Computational Social Scientist:
Grounded in social science research.

"Research interests include inequality, digital discourse, social stratification, and data-driven policy analysis. Key methods include statistical modeling, NLP, supervised learning, and longitudinal analysis."

RESEARCH INTERESTS: SOCIAL INEQUALITY • DIGITAL DISCOURSE ANALYSIS • STRATIFICATION RESEARCH • POLICY IMPACT ANALYSIS • LONGITUDINAL STUDIES • NETWORK ANALYSIS • TEXT AS DATA • SURVEY METHODOLOGY • COMPUTATIONAL SOCIAL SCIENCE •

Current Projects

Predictive Analytics

This project develops an end-to-end machine learning pipeline designed to predict real-time customer behavior and purchase intent using session-based interaction data. The workflow includes data cleaning, feature engineering, and the construction of predictive variables such as session duration, click patterns, page navigation sequences, and engagement intensity to model conversion likelihood. Multiple classification models are evaluated and tuned to optimize predictive performance, with a focus on interpretability and deployment readiness for real-time inference. The final pipeline supports scalable decision-making for personalized targeting and marketing optimization, contributing to a measurable 34% increase in conversion rate compared to baseline strategies.

Human & Machine social interaction

This work will examines algorithmic systems as contemporary instruments of governance that reproduce inequality while presenting themselves as politically neutral technologies. Drawing on Gramsci's cultural hegemony and Deleuze's control societies, it argues that algorithms do not simply automate decisions but actively construct ideological frameworks that legitimize racialized and neoliberal forms of exclusion. Focusing on immigration enforcement, border control, and welfare distribution, the analysis shows how predictive analytics, risk-scoring, and fraud detection systems disproportionately target marginalized groups while obscuring accountability through proprietary "black-box" infrastructures. Building on scholars such as Eubanks, Benjamin, and Couldry, the study frames algorithmic governance as a form of data colonialism that converts lived experience into statistical categories, masking systemic injustice under the rhetoric of efficiency and objectivity.

Technical Skills

Research Methods

Quantitative Data Analysis
Quantitative Data Analysis

Statistical analysis of numerical data using quantitative techniques including regression, hypothesis testing, and descriptive statistics.

 Qualitative Research
Qualitative Research

In-depth analysis of non-numerical data for contextual understanding using methods like interviews, focus groups, and content analysis.

 Thematic Analysis
Thematic Analysis

Identifying, analyzing, and reporting patterns (themes) within qualitative data to understand underlying meanings and experiences.

 Statistical Inference
Statistical Inference

Drawing conclusions about populations from sample data using probability theory and statistical methods.

 Hypothesis Testing
Hypothesis Testing

Formal procedures for validating research hypotheses using statistical evidence and p-values.

 Research Design
Research Design

Planning and structuring research studies to ensure validity, reliability, and ethical considerations.

 Measurement
Measurement

Developing reliable and valid measurement instruments for social science research.

 Survey Analysis
Survey Analysis

Analyzing survey data with appropriate statistical methods including weighting and missing data handling.

 Questionnaire Design
Questionnaire Design

Creating effective survey instruments for data collection with attention to validity and response quality.

Data Science & ML

Data Cleaning
Data Cleaning

Preprocessing and preparing data for analysis by handling missing values, outliers, and inconsistencies.

 Preprocessing
Preprocessing

Transforming raw data into analyzable format through normalization, encoding, and feature scaling.

 Exploratory Data Analysis
Exploratory Data Analysis

Initial investigation of data to discover patterns, spot anomalies, and test assumptions.

 Regression Analysis
Regression Analysis

Modeling relationships between variables to understand how independent variables affect dependent variables.

 Supervised Learning
Supervised Learning

Training models on labeled data to make predictions or classifications.

 Unsupervised Learning
Unsupervised Learning

Finding patterns and structure in unlabeled data through clustering and dimensionality reduction.

 Clustering
Clustering

Grouping similar data points together based on their characteristics.

 Natural Language Processing
Natural Language Processing

Computational analysis of human language for tasks like sentiment analysis and topic modeling.

