Systems Scientist, Language Technologies Institute, Carnegie Mellon University
Dr. Ravi Starzl is an expert in the computational analysis and modeling of complex information-driven systems, with experience in such diverse domains as biological systems, financial systems, and Internet topologies. His core competencies include software engineering, machine learning, mathematical modeling, big data systems, and biology.
Dr. Starzl has extensive experience with the computational and mathematical methods integral to the effective acquisition, management, and utilization of extremely large amounts of information. Having led several massive data analysis projects with data sets as large as 100+TB, Dr. Starzl is fluent in the methods of Big Data analytics, and is an active researcher in the area of parallelization of machine learning methods for Big Data. He also has extensive experience with many biotechnology and industrial technology quantification platforms, immunological methods, and small animal transplant models, as well as general clinical practices, healthcare management systems, and entrepreneurial biotechnology development. Dr. Starzl conducts leading edge research into the elucidation of patterns of communication and function in the immune system by adapting and extending analytic techniques that have proven successful in areas with similar types of complexity, such as human language and finance. Dr. Starzl is further extending his work into areas that are populated with similar types of problems that can be addressed by his analytic methodology.
Dr. Starzl’s investigational methodology is to pursue research objectives that deliver findings of practical significance as well as findings that advance fundamental understanding, via an aggressive multi-disciplinary systems approach that seeks to elucidate and clarify complex system behavior through mathematics, computer science, domain knowledge, and empirical experience.
By integrating numeric analysis with hands-on experiments, he is able to establish an iterative process where both analytic findings and empirical observations can quickly infuse each other with meaning, and which can help guide the direction of investigation. This process accelerates the identification and elucidation of key mechanisms, as well as enabling more effective model bootstrapping by using existing knowledge of the behavioral, structural, and mathematical properties of a system. Ultimately, this allows the rapid development or validation of new and relevant strategies, processes, or products.
Dr. Starzl is a Systems Scientist in the Language Technologies Institute at Carnegie Mellon University. He received his doctoral degree in Language and Information Technologies from the School of Computer Science at Carnegie Mellon University in 2012. At In addition to his research at CMU, Dr. Starzl develops and teaches classes on the topics of Big Data, biotechnology, and advanced software development. Prior to his academic work, Dr. Starzl held several senior positions in private concerns such as University of Pittsburgh Medical Center and United Therapeutics Corporation. He has also participated in the founding, growth, and sale of several biotechnology and high-tech startups.