David L.
About David L.
David L. is a Lead Machine Learning Engineer with extensive experience in machine learning and software engineering. He has worked in various roles, including as a Software Architect and Pre Doctoral Fellow, and holds a PhD in Computer Engineering from the University of Pittsburgh.
Current Role and Responsibilities
David L. serves as the Lead Machine Learning Engineer at Overview, a position he has held since 2021. In this role, he focuses on fault detection through machine learning, developing deep-learning models tailored for edge deployment in industrial manufacturing analytics. His work involves deploying machine learning models in distributed computing environments, which enhances the efficiency and reliability of industrial applications.
Previous Work Experience
Prior to his current role, David L. worked at several organizations. He was a Software and Systems Engineering Intern at EchoStar Corporation for two months in 2016. He served as a Software Architect at Adrich from 2019 to 2020. Additionally, he was a Pre Doctoral Fellow at SHREC: Center for Space, High-Performance, and Resilient Computing from 2017 to 2022. His early experience includes roles as an Undergraduate Researcher at South Dakota School of Mines and Technology and as a Sales Associate at Toys R Us.
Educational Background
David L. has an extensive educational background in computer engineering. He earned his Bachelor of Science (BS) from South Dakota School of Mines and Technology, studying from 2013 to 2017. He then pursued a Master of Science (MS) at the University of Pittsburgh from 2017 to 2019. He completed his Doctor of Philosophy (PhD) in Computer Engineering at the University of Pittsburgh, studying from 2017 to 2022.
Research and Technical Expertise
David L. possesses expertise in several technical areas. He specializes in deploying machine learning models in distributed computing environments and has skills in reconfigurable computing, which allows for system adaptation to new tasks. His strong background in resilient engineering methods is particularly relevant for industrial applications, enhancing the reliability and performance of systems.
Teaching and Tutoring Experience
David L. has experience in education, having worked as an Instructor’s Assistant and tutor at St. Thomas More Catholic School from 2012 to 2013. This role involved supporting students in their learning and assisting teachers in classroom management, providing him with valuable skills in communication and mentorship.