based machine learning (funded in part by a grant from the National Science Foundation and the Huck Chair in Biomedical Data Sciences and Artificial Intelligence held by Vasant Honavar at Penn State University)
- Predictive and Causal Modeling of Health Risks and Health Outcomes from Integrative Analyses of Clinical, Behavioral, Genomic and Socio-demographic Data (funded in part by grants from the National Institutes of Health).
- Explaining Machine Learned Predictive Models and their predictions (funded in part by a grant from the National Science Foundation).
- Accelerating Science through Advances in AI (funded in part by a grant from the National Science Foundation).
- Representing and Reasoning About Qualitative Preferences (funded in part by a grant from the National Science Foundation) Institute for AI-Enabled Materials Design, Discovery, and Synthesis (funded in part by a grant from the National Science Foundation).
- Virtual Data Collaboratory: A Federated Computational and Data Infrastructure for Collaborative, Data-Intensive Science (funded in part by a grant from the National Science Foundation).
- Genetic and environmentally-induced functional variation in the rice RNA structurome (funded in part by a grant from the National Science Foundation).
- Eliciting Causal Effects from Observational and Experimental Data (funded in part by a grant from the National Science Foundation and the Huck Chair in Biomedical Data Sciences and Artificial Intelligence held by Vasant Honavar at Penn State University)
- Relational Causal Models (funded in part by a grant from the National Science Foundation and the Huck Chair in Biomedical Data Sciences and Artificial Intelligence held by Vasant Honavar at Penn State University)
- Inferring Software Specifications from Open Source Repositories (funded in part by grants from the National Science Foundation)
- Computational Infrastructure for Sensitive Data Analysis (funded in part by grants from the National Science Foundation)
- Algorithms and Software for Knowledge Acquisition from Semantically Heterogeneous, Distributed Data (funded in part by grants from the National Science Foundation)
- Learning Predictive Models from Richly Structured Data
- Learning Predictive Models from Multi-Modal Data
- Topics in Grammatical Inference and Computational Learning Theory
- Federated Ontologies Knowledge Representation and Inference (funded in part by a grant from the National Science Foundation)
- Secrecy-Preserving Inference and Query Answering (Funded in part by a grant from the National Science Foundation)
- Algorithms and Software for Interactive Discovery and Composition of Web Services (Funded in part by a grant from the National Science Foundation)
- Comparative Analysis of Biomolecular Networks (funded in part by a grant from the USDA and in part by a National Science Foundation IGERT fellowship)
- Data-Driven Discovery of Macromolecular Sequence-Structure-Function-Interaction-Expression Relationships (in collaboration with Drena Dobbs and Robert Jernigan funded in part by a National Institutes of Health Grant 5R21GM066387)
- Biologically Inspired Algorithms for Knowledge Representation, Memory, Language Processing and Learning (Funded in part by a grant from the National Science Foundation)