Who I am
Adebayo is a machine learning engineer with four years of experience using data analytics and machine learning to improve processes, products, and outcomes. He is skilled at Machine Learning, MLOps, Natural Language Processing, AI Research, Data Science, and Software Engineering.
Some of the tools/frameworks Adebayo regularly works with are Cloud(Azure, Heroku), C++, Docker, DVC, Gensim, Git, HuggingFace, Java, JavaScript, Keras, Kubeflow, Matplotlib, MLflow, NLTK, Numpy, Pandas, PowerBI, Python, PyTorch, Seaborn, Scikit-Learn, Scipy, Spacy, Spark, SQL, and WandB.
Adebayo graduated with First Class Honors from the Department of Petroleum Engineering at the University of Ibadan. His final thesis which earned an A grade was on the application of machine learning models in the detection of leaks in natural gas pipelines. Away from academics, Adebayo was also a member of the campus press organization and the captain of a trophy-winning football team.
Currently, Adebayo is an MS student in Information Technology, specializing in applied machine learning and software development at one of the foremost AI research universities in the world – Carnegie Mellon University. His research interests include the applications of machine learning to healthcare & public policy; fair, responsible & explainable AI; causal machine learning; AI Ethics among others.
In his spare time, Adebayo usually enjoys playing or spectating chess, learning about interesting problems in the social sciences especially in the fields of behavioral psychology and philosophy, tutoring, and reading. He also occasionally follows football, tennis, and F1.
Adebayo is interested in applying his skills and intellect in impact-driven roles across Machine Learning, MLOps, Natural Language Processing, AI Research, Data Science, and Software Engineering aimed at creating novel solutions to important societal/business problems and enhancing the utility of data-driven products.
What I do
ProgrammingLanguages
C#, C++, Java, Python
DataFields
Big Data, Data Analytics, Data Science
Software Engineering
Agile methodology, Data Structures & Algorithms, Cloud, Continuous Integration, Continuous Deployment, Continuous Testing, Git, Docker Software Architecture and Design
ArtificialIntelligence
Computer Vision, Deep Learning, Machine Learning, Natural Language Processing, Recommender Systems
Automatic Question Generation, Ranking and Answering.
Summary: An automatic WH factoid questions generation, ranking and answering techniques from articles.
Technologies: Deep Learning (T5-based transformer, PyTorch), Docker, Git, Machine Learning (Random Forest), Natural Language Processing (Spacy, Scikit-Learn, HuggingFace, StanfordNLP, AllenNLP), and Sequence modelling (Hidden Markov Models).
Project Link
Hybrid News Recommendation System Along Moral Labels.
Summary: An hybrid news recommender system that attempts to recommend news items along the user's preferential moral lines.
Technologies: API Design (FastAPI), Cloud (Heroku), Deep Learning (LSTM, Keras),
Machine Learning (Gradient Boosting), Natural Language Processing (Scikit-Learn, NLTK), and MySQL.
Project Link
Semantic Segmentation of Crop Images.
Summary: A deep learning based architecture for semantic segmentation of crop images.
Technologies: API Design (Flask), Cloud (Azure), Computer Vision (OpenCV, ImageIO, TorchVision),
Deep Learning (FCNN and UNET Models, PyTorch), and Docker.
Project Link
To Promote or Not to Promote?
Summary: A machine learning modelling and data analysis of an employees promotion dataset.
This model won third place out of 500+ participants at the 2019 Artificial Hackathon organized by Data Science Nigeria.
Technologies: Data Analytics (Pandas, Numpy, Matplotlib, Seaborn),
Machine Learning (Logistic Regression, Gradient Boosting, XGBoost, CatBoost),
PostgreSQL.
Project Link
Recent Job History
Artificial Intelligence Engineer
IBM
Core Responsibilities
AI Research and Engineering, Trustworthy AI
Tools
AIF360, CI/CD/CT, Git, MLFlow, Numpy, Pandas, Scikit-Learn, Scipy, Python, WandB
Key Achievements
Improving the fairness of the boosting and
backpropagation algorithms by making modifications to
these algorithms leveraging anomalous subsets
identification.
Contributing novel findings on fairness and bias
into the open-source AI Fairness 360 toolkit.
Chief Technology Consultant
VINSIGTHE
Core Responsibilities
Machine Learning Engineering,
Machine Learning Operations, Software Engineering, Strategy & Innovation
Tools
Azure, Docker, DVC, Gensim, Git, Heroku, MLFlow, NLTK, Numpy, Pandas, Python, Scikit-Learn, Software Architecture and Design, Tensorflow, WandB
Key Achievements:
Streamlined ideation to production time by 60% by leading the remodeling of the production pipeline.
Created a medium-term growth strategy framework to help the company diversify its product streams.
Deployed an improved OCR module that leverages the transformer model to improve the word recognition rate of words from low-resource languages such as Yoruba and Hausa present in electronic books
Machine Learning Engineer
Cypher Crescent Limited
Core Responsibilities
Applied ML Research, Big Data Analysis, ML Engineering,
Natural Language Processing
Tools
Azure, C++, C#, Gensim, Git, Hadoop,
NLTK, Numpy, SQL, Pandas, PowerBI, PyTorch,
Scikit-Learn, Scipy, SHAP,
Spark, Statsmodels, Tensorflow
Key Achievements
Enhanced production forecast accuracy by 56%
by designing and deploying physics-inspired neural
networks in the analytic workflow.
Slashed document retrieval time by 77%
by digitizing documents using machine learning.
Developed an assistive chatbot that performs well log
interpretation tasks using boosted trees
and deep feed-forward neural networks reducing analysis time by 80%.
Deployed a machine learning pipeline that
leveraged the use of convolutional-recurrent neural
networks to help quantify the volume of gas flare from
an Oil and Gas production pipeline to reduce the potential
environmental impact of such gas flaring activities.
Contact Me
Looking for an experienced young professional for AI/ML/DS Roles?