I'm an empirical researcher with a Bachelor's degree in Psychology, focused on Artificial Intelligence and blending behavioural insight with technical curiosity to shape human-centred AI work.
Portfolio entries show work and experiments that apply psychological concepts to AI problems, user-facing designs, and data-driven studies from my senior-year focus.
An empirical data analysis project utilizing Generative AI (Gemini) and Python to parse, clean, and analyze over 14,700 UN Cadre Harmonisé (CH) / IPC humanitarian records across 28 Nigerian states and 668 LGAs. GitHub: https://github.com/thatssolade/ai-data-cleaning-and-analysis
I'm an empirical researcher with a Bachelor's degree in Psychology, focused on Artificial Intelligence and blending behavioural insight with technical curiosity to shape human-centred AI work.
I am Lade Omotade, a psychology graduate with a focus on artificial intelligence. I combine psychological insight with my interest in AI to explore human-centered models and applications.
Experience
Research & Content Specialist
Collider (Remote) · Sept 2025 - Present
Communications & Operations Associate
Collider / The Blast · Oct 2021 - Present
Communications Lead
The Youth Ally · Oct 2020 - Dec 2021
Education
Obafemi Awolowo University
B.Sc. Psychology · Class of 2024
Skills
An empirical data analysis project utilizing Generative AI (Gemini) and Python to parse, clean, and analyze over 14,700 UN Cadre Harmonisé (CH) / IPC humanitarian records across 28 Nigerian states and 668 LGAs. GitHub: https://github.com/thatssolade/ai-data-cleaning-and-analysis
An empirical data analysis project utilizing Generative AI (Gemini) and Python to parse, clean, and analyze over 14,700 UN Cadre Harmonisé (CH) / IPC humanitarian records across 28 Nigerian states and 668 LGAs. GitHub: https://github.com/thatssolade/ai-data-cleaning-and-analysis
Humanitarian datasets from international monitoring bodies often span multiple countries, dozens of sparse survey metrics, and unstandardized severity classes. In this project, I demonstrated how to structure, filter, and extract regional crisis indicators using prompt-assisted data workflows.
My results are as follows: Crisis Epicenters: Borno (3.78M), Kaduna (3.27M), and Katsina (3.25M) record the highest aggregate populations in Phase 3–5 (Crisis/Emergency). Urban Food Stress: High population density and urban inflation in Lagos account for over 2.69M individuals in Phase 3+ food stress. Emergency Pockets: Isolated 17 local government assessment units in critical Phase 4 (Emergency)
