Teresa J Di Meola (Harner)
About Teresa J Di Meola (Harner)
Teresa J Di Meola (Harner) is a Project Manager at Clostra, Inc. in the San Francisco Bay Area, where she leverages her dual background in software and mechanical engineering to develop AI-related products and tackle complex real-world problems.
Work at Clostra
Teresa J Di Meola has been serving as a Project Manager at Clostra, Inc. since 2018. In her role, she has contributed to the development of AI-related products by utilizing her dual background in software and mechanical engineering. She has been instrumental in brainstorming innovative ideas that have supported Clostra's growth. Her work involves applying sensor fusion techniques and deep convolutional neural networks (DCNNs) to tackle complex AI topics, including machine learning, deep learning, and reinforcement learning.
Education and Expertise
Teresa holds a Bachelor of Engineering (B.E.) in Mechanical Engineering from The City College of New York, where she studied from 1974 to 1977. She also completed a Master of Science (MS) in Computer Science with an emphasis on AI at the same institution from 1982 to 1984. Additionally, she pursued a Master of Fine Arts (MFA) in Fine Art and Game History/Design at the Academy of Art University from 2011 to 2014. Although she began PhD studies in electrical engineering at the Massachusetts Institute of Technology, she did not complete the program.
Background
Teresa has a diverse professional background that includes experience in materials science, process control, and hardware innovation. Before joining Clostra, she worked as a Senior Technical Writer/Engineer at Carl Zeiss Meditec for Lionbridge Technologies Inc. from 2015 to 2016. Her extensive engineering background has allowed her to participate in various projects that leverage her technical expertise and address real-world challenges.
Achievements
Throughout her career, Teresa has contributed to numerous successful AI-related products by exploring innovative methods for implementing unsupervised learning for self-automation. Her ability to define and specify AI-related software has been crucial in solving complex problems. She has experienced professional growth through meaningful work that positively impacts lives, reflecting her commitment to the field of engineering and technology.