haron Zhou is a highly accomplished PhD candidate in Computer Science at Stanford University, where she is advised by the renowned AI expert Andrew Ng. Her academic work and research span the theoretical and applied aspects of Artificial Intelligence (AI), with a focus on Generative Adversarial Networks (GANs), machine learning, and applications that drive social good, including medicine and climate science. Sharon has a passion for bridging the gap between cutting-edge AI technology and its real-world applications for human welfare.Before embarking on her PhD journey, Sharon built a strong foundation in machine learning product management, having worked at Google and various startups. Her experience at Google honed her ability to apply machine learning and AI to create impactful products and solutions for large-scale audiences. Sharon's diverse academic background includes a degree in Computer Science and Classics from Harvard University, giving her a unique interdisciplinary perspective on AI and technology.Sharon’s approach to AI is deeply human-centered, with a clear passion for using technology to address pressing global challenges. While she is enthusiastic about the power and potential of AI, particularly Generative Adversarial Networks (GANs), she values the importance of human understanding and context in the development and deployment of AI systems.

Eda Zhou is a Curriculum Developer at DeepLearning.AI, where she brings her expertise in AI, machine learning, and cybersecurity to create educational content that empowers learners in these fast-evolving fields. Eda holds both a Bachelor's and Master's degree in Computer Science from Worcester Polytechnic Institute (WPI), with a specialized focus on cybersecurity. Her strong academic background and hands-on experience drive her passion for applying new technologies, particularly in the realms of AI and machine learning, to safeguard computer networks and protect users.Eda’s interests extend into exploring how cutting-edge machine learning techniques, such as Generative Adversarial Networks (GANs), can be applied to enhance security measures. Her curiosity about the intersection of AI and cybersecurity has led her to focus on building innovative solutions for threat detection, data security, and overall protection of digital infrastructures.As a curriculum developer at DeepLearning.AI, Eda has contributed to the development of several Generative Adversarial Networks (GANs) courses, helping learners grasp the fundamental and advanced aspects of this powerful AI technique. Her courses guide students through building, applying, and enhancing GANs, providing them with the knowledge to leverage these models in various applications, including data generation and security.

Eric Zelikman is a passionate Deep Learning Engineer and Curriculum Engineer at DeepLearning.AI. With a strong academic background and a fascination for machine learning and representation learning, Eric is dedicated to exploring how algorithms can learn meaningful, efficient, and robust representations. He holds a degree in Symbolic Systems from Stanford University, where he focused on the intersections of machine learning, artificial intelligence, and cognitive science. His research interests revolve around disentangled representations in machine learning models and how these can be used across various fields of AI to build more interpretable and effective systems.Eric’s work is centered on the belief that machine learning algorithms can provide insights not only for improving AI systems but also for understanding more about human cognition and real-world challenges. His ultimate goal is to leverage AI and ML techniques to address some of the most pressing issues facing humanity today. As a Curriculum Engineer at DeepLearning.AI, Eric contributes to the creation of cutting-edge educational content that helps students learn and implement Generative Adversarial Networks (GANs), one of the most powerful tools in modern deep learning.