Andrew Pownuk
Unit: EPCC
Title: Computer Science Instructor
Research Expertise
At present, Dr. Pownuk’s research focuses on autonomous, self-adaptive, self-learning computational methods that can be applied to the automated development of mathematical models and related computer code. His research has been successfully applied to the development of various online learning systems.
Machine Learning Certifications
- 2024 – Machine Learning Intensive Part II, The Coding School
- 2024 – Introduction to Machine Learning, Stanford University, Continuing Studies Program
- 2021 – Machine Learning, an online non-credit course authorized by Stanford University and offered through Coursera
Research Experience
- 2020 – Participated in a Summer Faculty Research Fellowship at the U.S. Army CCDC Army Research Laboratory, Adelphi, MD, working on machine learning-based optimization.
- 2006 – Collaborated with the Bergen Language Design Laboratory (BLDL), Department of Computer Science, University of Bergen, Norway. This project focused on automated software development based on a given mathematical description.
- 2003 – Worked on an automated method for solving optimization problems for the COCONUT Project at the Department of Mathematics, Faculty of Natural Sciences and Mathematics, University of Vienna, Austria.
Areas of Expertise/Keywords
- Autonomous Computational Methods
- Self-Adaptive Systems
- Machine Learning
