The eight-year-old version of me would be thrilled to learn that I'm following the Peter Venkman career path: I spent time in the basement of the psychology department delivering electric shocks to undergrads, and now I'm moving to the private sector in New York ("where they expect results"​). I love doing scientific research, but unlike many of my colleagues, planning new experiments and proposing new lines of research have never been my favorite part of the job. Instead, I didn't get excited until I was staring at a great big pile o'​ numbers. Many of my colleagues saw the 'analysis'​ stage of research as drudgery, but I saw it as an opportunity to exert my creativity and find new ways to explore and interpret data. Once I realized that I was more interested in data analysis than neuroscience, I decided to change paths and become a data scientist. I have over 10 years of experience and more than a dozen publications using multivariate statistics and machine learning to understand high-dimensional neuroscience datasets -- now I'm looking for a career that will let me use these skills in a new setting.

Data Science Fellow @ The Insight Fellowship is a program partnered with Y-Combinator to helps STEM Ph.D.s translate the skills they developed in academic research to careers in data science. While at Insight, I developed an app to predict which topics a congressional representative will write about based on the source of their campaign funds. The project combined Natural Language Processing of every bill/resolution in the house from 2010-2015 with an analysis of every campaign contribution made over the same time period. Front-end development for the app was done with Flask and deployed on AWS. From September 2015 to Present (4 months) Cognitive Neuroscience Postdoc @ Worked with a team of engineers to explore communication networks in the brain using functional MRI and advanced mathematical methods, including graph-theoretic and Bayesian methods for assessing ‘information flow’ and community structure. Developed a new line of research to explore the neural and behavioral interaction between stress/anxiety and reward processing that will be funded for five years by the National Institute of Mental Health. Mentored undergraduate and graduate students on all aspects of the research process, including: hypothesis generation & experimental design, data collection, statistical analysis & interpretation, and scientific communication. From 2012 to September 2015 (3 years) Pre-Doctoral Research Trainee @ Awarded several traineeship grants (2008-2011) and fellowships (2011-2012) during graduate school to allow greater focus on independent research. These research projects have lead to multiple peer-reviewed publications, including one that was awarded “2013 Article of the Year” by Cognitive, Affective and Behavioral Neuroscience. Analyzed neuroimaging data with machine learning algorithms (e.g., support vector machines, linear discriminant analysis) to identify how neural representations differ across visual cortex. Served as a Teaching Assistant for mathematically oriented undergraduate and graduate courses in the psychology department, including: Mathematical Models of Human Behavior; Introduction to Learning and Behavior; Cognition, Computation & Brain. From 2008 to 2012 (4 years) Associate Research Specialist @ Collected and analyzed electroencephalographic (EEG) and physiological data, providing a hands-on introduction to time-frequency analysis, filter-design, and digital signal processing. Developed two large MATLAB software packages for in-house use: 1) a suite of tools for non- parametric (permutation-based) statistical analyses, and 2) a computationally efficient implementation of EEG source-localization algorithms for batch processing large datasets. Wrote several methods papers on the benefits of incorporating multivariate methods, such as factor analysis and independent components analysis (ICA), into the traditional EEG processing. From 2002 to 2005 (3 years)

Doctor of Philosophy (Ph.D.), Psychology; Cognitive Science @ University of Minnesota-Twin Cities From 2007 to 2012 Bachelor’s Degree, Mathematics; Psychology @ University of Wisconsin-Madison From 2003 to 2005 Brenton McMenamin is skilled in: Python, Matlab, Machine Learning, Multivariate Statistics, Bayesian statistics, Graph Theory, Unsupervised Learning, Factor Analysis, Data Visualization, Natural Language Processing, Scientific Writing, Java, R, SQL, SPSS, Bash, Linux