Wisdom Aselisewine

Wisdom Aselisewine

Ph.D Student of Statistics

University of Texas at Arlington

Biography

Wisdom Aselisewine is a Ph.D Student of Statistics at the University of Texas at Arlington. He obtained a B.Sc degree in Actuarial Science, from the University for Development Studies in Ghana in 2015, and a Master’s degree in Actuarial Science and Statistics, from the Ball state University in USA in 2020. His research interests include Machine Learning, Survival Analysis, Statistical Computing and Actuarial Science.

Interests
  • Machine Learning
  • Survival Analysis and Statistical Computing
  • Actuarial Science
Education

  • PhD in Statistics, 2024

    University of Texas at Arlington

  • MA in Actuarial Science and Statistics, 2020

    Ball State University

  • BSc in Actuarial Science, 2015

    University for Development Studies

Skills

Programming

R-80%, Python-60%, SAS-40%

Actuarial Science

  • Exams passed: Exams FM, P, IFM, and STAM
  • Technical skills: Access/VBA/Excel-90%, SQL-65%, Tableau-85%

Machine Learning

Regression/Classifications-95%, Clustering/Association Rule Learning-80%, Deep Learning/Reinforcement Learning/NLP-50%

Experience

 
 
 
 
 
Graduate Teaching Assistant
University of Texas at Arlington
Sep 2020 – Present Arlington, TX

Responsibilities include:

  • Teaching a lab class.
  • Assisting in faculty research.
  • Holding office hours to assist students.
 
 
 
 
 
Ball State University
Graduate Teaching Assistant
Ball State University
Aug 2018 – May 2020 Muncie, IN

Responsibilities include:

  • Taught quantitative reasoning class.
  • Created course instructions outlines for teaching.
  • Tutored over 60 students at the computer lab for one semester
 
 
 
 
 
Univeristy for Development Students
Graduate Teaching Assistant
Univeristy for Development Students
Aug 2011 – Dec 2015 Tamale, Ghana

Responsibilities include:

  • Tutored classes of over fifty students on subjects such as Calculus, Statistics, Actuarial and financial mathematics for two years.
  • Assisted faculty members in their research and grading of students’ assignments and quizzes for two years.

Accomplish ­ments

College of Science Graduate Fellowship.
University of Texas at Arlington Sep 2020
Outstanding Graduate Teaching Assistant Recognition.
Ball State University May 2020
Spencer Foundation Pre-Dissertation Scholarship
Spencer Educational Foundation May 2020
International Association Black Actuaries Scholarships Recipient.
International Association Black Actuaries May 2019 – May 2020
Graduate Merit Fellowship.
Ball State University Aug 2018 – May 2020
Prudential Life Insurance Best Student Award.
Prudential Life Insurance Dec 2015 – Nov 2017

My Blog

Final Project
Introduction The goal of this project is to train a model that can predict house prices accurately with minimal errors for house buyers and house sellers. The value of a house is usually determined by several factors which include location, square footage, number of bedroom, number of baths and so on.
Last updated on Sep 8, 2022 34 min read
Data mining: Assignment 3
The goal of this assignment is to learn about the Naive Bayes Classifier (NBC). In this assignment we’re going to implement the NBC from scratch without using NBC library in python and perform sentiment analysis using text dataset about amazon reviews.
Last updated on Sep 8, 2022 355 min read
Data mining: Assignment 2
The aim of this project is to demonstrate the problems of underfitting and overfitting. We will also illustrate on how to use linear regression with polynomial features to approximate nonlinear functions.
Last updated on Sep 8, 2022 10 min read
Data Mining: Assignment 1
The goal of this project is to build a CIFAR-10 image classifier using pytorch and tensorflow. In this project, different classifiers are built for the purposes of comparisons. To compare the performances among the fitted models, we will use the predicted accuracy and the estimated loss of each model as comparison metric.
Last updated on Sep 8, 2022 17 min read
Data Mining Project 1
The primary objective of this project is to build a predictive Machine Learning model that predicts which passengers survived the Titanic shipwreck in April 15, 1912 using passengers data. Also, we will perform a variable or feature importance test in this study which will help us to better improve our predictive model and gain more insights to the given data.
Last updated on Sep 8, 2022 26 min read

Projects

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All Machine Learning Actuarial Science Statistics
On the integration of decision trees with mixture cure model
SUPPLEMENTARY MATERIALS https://github.com/Aselisewine/Aselisewine
Kaggle Competition:Titanic-Machine Learning from Disaster
The aim of this project is to build a predictive Machine Learning model that predicts which passengers survived the Titanic shipwreck.
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