5/25/18 – Feras A. Batarseh, Data Science and Machine Learning Solutions for Software Engineering

Public Presentation: Data Science and Machine Learning Solutions for Software Engineering Challenges

Friday, May 25, 4:30 – 5:30 PM

Location: Engineering & Computation Building, Room 201

Hosted by the Computer Science & Engineering Department


You are invited to the following presentation. UAA parking is free on Fridays.


Abstract: The field of Data Science (DS) is witnessing a recent upsurge in research, tools development, and deployments of applications across multiple domains (for instance: within healthcare and government). Multiple software companies are shifting their focus to developing data-driven systems; also, many others are deploying Machine Learning or AI paradigms to their existing processes. In parallel, the academic research community has been injecting DS paradigms to provide solutions to traditional Software Engineering (SE) problems. When one observes the SE phases (Requirements, Design, Development, Testing, Release and Maintenance), it becomes clear that many AI paradigms (such as: Neural Networks, Machine Learning, Knowledge-Based Systems, Natural Language Processing) could be applied to improve the process and eliminate many of the major challenges that the SE field has been facing. In this talk, some use cases from the US government will be presented, and a brief review of the intersection of the fields of SE and AI will also be discussed.

Speaker Bio: Feras A. Batarseh is a Research Assistant Professor with the College of Science at George Mason University (GMU), in Fairfax, VA, USA. His research spans the areas of Data Science, Artificial Intelligence, and Context-Aware Software Systems. Dr. Batarseh obtained his Ph.D. and M.Sc. in Computer Engineering from the University of Central Florida (UCF) (2007, 2011), and a Graduate Certificate in Project Leadership from Cornell University (2016). His research work has been published at various prestigious journals and international conferences. Additionally, Dr. Batarseh published and edited several book chapters.


Dr. Batarseh has taught data science and software engineering courses at multiple universities including GMU, UCF as well as George Washington University (GWU). Prior to joining GMU, Dr. Batarseh was a Program Manager with the Data Mining and Advanced Analytics team at MicroStrategy, Inc., a global business intelligence corporation based in Tysons Corner, Virginia. During his tenure, he helped several clients make sense of their data and gain insights into improving their operations.

For more information on his research, and contact details, please refer to these webpages: https://cos.gmu.edu/ggs/people/faculty-staff/feras-batarseh-2/


5/21/18 – Canonical Ledgers LLC – Software Engineering Internship

Description:  Canonical Ledgers is a startup developing blockchain based applications. We maintain nodes in a blockchain p2p network, support community projects for the Factom blockchain, and develop decentralized applications. We are looking for a motivated intern to work on software design and server administration. This is a full or part time summer position with the possibility of subsequent full or part time work.

You will be working with our lead developer on:

  • Developing software for blockchain applications
  • Developing software for server monitoring and analysis
  • Developing tests for software and server infrastructure
  • Maintaining and monitoring servers that require high-reliability and uptime
  • Documenting server architecture and maintenance procedures A typical day will include a short stand-up meeting with our lead developer, independent coding and server maintenance, promptly responding to any server incidents with our lead developer, and a brief end-of-day code review.

Full Job Description

Code for Anchorage

Code for Anchorage

Anchorage, AK
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Code for Anchorage is a Code for America Brigade. Our focus is on serving our community through civic tech and open government data. Have an idea for a civic project? Want to …

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