Parsa Hosseini, Ph.D.
Country: United States of America
Position: Senior Data Scientist
Parsa Hosseini is a senior data scientist at Tesla focusing on the machine learning initiative. He works towards developing machine learning and deep learning algorithms for innovative applications. He has been an adjunct lecturer and faculty member with several universities since 2009 and currently is with Santa Clara University. His research focuses on machine learning, deep learning, signal and image processing. He received the PhD degree in electrical and computer engineering with research in computer science from Rutgers University, New Brunswick, NJ, the USA in 2018. He has served on the scientific committees and review boards of several national and international conferences and journals. He is a senior member at IEEE.
Deep learning is one of the most widely used machine learning techniques which has achieved enormous success in applications such as anomaly detection, image detection, pattern recognition, and natural language processing. Deep learning architectures have revolutionized the analytical landscape for big data amidst wide-scale deployment of sensory networks and improved communication protocols. In this talk, we will discuss multiple deep learning architectures and explain their underlying mathematical concepts. An up-to-date overview here presented concerns three main categories of neural networks, namely, Convolutional Neural Networks, Pretrained Unsupervised Networks, and Recurrent/Recursive Neural Networks. Applications of each of these architectures in selected areas such as autonomous driving, information technology and medical diagnosis are also discussed.