Hoomaan Sajjadi
About Hoomaan Sajjadi
Hoomaan Sajjadi is an AI Engineer with a background in machine learning and a focus on health applications. He has worked at various organizations, including Scale AI and Kryptowire Labs, and has conducted research in AI and medical prediction models.
Work at ScaleAI
Hoomaan Sajjadi currently serves as an AI Engineer at Scale AI, a position he has held since 2023. His role involves applying artificial intelligence techniques to enhance data processing and model development. He works remotely from the United States, contributing to projects that aim to improve AI capabilities in various applications.
Previous Experience at Kryptowire Labs
Prior to his current role, Hoomaan Sajjadi worked at Kryptowire Labs as a Machine Learning Researcher for a brief period in 2022. His tenure lasted for two months and was conducted in a hybrid work environment in the United States. During this time, he focused on machine learning applications relevant to cybersecurity.
Experience at Cerevu Medical
Hoomaan Sajjadi held the position of Machine Learning Engineer at Cerevu Medical from 2023 to 2024. This role was remote and lasted for five months, during which he contributed to the development of machine learning solutions in the medical field, aligning with his interests in health applications.
Education and Expertise
Hoomaan Sajjadi earned a Master's degree in Computer Science from Texas A&M University, where he studied from 2018 to 2022. His academic focus included extensive research in artificial intelligence and machine learning, particularly in medical prediction models and domain adaptation in deep learning. He also holds a Bachelor of Science in Electrical and Electronics Engineering from Sharif University of Technology, obtained from 2013 to 2018.
Research and Projects
During his time at Texas A&M University, Hoomaan Sajjadi conducted significant research in AI and machine learning. His work emphasized medical prediction models and the adaptation of deep learning techniques. He also completed a summer internship focused on deep learning models for human activity and context recognition, where he explored data processing methods to enhance model accuracy.