cv
Basics
Name | Daniel Agyapong |
Label | Machine Learning Engineer |
da2343@nau.edu | |
Phone | (520) 491-0072 |
Url | https://engineerdanny.github.io |
Summary | A highly motivated and enthusiastic Machine Learning Engineer with a passion for developing innovative machine learning algorithms and applications. I have a strong background in machine learning, deep learning, and computer vision. I am a team player with excellent communication skills. |
Education
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2022.08 - 2026.05 Flagstaff, Arizona
PhD
Northern Arizona University
Computer Science
- Deep Learning
- Data Mining and Machine Learning
- Modern Regression
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2017.08 - 2021.05 Kumasi, Ghana
Bachelor's Degree
Kwame Nkrumah University of Science and Technology
Electrical/Electronics Engineering
- Differential Equations
- Digital Systems
- Control Systems
- Power Systems
Work
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2022.08 - current Graduate Research Assistant
Northern Arizona University
Training and testing new machine learning algorithms and developing optimisation algorithms for microbiome sparse data sets.
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2022.05 - 2022.09 Software Engineer Intern
Google Summer of Code
Modified the tool, Rperform, to track quantitative performance metrics of R packages. Developed a custom GitHub Action to make it easier for package developers to use Rperform to test their code.
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2021.08 - 2021.05 Research and Teaching Assistant
Kwame Nkrumah University of Science and Technology
Assisted lecturers in teaching undergraduate courses in electrical/electronics engineering.
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2021.03 - 2022.05 Full Stack Engineer
Chosen IT Business Solutions, LLC
Developed and maintained cross-platform applications using Flutter and PhP Laravel. Worked closely with clients to understand their requirements and deliver high-quality software solutions.
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2020.04 - 2021.03 Full Stack Engineer (Remote)
Godlives Delivery (Sweden)
Built and maintained cross-platform mobile app with Flutter and backend with Node.js utilizing technologies like MongoDB for the database management, OneSignal for notifications and MailGun for email API service.
Volunteer
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2023.06 - 2023.08 Flagstaff, Arizona
Publications
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2023.09 Cross-Validation for Training and Testing Co-occurrence Network Inference Algorithms
Arxiv
This paper presents a novel cross-validation method for training and testing co-occurrence network inference algorithms.