Research

General Research Interests: Machine Learning, Data Mining, Artificial Intelligence, and Pattern Recognition.

Research Focus: Learning from Data Sequences/Data Streams, Learning from Spatio-temporal Data, One-shot Learning, Transfer Learning, Anomaly Detection, Prediction and Explanation.

Application Domain: Spatio-Temporal, and Industrial Applications.

Current Research Projects:

• Issues and Problems related to Conformal Prediction (Graph and Privacy Issues)

• Anomaly Detection for Dynamic Graphs (Sponsor: NSF)

• Cooperative AI Inference in Vehicular Edge Networks for Advanced Driver-Assistance Systems (Sponsor: NSF) 

• Sports Analytics using Graph-driven Approaches  

Previous Research Projects: 

• Surface Defect Detection and Classification (Sponsors: Rolls-Royce, NRF, NTU)

• Garage Parking Issues in Cities (Sponsor: BMW)

• Issues in SpatioTemporal Data Mining

• Learning and Mining in Moving Object Databases. (Sponsor: NASA)

• Optimization Methods for Machine Learning Problems. 

• Cyclone Tracking using Multiple Heterogeneous Satellite Data Sources. (Sponsor: NASA)

• Detecting Changes in Data Streams by Testing Exchangeability.

• Active Learning in Data Streams by Transduction. 

• Nonlinear Rescaling Method for Machine Learning and Data Mining. (Sponsor: NSF)

• Analysis of Micro-Doppler Signatures in Radar. (Sponsor: NRL)  

• Continuous and Lifelong Learning

• Anomaly Prediction and Detection in Spatio-Temporal Domain (Sponsors: NSF, RU-Seed Fund, ASRC Federal Mission Solutions)

• Learning Incentization Strategy Using Reinforcement Learning in Spatio-temporal Domain.