Kushankur Ghosh

Area of Study / Keywords

Anomaly/Outlier Detection Computer Security Machine Learning Data Mining

About

Kushankur Ghosh is a PhD student at the University of Alberta, supervised by Dr. Euijin Choo and Dr. Jörg Sander. His research focuses on building adaptive intrusion detection systems. His research interests encompass anomaly/outlier detection, unsupervised/self-supervised learning, and imbalance learning. Ghosh did his MSc under the supervision of Dr. Jörg Sander and Dr. Murilo Naldi in the field of unsupervised outlier detection where he studied a new direction to achieve parameter free outlier detection.

Previously, Ghosh studied the impact of data-driven and structural complexities in Deep Learning under the supervision of Dr. Nathalie Japkowicz. During his undergraduate studies, Ghosh worked on imbalanced and overlapping problems in domains like Sentiment Analysis, and Sarcasm Detection.

Research

• Anomaly/Outlier Detection
• Computer Security
• Machine Learning
• Data Mining

Teaching

• CMPUT 175: Introduction to the Foundations of Computation II (WINTER 2025)
• CMPUT 174: Introduction to the Foundations of Computation I (WINTER 2024)
• CMPUT 275: Introduction to Tangible Computing-II (WINTER 2024)
• CMPUT 274: Introduction to Tangible Computing-I (FALL 2023)
• CMPUT 175: Introduction to the Foundations of Computation II (WINTER 2023)
• CMPUT 274: Introduction to Tangible Computing-I (FALL 2022)
• CMPUT 175: Introduction to the Foundations of Computation II (WINTER 2022)
• CMPUT 274: Introduction to Tangible Computing-I (FALL 2021)

• GitHub
• Google Scholar
• LinkedIn