Quantitative psychologist driving social impact by connecting small businesses to communities using data insights. Highly skilled in machine learning, data visualization, experimental design, verbal and written communications.

Experience

• 2019-06 - present, Data Scientist, Facebook
  • Driving social impact by connecting 10 million + small businesses to communities, using data, programming, measurement, and machine learning to develop, influence, and optimize scaled marketing strategy.
  • Initiate and lead projects to use machine learning models, parametric, and non-parametric testings to optimze customer retention strategy.
• 2017-10 - 2019-04, Research Data Scientist, axialHealthcare
  • Lead projects to develop and operationalize end-to-end interpretable machine learning models to predict outcomes such as opioid overdose and relapse, using R, BigQuery, and Python. Improved both precision and sensitivity by more than 20%. One patent under filing.
  • Established standard operating procedures for R&D practices via R packaging.
  • Wrote papers, blog posts, and conference abstracts to promote company image.
• 2012-08 - 2017-08, Research and Teaching Assistant, Vanderbilt University
  • Modeled the mechanisms of individual differences in visual categorization, using JavaScript, CSS, Html, MySQL, R, JAGS, Python, and STAN.
  • Developed and evaluated the Vanderbilt Bird Expertise Test using the Item Response Theory, used JavaScript, CSS, Html, MySQL, R, and JAGS.
  • Authored 7 methodological and substantive papers, and 6 conference abstracts.
  • Served as teaching assistant in courses such as Statistical Analysis, Quantitative Methods, and Experimental Design.
  • Invited to lecture on topics such as psychometric modeling, cognitive modeling, programming web-based experiments, college teaching etc.
• 2010-08 - 2012-07, Research and Teaching Assistant, Peking University
  • Studied perception, specifically face adaptation (authored 1 paper) and perceptual learning, using Matlab and SPSS.
  • Teaching assistant in Signal Detection Theory.
  • Residential Assistant/Adviser in PKU-Yale Joint Program.

Education

• 2012-08 - 2018-05, Ph.D. of Psychology, Vanderbilt University
  • Major: Cognition and Cognitive Neuroscience
  • Minor: Graduate Quantitative Methods
  • Thesis: Understanding Real-World Visual Expertise in the Multidimensional Space
  • Adviser: Dr. Thomas J. Palmeri
• 2008-08 - 2012-07, B.S. of Psychology, Peking University
  • Thesis: Understanding the Neural Mechanisms of Motion Perceptual Learning
  • Adviser: Dr. Fang Fang

Publications

Face adaptation improves gender discrimination.

Yang, H., Shen, J., Chen, J., & Fang, F. (2011). Face adaptation improves gender discrimination. Vision Research, 51(1), 105-110. doi:10.1016/j.visres.2010.10.006


Studying real-world perceptual expertise.

Shen, J., Mack, M. L., & Palmeri, T. J., (2014). Studying real-world perceptual expertise. Frontiers in Psychology, 5, 857. doi:10.3389/fpsyg.2014.00857

The perception of a face can be greater than the sum of its parts.

Shen, J., & Palmeri, T. J., (2014). The perception of a face can be greater than the sum of its parts. Psychonomic Bulletin & Review. 1-7. doi:10.3758/s13423-014-0726-y


Modeling individual differences in visual categorization.

Shen, J., & Palmeri, T. J. (2016). Modeling individual differences in visual categorization. Visual Cognition, 24, 260-283. doi:10.1080/13506285.2016.1236053


Modeling the dynamics and representations of real-world visual expertise.

Shen, J. (2018). Modeling the dynamics and representations of real-world visual expertise. Retrieved from Vanderbilt University Electronic Theses and Dissertations.


Multilevel reliability measures of latent scores within an Item Response Theory framework.

Cho S.-J., Shen, J., & Naveiras, M. (2019). Multilevel reliability measures of latent scores within an Item Response Theory framework. Multivariate Behavioral Research.


Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model.

Cho S.-J., Brown-Schmidt S., De Boeck P., Shen J. (2020). Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model. Psychometrika, 85, 154–184. doi:10.1007/s11336-020-09694-6


Modeling the dynamics of real-world visual expertise.

Shen J. & Palmeri T. J. (manuscript). Modeling the dynamics of real-world visual expertise.

Awards

• 2016, Lisa M. Quesenberry Foundation Scholarship, Vanderbilt University
• 2016, IU Young Scientist Award from the Society for Mathematical Psychology
• 2015, The William F. Hodges Teaching Assistant Award, Vanderbilt University
• 2009, National Scholarship, Peking University
• 2008, Scholarship for Excellent Freshman by Peking University

Skills

• Data Collection and Analysis: R, Matlab, JavaScript, CSS, Html, SAS, Python, SQL
• Computational Modeling: JAGS, WinBUGS, STAN, Matlab
• Data Report: R Markdown, R ggplot2, Git, PowerPoint, Word, Illustrator

Certifications

• 2018-09, DataCamp – Python Programming Track
• 2018-01, DataCamp – Machine Learning with R Track
• 2017-05, Vanderbilt University – Graduate Quantitative Methods Minor
• 2017-03, SAS Institute – SAS Certified Base Programmer for SAS 9
• 2014-04, Vanderbilt University – Certificate in College Teaching

Service and leadership

• Volunteer marketer for various small businesses and non-profits. 2019-present.
• Co-organizer, Data Science Nashville Meetup, 2017-2020
• Co-organizer, Greater Nashville Healthcare Analytics Meetup, 2017-2020
• Tutor, Nashville Adult Literacy Council, 2017-2020
• Reviewer, Psychonomic Bulletin & Review, 2017
• Committee Member, Vanderbilt University Undergraduate Honors Committee, 2017
• Judge, Vanderbilt University Undergraduate Research Fair, 2016
• Expert TA Panelist, Vanderbilt University Teaching Assistant Orientation, 2014