Paul M.

Paul M. has a diverse work experience spanning various industries and roles. Paul started their career as a System Administrator at Wikimedia Foundation, where they led teams in designing and implementing security policies for Wikipedia. Paul then worked as a Graduate Teaching Fellow at UC San Diego, teaching undergraduate classes in statistics, econometrics, and game theory.

Moving on, Paul worked as an XSEDE Fellow at the San Diego Supercomputer Center, conducting research on multiple imputation and machine learning using large datasets of geolocated social media data. Paul later joined Emory University as a Research Fellow and Faculty Member, leading a research lab focused on leveraging machine learning and predictive models for optimizing models of human behavior.

Paul then transitioned to the University of Michigan, where they served as a Research Associate at the Institute for Social Research. Here, they led a research lab in developing new machine learning algorithms and published a new algorithm that improves geospatial clustering.

In the industry, Paul has held several positions at prominent companies. Paul served as the Director of Data Science and Artificial Intelligence at Epsilon, a global data-driven marketing company. Paul also worked at Ericsson in various leadership roles, including AI Research Leader, Principal Data Scientist, and Director of Data Science in different divisions of the company. At Ericsson, they managed teams of researchers, developers, and engineers, leading them to develop new machine learning platforms and proprietary software.

Paul then joined Rivian as a Senior Engineering and Machine Learning Manager, where they led the data function in the product development organization, focusing on batteries, charging, reliability, and performance.

In 2023, Paul took on the role of VP of Engineering at Confiant Inc, overseeing their globally-distributed engineering organization and being responsible for product strategy, roadmap planning, and resource management.

More recently, Paul joined Red Hat as a part of the AI/ML Software Review Committee, where they are a technical leader in the development of an open-source machine learning data and model federation environment for enterprises. Their responsibilities include code review and technical integration.

Throughout their career, Paul has demonstrated expertise in machine learning, data science, and leading cross-functional teams. Paul has published research papers, mentored others, and secured significant funding through grant applications.

Paul M. has a strong educational background in the field of computational social science. Paul obtained their Doctor of Philosophy (PhD) degree from UC San Diego in 2018. Prior to that, they pursued a Master's degree in Mathematics and Game Theory from the University of Maryland and another Master's degree from The George Washington University. Paul also holds a Bachelor's degree from Columbia University.

In addition to their academic degrees, Paul M. has obtained several certifications. Paul completed the "Empirical Implications of Theoretical Models" certification from Duke University in July 2013. Paul also received certifications in "Human Subjects Research" from both the University of California San Diego and Harvard University, although specific details regarding when these certifications were obtained are unavailable.