Data scientist

KE SANG

*** ********** ****. ****** ****, NJ ****0 812-***-**** *************@*****.***

EDUCATION

Indiana University, Bloomington, IN (Fall 2009 – Present)

Ph.D Candidate, Cognitive Science. GPA: 3.97

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Graduate Coursework: Intro to Bayesian Data Analysis; Multi-agent Modeling of Social Behavior; Intro to Dynamical Systems in Cognitive Science;

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Models in Cognitive Science; Math and Logic for Cognitive Science; Programming Methods in Cognitive Science; Python

Coursera: Machine Learning; Intro to Data Science; Practical Machine Learning; Developing Data Products; Exploratory Data Analysis; etc.

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East China Normal University, Shanghai, China (Fall 2006 – Spring 2009)

M.S., Experimental Psychology. GPA: 3.83

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Fudan University, Shanghai, China (Fall 2001 – Spring 2006)

B.M., Clinical Medicine. GPA: 3.32

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PROJECT

Building a Predictive Text Model for SwiftKey (10/2014 – Present)

Clean the data from Twitter and blogs with R and natural language processing techniques.

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Build predictive models such as the Markov Chain model and n-gram language models to forecast succeeding words in the text context.

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Music Consumption Patterns Online and Individual Differences (08/2014 – Present)

• Use Python to crawl data from the EchoNest API.

• Perform exploratory data analysis on listeners’ music consumption patterns with Pandas, scikit-learn, Tableau, and SQL.

• Cloud computing a 500 gigabytes dataset on music consumption patterns and individual differences in the MapReduce framework.

Predicting Human Gestures from Body Sensor Data (06/2014 – 07/2014)

• Use R and caret to do data processing, PCA and feature selection.

• Build and compare various predictive models including SVM, logistic regression, and random forest, etc.

Implementing a Movie Recommendation System (07/2013 – 08/2013)

• Implement the collaborative filtering learning algorithm in Matlab.

• Add regularized gradient function and cost function to the algorithm.

Human Search Behavior and Individual Differences (09/2012 – 04/2014)

• Program the experimental video game with Python.

• Analyze and model data from bipolar and schizophrenia populations with R and Chaos theory, etc.

Game Theory and Social Competition: a Colonel Blotto Simulation (05/2012 – 01/2014)

• Simulate game outcomes under various game settings with Monte Carlo method.

• Analyze different heuristic decision strategies for the weaker player in the Colonel Blotto game in Matlab.

Cognitive Modeling Human Search Behavior (02/2010 – 04/2012)

• Use dynamic programming and algorithms to find optimal mathematical solutions for one searching experimental paradigm in Python.

• Perform statistical and cognitive modeling to make classification predictions on behavioral data.

DATA SCIENCE SKILLS

MapReduce (1 year) Machine Learning (3 years) Data Mining (5 years) Algorithm (5 years) R (5 years)

Statistical Modeling (5 years) Bayesian Statistics (5years) Java (3 years) Tableau (0.5 year) SQL (1 year)

Traditional Statistics (5 years) Python (3 years) NetLogo (5 years) Matlab (5 years)

WORKING EXPERIENCE

Research Fellow at Indiana University (08/2014 – Present): Using Python to program a video game software, cognitive modeling human

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behavioral data, and research reports and papers writing.

Teaching Assistant at Indiana University (09/2010 – 05/2014): Teaching lectures, grading homework and holding office hours for

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undergraduate courses such as Cognitive Psychology and Statistics, and graduate courses including Bayesian Statistics and Math and Logic for

Cognitive Science.

Resident at Shanghai Huadong Hospital (07/2005 – 06/2006): Initial and ongoing assessment of patient's medical status, develop

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assessment and treatment plans under supervisors. Order tests, examinations, medications, and therapies. Perform procedures, and assist in surgery.

PUBLICATION

Sang, K., Todd, P. M., Goldstone, R. L., & Hills, T. T. (2013). Learning near-optimal search rules in a minimal explore/exploit task. Cognitive

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Science.(revise and resubmit).

Sang, K., Dai, J. Y., Todd, P. M., & Goldstone, R. L. (2013). Temporal discounting in a sequential search task. Proceedings of the Thirty-Fifth Annual

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Conference of the Cognitive Science Society. Berlin, Germany: Cognitive Science Society.

Sang, K., Todd, P., Rass, O., Bolbecker, A., Howell, J., Hetrick, W., & O'Donnell, B. (2012) Cognitive Search Behavior in Schizophrenia and Bipolar

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Disorder. Twenty Sixth Annual Meeting of the Society for Research in Psychopathology. Ann Arbor, Michigan: The Society for Research in

Psychopathology.

Sang, K., Todd, P. M., & Goldstone, R. L. (2011). Learning near-optimal search in a minimal explore/exploit task. Proceedings of the Thirty-Third

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Annual Conference of the Cognitive Science Society. (pp. 2800-2805). Boston, Massachusetts: Cognitive Science Society.

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