Michtom School of Computer Science

M.A.P. | Mastery Assessment Pedagogy for Learning Computer Science

Open-Ended Dynamics of Coevolutionary Games

Video Background Modeling for Efficient Transformations Between Images and Videos

Cross-lingual Temporal and Modal Dependency Parsing

Between Visualizability and Abstractness: Deploying Visual Text to Detect Non-literal Language

Temporal Dependency Structure Modeling

Insightful Learning Systems: Supporting STEM Education with Accessible Cyberlearning

Efficient Methods in Deep Learning Lifecycle: Representation, Prediction and Model Compression

Cohesion in Online, Different Time and Place Collaborative Learning Environments

Retrospective Computations over Sets of Snapshots - Design, Implementation and Optimization

Temporal Dependency Structure Modeling

Complexity in Coevolution and Deep Learning

Turning Machine Learning Inward: Automating Cloud Databases

Robust and Efficient Techniques in Deep Learning with applications in Computer Vision and Language Understanding 

Problem Solving Markov Models and Recursive Pedagogy

Learning to Perform Actions from Demonstrations with Sequential Modeling

Abstract Meaning Representation Parsing

Learning-based Interactive Data Exploration

Monte Carlo Simulation Generation Through Operationalization of Spatial Primitives

Profiling and Quality Assessment of Query Optimizers

Toward a Theory of the Evolution of Modularity Using Sequential-Subtask Environments and Development

Devel-Op: An Optimizer Development Environment