Who Am I?

Hi I'm Richard Lu. I am a data scientist trained in industrial engineering (BS) and the social sciences (PhD) interested in data of all kinds. I specialize in using sophisticated computational techniques to derive business insights from Big Data.


My Training

Haas School of Business,
University of California, Berkeley

Dissertation (In Progress) - Surveying Personality with Behavior: The Situational Influences and Individual Outcomes of Self-Monitoring Behavior

Here is a one-page, high-level summary of my dissertation.

  • Sameer Srivastava, Chair
  • David Bamman
  • Cameron Anderson

H. Milton Stewart School of Industrial and Systems Engineering,
Georgia Institute of Technology

GPA: 4.00


My Specialties

Because learning rarely follows a linear trend, I have included here my self-reported proficiencies for my skills and languages. For the languages, I additionally include a rough estimate of the number of hours I have spent in each.


Data Visualization

Dimensionality Reduction

Machine Learning

Natural Language Processing

Object Oriented Programming

Relational Databases

Statistics & Econometrics

Web Scraping



4800 hours


1000 hours


290 hours

Web Scripting (HTML, CSS, JavaScript, PHP)

145 hours


75 hours


40 hours

Selected Data Experience

Imputing Cultural Fit

  • Developed a generalizable methodology for extending cross-sectional surveys to longitudinal data using a random forest model
  • Leveraged natural language processing tools and principal components analysis to extract features from the raw content of over five million emails
  • Overcame challenges in the machine learning pipeline such as small N, class imbalance, and model validation by transforming classification probabilities to a weighted mean measure, bootstrapping unbalanced classes, and designing complementary evaluation metrics, respectively

Worked with Jennifer Chatman (UC Berkeley), Amir Goldberg (Stanford), and Sameer Srivastava (UC Berkeley) to develop a research paper.

Assessing Career Progression

  • Cleaned and extended a personnel dataset of more than three million person-month observations by creating variables such as organizational hierarchy based on direct reports and move atypicality based on all realized job title transitions
  • Analyzed differential effects of move atypicality by gender on career outcomes (pay and performance) using statistical methods such as matching on observables, piecewise exponential hazard rate models, and linear regression

Worked with Ming Leung (UC Riverside) and Sharon Koppman (UC Irvine) to develop a research paper.

Visualizing Responsibility

  • Extended a transfer learning convolutional neural network model based on Google's Inception-v3 computer vision architecture to evaluate the perceived responsibility of a profile picture by training on unique survey data
  • Integrated recent research on model interpretation in the form of class activation mapping to produce heatmaps of elements that most contributed to the responsibility ratings, opening the black box of deep learning models
  • Performed multivariate linear regression analysis to identify the impact of perceived responsibility in a low-wage, technology-mediated labor market

Worked with Ming Leung (UC Riverside), Sibo Lu (UpWork), and Michael Fermanian to write a grant proposal for which we received $8,000 from the Fisher Center for Business Analytics.

Improving Flow Time

  • Worked with a team of seven other individuals to improve the flow time of inventory through a 235,000 square foot distribution center
  • Developed a simulation model and a set of decision support tools, including a layout optimization, to estimate an overall improvement of 325% on the flow time of inventory

Delivered a technical report to the organization.


Fellowships and Honors for my Work

Sasakawa Young Leaders Research Fellowship

Fisher Center for Business Analytics Research Grant

NSF Graduate Research Fellowship Honorable Mention

IWSM Student Scholarship Award

Industrial Engineering Outstanding Researcher Award


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