Jade Dergevorkian

Project Title: Visual Anomaly Detection

Jade Dergevorkian is a graduate student pursuing a Master's degree in Computer Science at California State University, Northridge (CSUN). She is passionate about leveraging deep learning and computer vision to develop AI solutions that address real-world challenges and create a positive societal impact. As a member of Dr. Hasan's GRAIL Lab, Jade contributes to GeoAI research projects focused on computer vision applications. She is currently conducting research on visual anomaly detection, with a focus on developing AI models capable of accurately identifying rare, unexpected, or defective patterns in complex visual environments.

Aaron Hoffman

 Project Title: Anomaly Detection in Medical Imaging

Aaron is a graduate student in the Department of Computer Science at CSUN. He has a strong interest in applying machine learning algorithms to advance innovative medical software. His current research focuses on detecting anomalies in medical imaging data. The goal is to help predict diseases that are not easily visible, even to experienced practitioners. This work aims to support earlier and more accurate diagnoses.

Cody Laurie 

Project Title: Anomaly Detection in Medical Imaging

Cody Laurie is a Master’s student studying Data Science at California State University, Northridge. With a strong foundation in computer science, algorithm design, time complexity, and machine learning, his academic and research interests center on designing efficient algorithms, statistical modeling, and anomaly detection across complex data domains. Cody's work focuses on developing a novel and computationally efficient anomaly detection algorithm for medical image data. Beginning with brain MRI scans, the long-term goal of this research is to create a scalable and accurate framework capable of identifying both subtle "early stage" and more pronounced anomalies to assist medical professionals

Vaishnavi Sen 

Project Title: Fighting Fire with Fire: Deepfake Detection

Vaishnavi is an international undergraduate student majoring in Computer Science at CSUN. She is passionate about leveraging artificial intelligence and emerging technologies to drive meaningful advancements in society. Her current research focuses on detecting AI-generated text, with the goal of combating misinformation, promoting academic integrity, and supporting the responsible use of technology.

Luis Olmos 

Project Title: Graph Anomaly Detection

Luis Olmos is a dedicated researcher in the lab, contributing to innovative work in anomaly detection for graph-structured data. He was the recipient of the CSUN-LSAMP Research Grant for Fall 2024 and Spring 2025, and was competitively selected for the prestigious SMART Scholarship by the U.S. Department of Defense. He has published three peer-reviewed conference papers showcasing his research contributions. Currently, Luis is developing a novel anomaly detection algorithm for graph-structured data.