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Fakhri Abbas

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About Me

I am a Ph.D. candidate at the Department of Software and Information Systems at the University of North Carolina, Charlotte (UNC Charlotte). I recived my master's degree from Illinois State University. My research interests lie in the areas of data science with an emphasis on the beyond-accuracy measures of Recommender Systems. My primary research concerns in incorporating diversity, serendipity, and curiosity in recommender systems.

  • Name: Fakhri Abbas
  • Email: fabbas1@uncc.edu
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Ph.D. in Software Information Systems

University of North Carolina at Charlotte

Research area: Recommender Systems, Machine Learning, Natural Language Processing (NLP)

Dissertation: Generation of Diversity Driven Critique to Support Recipe Recommendation usingConversational Recommender Systems


Masters in Software Information Systems

Illinois State University

Track: Systems Development & Analysis


Bachelor in Computer Systems Engineering

Birzeit University

Minor in Business Administration

Industrial Experience

June, 2019 - August, 2019

Quantitative Researcher Intern

Hartree Partners

Develop systematic trading models utilizing (Un)/supervised Machine Learning techniques using state of the art packages

Work with traders and developers to identify best solution options

Tools: Python, Dash, SciPy, Pandas, Scikit Learn, Matplotlib

January, 2019

Data Science Intern


Building named entity recognition (NER) to detect artist name from image text caption using conditional random fields

Tools: SpaCy, Numpy, Pandas and NLTK libraries in Python

May, 2015 - August, 2016

Web Application Developer

Illinois State University

Design and develop a web application for academic advisors

Develop a course catalog search using Lucene

Tools: Php Larvel, HTML, CSS, JavaScript, Elastic Search (Lucene), SQL, JQuery


Web and Mobile Developer



August, 2016- Now

Teaching - Main Instructor

University of North Carolina at Charlotte
  • Web Application Development / Summer 2021, Spring 2021
  • Design and programming concepts for developing interactive web-based applications: HTML, CSS, the Document Object Model (DOM), event-driven programming, client-side scripting, and web security considerations.
    August, 2016- Now

    Teaching Assistant

    University of North Carolina at Charlotte
  • Network-based Application Development/Spring 2019, Fall 2020
  • This class discusses theory and practice of what is popularly called "Full Stack Web Development". It includes everything from Front-end and Back-end Development, Version Control Systems (VCS), Testing, Servers, Databases, Code Quality, and Deployment. At the end of the course, students will be able to create and launch a full web application.
  • Principles of Design Infrastructure/Fall 2017, Spring 2018
  • This course provides an introduction to concepts for the design and implementation of robust IT infrastructures. Topics include: system hardening, secured access, penetration testing, file storage services, as well as advanced topics in design and configuration of network based services.
  • Rapid Prototyping/ Fall 2018, Fall 2019
  • Introduction to learn various ways to rapidly prototype interface design ideas. Explores the theory behind rapid prototyping and how it relates to Human-Computer Interaction (HCI). Students study low fidelity prototyping methods such as FIDO design and paper prototyping, and then move into higher fidelity prototyping methods, such as throwaway digital prototyping. Evolutionary prototyping and interface building using high-level programming languages are covered. In addition to software prototyping, students perform blank model prototyping for physical devices.
  • Usable Security and Privacy/ Spring 2021
  • This course introduces students to the usability and user interface issues related to a variety of security and privacy technologies and tools. The course examine the security and privacy implications of users' behaviors with applications, and design guidelines for improving both the security and usability of those mechanisms.
  • Interactive Systems Design and Implementation/ Spring 2020
  • An introduction to the fundamentals of implementing interactive systems, with a focus on human-centered design. Topics include: architecture of interaction applications, event handling, direct vs. indirect GUI programming, 2D graphics programming, layout, design patterns, and design critique. Students learn the fundamental theory of the Model-View Controller Architecture and apply it to building a complete standalone application. Outcomes include the creation of a full application with multiple different views that communicate with a single model, as well as experience working with GUI programming and implementing common interaction design patterns such as direct manipulation and drag and drop.


    Serendipity in Health Information

    University of North Carolina at Charlotte

    This work implements a framework to find serendipitout health news crawled from Medical News Today. The framework divide serendipity into two components: Surprise, and Value.

    Incorporating Curisoity in Book Recommender

    University of North Carolina at Charlotte

    This work approximate an individual’s curiosity distribution over different levels of stimuli guided by the well-known Wundt curve in Psychology. Then, the approximated curisoity has been used to recommend books to the users.

    Sequence Recommendation in Learning Analytics

    University of North Carolina at Charlotte

    Recommend a sequence of scientific research papers to students that matches their background and interest.

    Increasing Diet Diversification using Conversational Recommender System

    University of North Carolina at Charlotte

    Using dynamic-critiquing in Critique-based Conversational Recommender Systemes to increase the diversity of recommended recipes during exploration to promote diet diversification


  • Abbas, F., Najjar, N., & Wilson, D. (2021, September). The Bites Eclectic: Critique-Based Conversational Recommendation for Diversity-Focused Meal Planning. In International Conference on Case-Based Reasoning (pp. 1-16). Springer, Cham.
  • Abbas, F., Najjar, N., & Wilson, D. (2021, August). Increasing diversity through dynamic critique in conversational recipe recommendations. In Proceedings of the 13th International Workshop on Multimedia for Cooking and Eating Activities (pp. 9-16).
  • Abbas, F., Najjar, N., & Wilson, D. (2021, April). Critique Generation to Increase Diversity in Conversational Recipe Recommender System. In The International FLAIRS Conference Proceedings (Vol. 34, No. 1).
  • Abbas, F., & Niu, X. (2019). One Size Does Not Fit All: Modeling Users’ Personal Curiosity in Recommender Systems. ArXivorg.
  • Mohseni, M., Maher, M. L., Grace, K., Najjar, N., Abbas, F., & Eltayeby, O. (2019, June). Pique: Recommending a personalized sequence of research papers to engage student curiosity. In International Conference on Artificial Intelligence in Education (pp. 201-205). Springer, Cham.
  • Abbas, F., & Niu, X. (2019, November). Computational Serendipitous Recommender System Frameworks: A Literature Survey. In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) (pp. 1-8). IEEE.
  • Niu, X., & Abbas, F. (2019, March). Computational surprise, perceptual surprise, and personal background in text understanding. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval (pp. 343-347).
  • Niu, X., Abbas, F., Maher, M. L., & Grace, K. (2018, April). Surprise me if you can: Serendipity in health information. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-12).
  • Abbas, F. (2018, October). Serendipity in Recommender System: A Holistic Overview. In 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) (pp. 1-2). IEEE.
  • Niu, X., & Abbas, F. (2017, July). A framework for computational serendipity. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 360-363).
  • Abbas, F. (2015). Sensitivity analysis for the winning algorithm in knowledge discovery and data mining (KDD) cup competition 2014. Illinois State University.