Department of Computer Engineering

As the AI and Software Lead at Ariapa, I am responsible for guiding the development of innovative AI products that utilize advanced computer vision techniques. Our extensive product range includes industrial robots, face detection systems, and license plate recognition systems, among others. We employ a wide array of frameworks and technologies, such as Deep Learning, Computer Vision, and Machine Learning, to ensure the highest quality in our products. My role is to steer our software development and AI initiatives, with a constant focus on delivering cutting-edge and effective solutions to our clients.

As a member of Ariapa, I contributed to the development of a masked face recognition product. Our product leverages computer vision, deep learning, and machine learning technologies to accurately recognize faces even when they are partially obscured by masks. To develop this product, our team utilized tools and frameworks such as TensorFlow, Keras, Python, and OpenCV.

As a member of the Faraabeen team, I contributed to the development of a Network Traffic Analyzer product based on the Netflow protocol. Using machine learning and artificial intelligence, our product detects network behavior and anomalies. To develop this product, our team utilized a set of frameworks and technologies including Docker, Fast API, ElasticSearch, and Redis.

Jeekobook is a social media app for book lovers. Members can share and discover their favorite books, write reviews, rate books, and connect with other readers.

As a Visiting Researcher at Utrecht University, I specialized in the field of computer vision and pattern recognition. My work focused on image understanding and retrieval, and my research was published in the Pattern Recognition journal under the title “Understanding Image Concepts using the ISTOP Model.”

As a researcher at the Advanced Image and Signal Processing research center, my work was centered on developing innovative methods for automatic tracking of driver’s awareness and drowsiness detection. Our team’s groundbreaking research was recognized with the registration of IRI patent 80564.