Cornell • Information Science
I’m Ayotunde Ejiko - an Information Science student at Cornell, Division I sprinter, and community builder. I work at the intersection of product, data, and cybersecurity, focused on practical tools people can trust.
Focus: Product thinking • Data storytelling • Security & trust • Human-centered systems
About
I’m motivated by opportunities where technology improves outcomes for underserved communities - whether that’s building better systems, communicating insights clearly, or designing products people can trust.
Now
A quick snapshot of what I’m building and learning right now.
Experience
Impact-first highlights of what I’ve shipped, supported, and learned across product, data, security, and systems work.
Built low-cost hive monitoring workflows by combining sensors, camera checks, and environmental data into a single view for field decision-making.
Helped translate cybersecurity concepts into hands-on learning experiences and clearer training pathways for students.
Supported real infrastructure work in a safety-critical environment, learning how grid systems operate and how projects get executed.
Supported product teams with deprecation and cleanup work to improve client experience and reduce long-term maintenance burden.
Built momentum in coding, collaboration, and rapid prototyping through mentorship and hackathon-style projects.
Selected Work
Short, skimmable write-ups that show how I think, what I built, and what changed because of it.
How I approached low-cost hive monitoring, installation constraints, and turning raw readings into decisions.
Comparative analysis across NYC, DC, and LA (2000–2024) that turns arrest data into a policy story: monthly trends, reform inflection points, and persistent racial disparities.
Projects
Highlights
Athlete schedule + class load + org work - I’m organized and consistent.
I can turn complex info into decisions people can act on.
I care about building teams, mentorship, and inclusive spaces.
I use AI to move faster (drafting, analysis, automation) while staying accountable for accuracy, bias, and user impact.
I can go from messy inputs to a clear story: cleaning, validating, visualizing, and writing the takeaway.
I map the whole pipeline - people, process, and tech - and look for the highest-leverage fix.
Case Study
A low-cost IoT monitoring workflow for beehives that combines sensor readings, camera checks, and field context into a single view so beekeepers can spot problems early and act with confidence.
Hive conditions can change quickly. Without consistent monitoring, small issues (temperature swings, moisture, disturbances) can turn into colony loss. The challenge was building monitoring that was affordable, easy to install, and reliable outdoors.
Sensors capture environmental readings (ex: temperature / humidity) - readings are checked for continuity and stored - camera checks and field notes add context - a central view highlights trends and potential issues.
Case Study
A comparative policy + data analysis using arrest trends to examine how reforms (e.g., marijuana legalization, stop-and-frisk pullbacks, and Prop 47) changed enforcement patterns - and where racial disparities persisted.
Athletics
I compete as a sprinter and bring the same mentality to work: prep, repeat, improve. I’m happiest when goals are measurable and the process is disciplined.
Leadership
Awards
Book Time
Use the link below to book a 30-minute slot. If you’re reaching out about internships, collaborations, or project ideas, add a short note so I come prepared.
Prefer email instead? Reach me at ae447@cornell.edu.
Contact
Best way to reach me: email or LinkedIn. If you’re reaching out about internships, collaborations, or speaking opportunities, include the role/topic, a timeline, and any links I should review.
For fastest response, email me or message me on LinkedIn.