Computer Sciences

Deep learning has shown superior performance for automating image recognition tasks, exceeding human capabilities in both time and accuracy. Histopathology diagnostics is one of the more popular challenges at the intersection of artificial intelligence, computer vision, and medicine. Developing methods to automatically segment and detect pathologies in digitized histology slides imposes unique challenges due to the large size of these images and the complexity of the features present in biological tissue.

Student: Abodh Poudyal
Degree: Electrical Engineering Ph.D.
Title: Resilience Planning and Optimization of Electric Power Systems against Extreme Weather Events

Student: Andrii Zhuravchak
Degree: Computer Engineering MS
Thesis Title: Energy-Efficient Wearable Activity Recognition through Activity-Aware Sensor Data Compression and exploring the usage of Ultra-Wideband Radars for HAR

Student: Rabayet Sadnan
Degree: Electrical and Computer Engineering PhD
Thesis Title: Distributed Computation and Optimization for Electric Power Distribution Systems

Student: Suraiya Akhter
Degree: Computer Science MS
Thesis Title: Machine Learning-Based Prediction of Bacteriocins via Feature Evaluation

Student: Vincent Lombardi
Degree: Computer Science MS
Thesis Title: Bootcamp Method for Training General Purpose AI Agents