Michael K.
About Michael K.
Michael K. is a data scientist specializing in machine learning, currently working at Cognitive Space in Portland, Oregon. He has previously held positions at notable companies such as Nike, Coinbase, and Launchpad.AI, and has contributed to various projects in the field of machine learning and data science.
Current Role at Cognitive Space
Michael K. currently serves as a Data Scientist specializing in Machine Learning at Cognitive Space. He has been in this position since 2023, contributing to projects that leverage machine learning techniques to enhance data analysis and decision-making processes. His role involves developing algorithms and models that support the company's objectives in the field of space technology.
Previous Experience at Nike
Michael K. worked at Nike in various capacities, including Senior Search Data Scientist and Senior Full Stack Data Scientist. His tenure at Nike lasted from 2019 to 2021, during which he focused on data-driven solutions for e-commerce search and user experience optimization. His work included the application of natural language processing techniques to improve search functionalities.
Educational Background
Michael K. holds a Master of Science in Geographic Information Science and Cartography from the University of Arizona, where he studied from 2021 to 2023. He also earned a Bachelor's Degree in Astrophysics from the University of Colorado at Boulder, completing his studies from 2009 to 2013. His academic background provides a strong foundation for his work in data science and machine learning.
Contributions to Machine Learning Community
Michael K. actively engages in mentorship, blogging, and teaching to foster growth within the machine learning community. He has developed several libraries, including a deep contextual bandits library called space-bandits and a crater detection library named pycda. His efforts aim to share knowledge and tools that advance the field of machine learning.
Technical Expertise and Projects
Michael K. has expertise in e-commerce search, particularly in natural language processing. He has worked on decision-making systems and recommendation engines, and conducted ablation studies focused on fairness in facial recognition systems. Additionally, he developed the Sparse Evolutionary Training (SET) implementation in PyTorch, known as synapses, showcasing his technical skills in machine learning applications.