Research

Research Interests

Machine Learning, Pattern Recognition, Multi-resolution Sensor Fusion, Target Detection, Dimensionality Reduction and Manifold Learning, Learning from Imprecise and Uncertain Labels, Weak Learning, Multiple Instance Learning, Metric Embedding

Summary

I currently conduct research in the Machine Learning and Sensing Lab (MLSL) at the University of Florida. Our lab is dedicated to advancing methods for the analysis of remotely-sensed data. Presently, I am funded by the Office of Naval Research to develop Machine Learning algorithms for target detection and environmentally-aware semantic segmentation. For the remainder of my PhD studies, I plan to investigate methods for discriminative manifold embedding which learn from weakly-labeled training data. My research is applicable to a broad range of tasks, including: target recognition in hyperspectral imagery, self-driving vehicles, medical diagnosis, land-use classification, manufacturing, meteorology, environmental monitoring, economic forecasting and more.

Current Research Projects

• Superpixel Segmentation and Texture Feature Learning for Multi-Aspect Underwater Scene Understanding
  Funding Agency: Office of Naval Research
  Role: Graduate Research Assistant; PI: Dr. Alina Zare, University of Florida
  Dates: June 2021 - Present

Completed Research Projects

• Aided Target Recognition using Imprecise and Uncertain Data
  Funding Agency: Army Research Office
  Role: Graduate Research Assistant; PI: Dr. Alina Zare, University of Florida
  Dates: May 2019 - May 2021

• Multi-Sensor Fusion for Buried Object Detection
  Funding Agency: Army Research Office
  Role: Graduate Research Assistant; PI: Dr. Alina Zare, University of Florida
  Dates: April 2017 - May 2019