 Feature Engineering
Feature Engineering

Creating informative features for ML models to improve predictive performance.

 Model Evaluation
Model Evaluation

Assessing model performance and validity using metrics like accuracy, precision, and recall.

 Imbalanced Data
Imbalanced Data

Handling datasets with uneven class distributions using techniques like SMOTE or weighted loss functions.

Technical Tools

Python
Python

Primary programming language for data analysis, machine learning, and statistical modeling.

 R
R

Statistical computing and graphics environment for data analysis and visualization.

 SQL
SQL

Database querying and management for extracting and manipulating structured data.

 SPSS
SPSS

Statistical analysis software for social science research with comprehensive analytical capabilities.

 Stata
Stata

Statistical software for data science with strong econometric and social science applications.

 Jupyter Notebooks
Jupyter Notebooks

Interactive computational environment for creating and sharing documents with live code.

 RStudio
RStudio

Integrated development environment for R with tools for visualization and reporting.

 Git
Git

Version control system for tracking changes in code and collaborative development.

 Version Control
Version Control

Managing changes to code and documents over time for reproducibility and collaboration.

 Excel
Excel

Spreadsheet analysis and visualization tool for data manipulation and reporting.

Data Types & Sources

Text Data
Text Data

Analysis of written content and documents using NLP techniques and qualitative methods.

 Social Media Data
Social Media Data

Data from social platforms for social research including Twitter, Facebook, and Reddit.

 Census Data
Census Data

Official population and demographic data for regional and national analysis.

 Administrative Data
Administrative Data

Official records and administrative sources for policy evaluation and social research.

 Survey Data
Survey Data

Systematically collected questionnaire data for social science and market research.

 Longitudinal Data
Longitudinal Data

Data collected over time from same subjects for studying change and development.

 Panel Data
Panel Data

Multi-dimensional data with time component for analyzing complex relationships.

 Geospatial Data
Geospatial Data

Location-based data with geographic information for spatial analysis and mapping.

 Public Policy Data
Public Policy Data

Datasets relevant to policy analysis including government reports and impact evaluations.

Research Competencies

Literature Review
Literature Review

Systematic examination of existing research to establish context and identify gaps.

 Synthesis
Synthesis

Integrating findings from multiple sources to develop comprehensive understanding.

 Reproducible Workflows
Reproducible Workflows

Creating replicable research processes using version control and documentation.

 Documentation
Documentation

Thorough recording of research processes, decisions, and methodologies.

 Reporting
Reporting

Communicating research findings effectively through written reports and presentations.

 Ethical Data Use
Ethical Data Use

Adhering to ethical guidelines in research including informed consent and data protection.

 Privacy Awareness
Privacy Awareness

Protecting sensitive data and identities through anonymization and secure handling.

 Interdisciplinary Collaboration
Interdisciplinary Collaboration

Working across academic disciplines to address complex social questions.

 Knowledge Translation
Knowledge Translation

Converting research findings into practical insights for policymakers and practitioners.

Advanced Methods

Causal Inference
Causal Inference

Identifying cause-effect relationships using methods like RCTs, matching, and instrumental variables.

 Multilevel Modeling
Multilevel Modeling

Analyzing nested data structures like students within schools or patients within hospitals.

 Time Series Analysis
Time Series Analysis

Analyzing data points ordered in time to identify trends, seasonality, and patterns.

 Text Mining
Text Mining

Extracting information and patterns from text data using computational methods.

 Sentiment Analysis
Sentiment Analysis

Determining emotional tone in text to understand opinions, attitudes, and emotions.

 Topic Modeling
Topic Modeling

Discovering abstract topics in text collections using algorithms like LDA.

 Network Analysis
Network Analysis

Studying relationships between entities through network theory and graph algorithms.

 Spatial Analysis
Spatial Analysis

Analyzing geographic patterns and relationships using GIS and spatial statistics.

 Bayesian Methods
Bayesian Methods

Statistical inference using Bayes' theorem to update probabilities based on new evidence.

Get in Touch

Whether you're interested in research collaboration, methodological consultation, or professional opportunities, feel free to reach out. I welcome conversations at the intersection of social science, statistics, and applied data science.

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