Computational Social Scientist — Copenhagen, DK
Jakob Bell
I build agents from data.
MSc student in Social Data Science at the University of Copenhagen. Open to research collaborations and student roles.
01 / About
About
I'm Jakob Bell, a computational social scientist based in Copenhagen. I'm a master's student in Social Data Science at the University of Copenhagen, where I work at the intersection of people and machines — building AI agents, language pipelines, and models that turn messy social and textual data into something legible.
My path into data science ran through the social sciences. I hold a BA in Political Science with a concentration in Public Law from UC San Diego, and two Associate of Arts degrees — one in Political Science and one in Liberal Arts. That mixed-methods background is the point, not a detour: I'm as comfortable close-reading a source as I am fine-tuning a classifier, and I try to keep both honest against each other.
Education
- 2025 – 2027
MSc, Social Data Science
University of Copenhagen
- 2022 – 2024
BA, Political Science
UC San Diego — Concentration: Public Law
AA, Political Science
AA, Liberal Arts
02 / Focus
What I do
What is a computational social scientist?
Computational social science studies human and social behavior at scale using the tools of data science — large text and behavioral datasets, machine learning, network analysis, and simulation — without letting go of the theory and interpretation that make the findings mean something. It sits between the qualitative and the quantitative: not just what the numbers say, but why, and for whom. My work lives in that seam — pairing computational methods with a close read of the source material.
A
AI Agents & Automation
Designing and deploying AI agents and automated workflows — custom agents, local LLMs, document-processing pipelines, and no/low-code orchestration (n8n, Make) — and studying how these systems actually interact with the people who use them.
B
NLP & NLU
A special focus on Natural Language Processing and Natural Language Understanding: topic modeling, transformer embeddings, fine-tuned classifiers, and LLM-assisted annotation to surface structure and meaning in large bodies of text.
C
Human-Centered, Mixed-Methods AI
Bringing social-science rigor to AI: mixed-methods design, data quality from collection onward, interpretable models (e.g. SHAP), and clear visualization — keeping systems accountable to the humans and communities they describe.
Selected tools & skills
- Python
- NLP & NLU
- Machine Learning
- Transformers / RoBERTa / BERTopic
- XGBoost
- SHAP
- n8n & Make
- AI Agents / LLMs
- Data Visualization
- EDA & Data Cleaning
- GeoPandas
- Polars
- Git & GitHub
- API Integration
- GDPR Compliance
- LaTeX
- Jupyter
03 / Selected Work
Selected work
01News Trend Analysis
Did social media set the news agenda? Testing whether immigration coverage reacted to Twitter spikes.
2025 · Group project
Python · GDELT API
02S&P 500 Insider-Trading Analysis
Building a dataset of every S&P 500 company from Yahoo Finance + SEC EDGAR, then regressing what predicts stock outcomes.
2025 · Final exam project
Python · yfinance
03Nepalese Student Migration Analysis
What the rise of Nepal's educational-consultancy industry reveals about outbound student migration.
2025–26 · Course project
Python · BeautifulSoup
04Hearing the Lonely Hearts
A mixed-methods study of how loneliness is voiced and received on r/lonely — across 65k+ Reddit posts.
2026 · Group project
Python · Polars
05Danish Fishing AIS
Mapping where fishing happens in Danish waters — and flagging trawling inside protected marine zones. Built in 48 hours.
2026 · Team leader
Python · Polars
More on GitHub ↗
04 / Photos
Off the clock
