projects | ashrood

Recent AI/ML Projects

Target Audience

This page is specifically created to showcase academic learning, technical competencies and professionals development in the field of AI/ML

Technical hiring Managers | AI ML Experts | Academic Reviewers

Artifact 1: AI/ML Historical Timeline

This piece of artifact is a visual timeline representation of the evolution of artificial intelligence from the inception at 1950 to the present time 2026 AI and human interaction. I started with research and did the analysis of several data to come up with an easy to understand communication tool using Figma and Adobe Illustrator. By completing this, I learned that AI an machine learning are driven by collaborations that heavily focused around data, hardware, and hard work with resilience. Meaning it takes strong determination to innovate in the space of AI/ML.

Objectives

Describe the service and how customers or clients can benefit from it. This is the place to add a short description with relevant details, like pricing, duration and how to book.

Process

My approach started with research and analysis, then conceptualizing and final design and presentation.

Skills Demonstrated

Machine learning fundamentals
Case study analysis
Technical communication
Deep learning concepts
Data interpretation

Key Learnings

Every human has the ability to create and innovate and collaboration is the best way to achieve outstanding success in the field of technology
Data plays a huge role in the development of AI/ML. AI cannot survive without data.
AI development balances technical performance with fairness, transparency, and the broader impact on society.
Hard work and consistent and time iteration gave birth to the key milestones in AI today.

Tools used include: Word doc, Google, Cupid, Google scholar, Adobe Illustrator, Figma, Claude, Perplexity.

References

Bhat, A. K. (2025, March 11). The evolution of AI: From foundations to future prospects. IEEE Computer Society. https://www.computer.org/publications/tech-news/research/evolution-of-ai , Class material

Artifact 2: Machine Learning Vs Deep Learning

Overview

This artifact compares the application of traditional machine learning and deep learning to solving problems. It explains how both technique work, majorly focusing on real world application of natural machine learning and deep learning techniques with cases studies in the real estate and healthcare space.

Understand and describe Artificial Neural Networks (ANNs) and Convolutional Neural Networks and how they learn.

Articulate their capabilities and application to real world problems

Objective

Tools

PubMed database

Adobe Express

Science direct

Perplexity

Adobe Illustrator

Class materials

Skills Demonstrated

Machine Learning Fundamentals
Deep Learning
Model training
Artificial Intelligence
Understanding context
Technical capability
Critical Thinking
Visual communication

Deep learning models require larger datasets, greater computational resources, and longer training times than traditional machine learning models.
Algorithm selection is dependent on the problem you're trying to solve.

Key Take away

References

Mienye, I. D., Swart, T. G., Obaido, G., Jordan, M., & Ilono, P. (2025). Deep convolutional neural networks in medical image analysis: A review. Information, 16(3), Article 195. https://doi.org/10.3390/info16030195

Yazdani, M. (2021). Machine learning, deep learning, and hedonic methods for real estate price prediction (arXiv:2110.07151). arXiv. https://arxiv.org/abs/2110.07151

Artifact 3: Gamifying STEM Learning for Teenagers

Introduction

Skills Demonstrated

This artifact highlights one of the skills I learn in this class. The AI Lab: was the very first workshop project I did with my group. For the group project we build a travel assistant agent that can help travelers plan their trip from one location to another, either within the same city or out of state.

Building on that knowledge, I designed a use case to help teenager gamify their learning experience by converting some of the time they spend on their social devices to learn skills that can be transferred to their classes.

Simplify the use and adoption of complex ML models and leverage it to improve STEM learning amongst middle and high school children.

Data Collection

Data Analysis

AI Ethics

Machine Learning Fundamental

Promp Engineering

Web Crawing

Process

Take away

My approach was using exploratory and contextual enquiry to gather information.

I reviewed 3 publications and conducted 5 unstructured interviews - both parents, living together with their children (age 14 and 16) and 1 dad with 13 year old teenage girl and 1 mom with boy of age 8.

Boys rarely talk about their academic struggles.
For the boy, gaming and studying together as a group do not over lap, they seem to have friends for different things.
Girls on the other hand are comfortable with sharing their struggles at school with their parent.
Parents would be willing to pay extra for theirs kids to learn new skills they can use in their academic.
Regular monitoring and feedback help identify and reduce bias after AI systems are deployed.

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Artifact 4: Navigating Data Bais through AI/ML Leadership

Introduction

The human brains takes mental shortcuts that can produce hidden thinking errors called cognitive biases, such as only noticing evidence that confirms what we already believe. These biases shape our safety, communication, and daily decisions. With awareness, we can learn to catch them and think more clearly. Similarly, AI learns from human-provided data, it can absorb and reproduce human biases.

Understand factors that contribute to successful AI/ML change leadership, integration. And develop strategies to navigating and manage human bias within that context.

Objective

Analytical thinking, Research, Data processing and synthesis, AI/ML Leadership, Stakeholder Influence and Collaboration, Clear communication.

Skills Demonstrated

I believe the most dangerous bias in this field isn't malicious it's the quiet, well-intentioned kind that comes from building for people who look, live, and present symptoms like the people already in the room. My job is to keep expanding who's in the room, at every stage, so the solutions we create actually serve the full breadth of humanity they claim to serve.

Value Preposition

Healthcare present a unique need for zero data human bias because every experiment, process and development is connected to the life of real people. Bias can be silent, building actionable strategies and processes for continuous validation and revaluation is very essential and same as providing team with clarity helps them remain grounded through operational culture.

Relevance

Tools used are Google, Chat GPT, Critical thinking, Data analysis and context development, Microsoft Office, Adobe Express, Illustrator.

AI-COMMERCIAL APPLICATIONS Oluwatosin Kayode.png

ARTIFACT 5

In this newsletter, I focus on the commercial applications of AIML in the current business streams across different industries from biopharma, to Robotics and defense. The aim is to see how organizations are developing models and the different ways the models are being used to make decisions and improve efficiencies.AI is helping businesses right now.

Tools

Teams Meeting, Science Direct, PubMed, Fit jam whiteboard, Google, Claude AI, Chat base, Illustrator.

Looking Forward

95% of enterprise generative AI pilots delivered no measurable P&L impact, according to MIT NANDA's 2025 analysis of over 300 public deployments. Data quality and availability are consistently named among the leading reasons pilots stall, with IP and security and compliance constraints close behind.