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Cognitive Scientist
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I am interested in the intersection of memory, learning, and categorization, with a goal of developing models that elucidate why people are good at these things, and that inspire more generalizable learning algorithms in AI. Specialties: Bayesian and frequentist statistics, computational modeling, semantic space models, text mining, data wrangling, designing experiments, programming (R, Python, Java, C++, Matlab), writing papers, Ultimate frisbee
Indiana University Bloomington
Ph.D., Cognitive Science, Cognitive Psychology
January 1, 2007 – January 1, 2012
Carleton College
Bachelor of Arts (B.A.), Computer Science and Cognitive Studies
January 1, 2003 – January 1, 2007
North Carolina School of Science and Mathematics
High School, with Honors
January 1, 2001 – January 1, 2003
Skillprint
Senior Research Scientist
June 1, 2022 – Present
Stanford University
Research Scientist
September 1, 2018 – Present
Palo Alto, CA
Radboud University
Assistant Professor of Artificial Intelligence
September 1, 2016 – August 1, 2018
Nijmegen, Netherlands
New York University
Postdoctoral Researcher
January 1, 2015 – August 1, 2016
New York City Metropolitan Area
Leiden University
Postdoctoral Researcher
December 1, 2012 – December 1, 2014
Leiden, the Netherlands
Indiana University Bloomington
Research Assistant/Ph.D. Student
July 1, 2007 – December 1, 2012
Bloomington, IN
Mechanisms for Cross-Situational Learning of Word-Referent Mappings: Empirical and Modeling Evidence.
August 1, 2007 – December 1, 2012
In my Ph.D. thesis I conducted experiments to explore what factors (e.g., word frequency, active learning, and context diversity) affect language learning, and I then modeled human learners with several different computational models to elucidate the underlying cognitive processes. Overall, I found that human word learning is best accounted for by competing attentional biases for familiar word-object pairs and for uncertain stimuli. The factors and algorithms I tested have applications in education and machine translation.
Cultural Fit Analysis
The candidate has a strong academic and research-oriented background, with roles primarily in universities and research institutions. While this demonstrates intellectual curiosity and a drive for discovery, the transition to a corporate Data Analyst role might require adaptation to different work rhythms, project delivery methodologies, and business-driven objectives. The project diversity is limited to academic research, and direct alignment with typical industry data analysis challenges is not evident. The breadth of skills listed is primarily academic and theoretical, rather than practical industry tools.
Soft Skills & Operational Fit
The candidate's extensive research background suggests strong analytical thinking, problem-solving, and independent work capabilities. Experience as an Assistant Professor indicates presentation and mentorship skills. However, specific operational fit for a typical corporate Data Analyst role, including experience with standard business intelligence tools, large-scale data processing, or stakeholder communication in a non-academic setting, is not explicitly detailed.