Bio. I am a Masters student in the School of Computer Science at Carleton University with a research focus on Learning Systems (encompassing learning automata and reinforcement learning), Machine Learning, and Deep Learning. I am also a Data Scientist at Pythian, a big data analytics company headquartered in Ottawa. I am a Google Certified Professional Data Engineer.

My research interests are in the theoretical and practical aspects of machine learning, the principles and algorithms that affect learning and how they are applied to specialized domains in making predictions, especially where there are rapid shifts in observable features as well as how mathematical principles, statistics and probabilistic reasoning can be applied to aid better decision making. I am also interested in researching the frontiers involving the synthesis of deep learning and reinforcement learning (what is now known as deep reinforcement learning), and how they can improve the learning task of an agent interacting in a non-deterministic, non-stationary environment (where exact mathematical analysis are no longer feasible), especially when the states of the environment are only partially observable.


Timeline.
2017-present: Data Scientist at Pythian Machine Learning, Deep Learning, Big Data Analytics, Google Certified Professional Data Engineer
2016 - 2018: Carleton University Computer Science M.Sc. candidate Reinforcement Learning, Learning Automata, Pattern Recognition, Adaptive Data Structures. Adviser: John Oommen
2015-2016: Graduate Assistant at the University of Calabar Programmming Languages, Machine Learning, Data Science
2015-2016: Research Scientist at EcoDev Konsult Greenhouse Gas Estimation from Land Use, Land Use Change and Forestry
2014: Complusory National Service to Nigeria Instructor, Database Design and Development
2009-2013: Babcock University: BSc Concentrations in Artificial Intelligence and Decision Support Systems

Books

Building Machine Learning and Deep Learning Models on Google Cloud Platform
In Progress
This book seeks to equip the reader from the ground up with all the essential principles and tools for building learning models. Machine learning and deep learning is rapidly evolving, and often it is overwhelming and confusing for a beginner looking to delve into this field. Many have no clue where to start. This book is a one-stop shop that takes the beginner on a journey to state-of-the-art theoretical understanding and practical mastery without assuming any pre-requisite. Although this book is written with the beginner at heart, it is not ridiculously verbose and repetitive, but written in a direct and succinct manner that will appeal to experts and serve as a refresher for the core concepts.
Ekaba Ononse Bisong


Java Laboratory Manual: A Quick Starter Guide
Published
This book serves as an introductory text to Computer Programming for first-year undergraduate students of Computer Science, Mathematics and Statistics at the University of Calabar. Each section ends with an end-to-end programming problem, so students have an early feel for sound software development principles. The overall goal of this book is to present the base paradigms of programming and in particular Object Oriented Programming using Java. Notwithstanding, the concepts presented in this book enables students to develop an understanding of the fundamental principles that underscore most popular programming languages such as C++, Python, and JavaScript.
Available only at the University of Calabar, Calabar, Nigeria
Bisong, E.O., Eteng, I.E., Ele, S.L., Arikpo, I.I., Edim, A.E., Ogban, F.U., and Essien, E.E.



Thesis

[PDF] On Designing Adaptive Data Structures with Adaptive Data "Sub"-Structures (In progress)

Ekaba Bisong,
M.Sc. Thesis, 2018





Research

Built to Last or Built Too Fast? Evaluating Prediction Models for Build Times
Accepted, Proceedings of the 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR 2017). Buenos Aires, Argentina. 20-21 May 2017. This research focuses on finding a balance between integrating often and keeping developers productive. We propose and analyze models that can predict the build time of a job. Such models can help developers to better manage their time and tasks.
Bisong, Ekaba, Tran, Eric, and Baysal, Olga




Pet Projects

Cereal Classifier: Android App
July 2017. This project builds a cereal box image classifier from the Google InceptionV3 model with TensorFlow via transfer learning. The app is trained to identify a select number of cereals.
Credit Card Fraud Detection
March 2017. Credit card fraud detection is the science and art of detecting unusual activity in credit transactions. A significant challenge for credit fraud detection research is the availability of real-world data due to privacy and legal concerns.
Predicting Forest Cover Type
February 2017. This project develops a classification model using XGBoost to accurately classify and predict forest cover type from a set of cartographic features. The data is from the UCI Machine Learning repository.
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Built to Last or Built Too Fast?
December 2016. Long build times can be an issue when integrations are frequent. This project is a cubist “minimum-viable-product” PoC model to predict the build time of a job.
Analysis of Agricultural Trends in Nigeria: A Shiny App
June 2015. This application displays a barplot of several agricultural trends in Nigeria from 1961 to 2012. The collected data is from the World Bank Country Database.



©Ekaba Bisong
Design adapted from Andrej Karpathy