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Serena Kim, PhD

Biography

I'm Serena, an assistant professor in the School of Public and International Affairs at NC State University. I moved to North Carolina in Fall 2023. From 2019 to 2023, I was a research associate in the College of Engineering, Design, and Computing and a scholar in residence in the School of Public Affairs at the University of Colorado Denver. During this time, I taught graduate courses on evidence-based decision-making, public policy analysis, and research and analytic methods.

My primary research interest lies in the social science applications of full-stack data analytics, which integrates understanding of social problems, creating and managing databases, deploying models based on statistics and machine learning algorithms, developing systems and software for continuous and reliable data generation and processing, and communicating data to promote data-informed decision-making. Topics of interest include neural networks for natural language processing and computer vision, data mining, geospatial data science, network analysis using big data, machine learning algorithms for prediction, applied software development, data visualization, and data-centric AI for systematic data engineering in social science research.

I have domain expertise in US energy policy and the energy transition such as the adoption of renewable and distributed energy resources. But I've also collaborated with researchers who focus on public health and environmental sustainability, and I apply full-stack data analytics in these domains. My work has appeared in peer-reviewed journals, including Energy Policy, Policy Studies Journal and SSM - Population Health among others, and my research has been featured in media outlets such as The New York Times and Washington Post. I also have a strong foundation in institutional analysis, and my research is often informed by transaction cost economics and collective action frameworks.

When I'm not at work, I enjoy hiking, dancing, practicing yoga, playing the piano, trying new recipes, reading books, watching movies, and attending performing arts shows and college and professional basketball games with my husband, William Swann, our family and friends.

Resume

Summary

Serena Kim

Computational social scientist with domain expertise in energy policy, renewable energy adoption, public perception of renewable energy, institutional analysis, and collective action

  • 2221 Hillsborough St, Raleigh, NC 27607
  • serena_kim@ncsu.edu

Education

PhD in Public Administration

Askew School of Public Administration and Policy, Florida State University, Tallahassee, FL

Dissertation: Essays on U.S. Renewable Energy and Sustainability Policy

MS in Computer Science

University of Colorado Boulder, Boulder, CO

Courses Taken: Natural Language Processing, Software Engineering, Data Science Teams, Network Analysis and Modeling, Geographic Information Systems, Data Mining, Machine Learning, Entrepreneurial Projects, Computer Graphics, Neural Networks and Deep Learning, Big Data Analytics: Systems, Algorithms, and Applications

BA in Public Administration & Economics (Dual Major)

Yonsei University, Seoul, South Korea

Professional Experience

Assistant Professor

August 2023 -

School of Public and International Affairs, NC State University

  • Teach and research social science research methods

Senior Instructor & Scholar in Residence

2019 - July 2023

School of Public Affairs, CU Denver

  • Taught 27 sections of four graduate courses
  • Public policy analysis concentration director

Postdoctoral Research Associate

2021 - July 2023

College of Engineering, Design, and Computing, CU Denver

  • Research on the energy transition to renewables and vehicle electrification

Senior Researcher

2020 - Present

The Schreiber Research Group

  • Geospatial, text, and network data analysis and visualization

Data Analysis Skills & Tech Stack

I love coding and programming for scientific discovery and research communication. I have developed my statistical and computational skills over 10+ years of formal training in computer science, economics, and public policy and administration, and I am motivated and excited to learn new tools and important developments in computer science and statistics. Below are selected skills and tools that I use regularly. Although I have used other programming languages, including C/C++, Go, JavaScript, Ruby, Kotlin, R, and MATLAB for various projects, Python has been my main working language. Progress bars below indicate how far along I am in the process of developing each skill as of August 2024.

Text Classification - Topic Categorization, Sentiment Analysis, Spam Detection95%
Named Entity Recognition (NER) - spaCy, BERT90%
Large Language Models - Llama, Titan (AWS Bedrock)65%
Topic Modeling - Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF)80%
Supervised Learning - Regression, Decision Trees, and SVM90%
Unsupervised Learning - K-Means, Hierarchical Clustering, Dimensionality Reduction85%
Computer Vision - Image and Video Recognition, Object Detection80%
Ensemble Learning - Bagging, Boosting, Stacking95%
Hyperparameter Optimization - Search, Bayesian Optimization90%
Interpretable/Explanable AI85%
Deployment and Monitoring - Docker, Cloud Services80%
Data Lake Management - AWS S3, Azure Data Lake60%
Batch Processing - Apache Hadoop, Apache Spark50%
Cloud Data Warehousing - GCP, AWS RDS, AWS Redshift80%
Data Mining, Webscraping (Using Python)95%
Structural Data Manipulation in Various Objects (JSON, Graphs, Arrays, Tables, Tuples)100%
Continuous Integration/Continuous Deployment (CI/CD)80%
DevOps Practices - Docker, Kubernetes50%
Backend Development (Python) - Django, Flask75%
Back-End Development (JavaScript) - React, NodeJS75%
Front-End Development - HTML/CSS/JavaScript 85%
Database Design and Management - PostgresSQL, MongoDB, AWS RDS/S380%
Adobe Suite - Photoshop, Illustrator, InDesign75%
3D Graphics - OpenGL (C/C++)65%
Geospatial Data Management - Vector Objects90%
Geospatial Data Management - Raster Objects70%
Geospatial Visualization - LeafletJS, Folium, Mapbox GL JS, etc.95%
ArcGIS Suite - ArcMap, ArcGIS Online, ArcGIS Pro85%
Geospatial Regression - PySAL80%
Social Network Analysis - NetworkX and graph-tool85%
Algorithmic Network Analysis - Epidemic modeling, Graph Traversal, Network Sampling60%
Graph Neural Networks and Graph Convolutional Networks60%
Python - Regression and Time Series Models95%
R - Regression and Time Series Models, Synthetic Control80%
Stata - Regression and Time Series Models, Survey Analysis95%
Interview Data Collection and Analysis85%
Survey Data Collection and Analysis90%

CV