Jake Callahan
About Jake Callahan
Jake Callahan is an R&D Intern at Sandia National Laboratories with a background in data science and automation, holding a Master's degree in Mathematics from Brigham Young University.
Current Position at Sandia National Laboratories
Jake Callahan is currently serving as an R&D Intern at the Computer Science Research Institute within Sandia National Laboratories, a role he began in 2021. As part of his responsibilities, he is involved in significant research projects that cater to the institute's computing and data analysis needs. His work supports the broader mission of Sandia National Laboratories, advancing scientific research and development in various domains.
Previous Experience at Honeywell
In 2020, Jake Callahan worked as an Automation and Cognitive Services Intern at Honeywell. During his two-month tenure, he contributed to the development and optimization of automation processes, implementing cognitive technologies to enhance operational efficiency. This role provided him with valuable experience in industrial automation and the application of cognitive services in a professional setting.
Role at OrderBoard, Inc.
Jake Callahan held the position of Data Scientist at OrderBoard, Inc. from 2019 to 2021 in Orem, Utah. Over the course of two years, he was involved in data analysis, model development, and providing data-driven insights that supported the company's decision-making processes. His work contributed to enhancing the company's operational strategies and performance.
Educational Background
Jake Callahan obtained a Master of Science (MS) degree in Mathematics from Brigham Young University in 2022, following his one-year study from 2021 to 2022. Prior to this, he completed his Bachelor of Science (BS) degree in Mathematics: Applied and Computational Emphasis from the same institution, studying from 2016 to 2020. His academic background provided a strong foundation in mathematical theories and their applications in computational contexts.
Technical Skills and Expertise
Jake Callahan has extensive expertise in probabilistic modeling and is proficient in using a range of Python data science libraries including NumPy, SciPy, Scikit-learn, Pandas, Pytorch, and Keras. He has applied these skills to diverse projects such as developing an infant cry classification system using Long Short-Term Memory networks and conducting research on unsupervised image segmentation with Markov random fields. Additionally, he has experience optimizing control systems for solar panel cooling.