Dr. Trenton Kriete is the Chief Data Scientist at RxRevu, a Denver based healthcare IT and analytics company. At RxRevu, Trent is charge of the statistical and predictive portion of RxRevu's Prescription Intelligence solution, as well as experimental design in order to validate the solution in clinical settings. With greater than 20 years of software development experience, 12+ years of advanced statistical and machine learning training, as well as 10+ years studying and publishing on the biological basis of human behavior, Dr. Kriete has developed a unique combination of complimentary skills. Prior to joining RxRevu he was a researcher in a top computational neuroscience laboratory utilizing cutting edge techniques, such as Deep Neural Networks and other machine learning techniques, to help us better understand the functioning of the human brain. Dr. Kriete received his B.S. in Computer Science in 1999 from the University of Evansville, where he was recognized with the Guthrie Mae Outstanding Senior award for his leadership and service to the University. Following a career in web development, he received his Master's in Computer Science from Vanderbilt in 2005 and his PhD in Cognitive Science from the University of California in 2010. Dr. Kriete has been published in prestigious journals such as the Proceedings of the National Academies of Sciences and presented his research at multiple international conferences. https://scholar.google.com/citations?user=HlMy1aYAAAAJ&hl=en

Chief Data Scientist @ Responsible for creation, testing, and maintenance of RxRevu Prescription Intelligence (predictive analytic) solutions. Responsibilities include: * Creation and deployment of machine learning algorithms geared towards improving cost, quality, and outcomes of patients. * Experimental design and assessment of predictive models in clinical settings * Management of data science focused tools and infrastructure From August 2014 to Present (1 year 5 months) Postdocoral Researcher, Computation Cognitive Neuroscience: O'Reilly Lab @ My research is focused on using formal computational models of cognitive processes in order to help us better understand the mechanisms that underlie our behavior. The world of neuroscience is physical in nature, consisting of biological entities such as neurons, axons, and neurotransmitters. On the other hand, the world of cognitive psychology is made of more abstract and informational items such as attention, biases, and representations. The interactions between these entities and the laws that govern them can be complex and often are difficult to understand when using purely analytical approaches. My research seeks to form a conceptual bridge joining the fields of cognitive psychology and neuroscience, providing a common language to discuss these related fields. Specifically, I utilize formal computational models of human level cognitive processes that are inspired as well as constrained by biological data and laws. This approach, known as computational cognitive neuroscience, is unique in that it requires a high level of biological fidelity while also reaching up to levels of analysis that include matching patterns of human behavioral data. By constraining the theoretical framework from both bottom-up and top-down levels of analysis, the tools of computational cognitive neuroscience can provide a lingua franca of sorts, allowing us to describe high level and abstract psychological constructs in terms of the underlying neural mechanisms. From August 2010 to August 2014 (4 years 1 month) Adjunct Professor @ Teaching multiple collegiate level courses including a core Statistics and Experimental Design course as well as an Introduction to Psychology course. My responsibilities include: * Preparing lecture materials to convey ideas in a clear and accessible manner to the students * Evaluation of students according to core curriculum guidelines set out by the Psychology department of the University of Colorado Denver. * Helping students outside of regular hours to ensure they continue to make progress towards the class goals, as well as their own. * Use of academic software to manage the class including Blackboard and Canvas From January 2012 to 2014 (2 years) Freelance Computer Consulting @ Since starting my research and academic career, I have maintained a business and skill set centered around web based technologies and solutions. This work has required expert level use of the entire LAMP stack (Linux, Apache, MySQL, and PERL) in order to provide custom and scalable solutions for my clients, as well as interfacing with multiple 3rd party components via various XML based APIs, JSON, and many other communication frameworks. Currently, I am actively pursuing combining my web software skills with my statistical and machine learning expertise in order to develop custom data driven applications. From 2003 to 2014 (11 years) PhD student @ For my dissertation, I used the tools of computational cognitive neuroscience to investigate a specific theoretical account. Namely, if many aspects of the behavior profile seen in people with autism could be explained in terms of a dysfunctional dopaminergic influence on the PFC / BG working memory system. Under my framework, the range of behaviors accounted for include explaining specific patterns of executive functioning, implicit learning deficits, development of overselective behavior, and differences in patterns of category learning in people with autism. From August 2006 to May 2010 (3 years 10 months) Perl Developer @ * Front end application development using Perl in a persistent process environment to provide a scalable high throughput application. * Technologies Used: PERL, Sybase, Apache, Redhat Linux, Javascript, XSLT, XML, FastCGI, MySQL From 2001 to 2003 (2 years) Software Engineer @ * Custom Application Development * Development using multiple environments, technologies, and programming languages. These included: * Windows and Linux * Perl, VBScript, and ASP * IIS & Apache * MSSQL & MySQL From 1999 to 2001 (2 years)

Doctor of Philosophy (Ph.D.), Cognitive Science @ University of California, Merced From 2005 to 2010 M.S., Computer Science @ Vanderbilt University From 2003 to 2007 B.S., Computer Science @ University of Evansville From 1994 to 1999 Trenton Kriete is skilled in: Machine Learning, Predictive Analytics, Statistics, Cognitive Science, Deep Learning, Neural Networks, Artificial Intelligence, Healthcare, Python, Experimental Design, Science, Perl, Neuroscience, Research Design, Data Analysis