Dr Jad Abbass

Contact

Jad Abbass
About
Teaching and learning
Research
Leadership and management

About

I am a Lecturer in the School of Computer Science and Mathematics. My teaching duties are primarily focused on undergraduate and postgraduate modules related to Data Science and Machine Learning. I am the Co-founder and Leader of the new MSc Artificial Intelligence (AI) course, which is currently under validation and will be launched in early 2025. Additionally, I supervise MSc dissertations and BSc final-year projects. Before joining Kingston University in June 2022, I taught undergraduate and postgraduate Computer Science modules in Lebanon for over 13 years, specializing in Computer Programming, Design and Analysis of Algorithms (Time Complexity), and Artificial Intelligence.

In terms of research, besides my interests in AI and Machine Learning, I have extensive experience in Computational Biology, particularly in Protein Structure Prediction (PSP), Estimation of Model Accuracy (EMA)/Model Quality Assessment (MQA), and Structural Alphabets.

I am a reviewer for several journals, including PLoS ONE, PLoS Computational Biology, PLoS Digital Health, the Computational & Structural Biotechnology Journal (Elsevier), Computational Biology & Chemistry (Elsevier), BMC Bioinformatics, Frontiers in Biomedical Sciences, and the Journal of Chemical Information and Modeling (ACS).

I am a Fellow of the Higher Education Academy (FHEA), a member of the Biochemical Society (BS), and a member of the Institute of Electrical and Electronics Engineers (IEEE).

Academic responsibilities

Lecturer in Data Science / Programme Co-Founder & Leader (MSc AI)

Qualifications

  • PhD, Computer Science - Kingston University, London, UK
  • MSc, Computer Science - Lebanese American University, Beirut, Lebanon
  • BSc, Computer Science - Beirut Arab University, Beirut, Lebanon
  • FHEA - Fellow of the Higher Education Academy

Teaching and learning

Undergraduate courses taught

Postgraduate courses taught

Research

My research interests are mainly in the fields of Protein Bioinformatics, Artificial Intelligence and Machine Learning.

Areas of specialism

  • Computational Biology
  • Artificial Intelligence
  • Machine Learning

Publications

Jump to: Article | Book Section | Conference or Workshop Item | Thesis
Number of items: 11.

Article

Abbass, Jad and Parisi, Charles (2024) Machine learning-based prediction of proteins’ architecture using sequences of amino acids and structural alphabets. Journal of Biomolecular Structure and Dynamics, ISSN (print) 0739-1102 (Epub Ahead of Print)

Abbass, Jad and Nebel, Jean-Christophe (2020) Rosetta and the journey to predict proteins' structures, 20 years on. Current Bioinformatics, 15(6), pp. 611-629. ISSN (print) 1574-8936

Abbass, Jad and Nebel, Jean-Christophe (2020) Enhancing fragment-based protein structure prediction by customising fragment cardinality according to local secondary structure. BMC Bioinformatics, 21, p. 170. ISSN (online) 1471-2105

Abbass, Jad and Nebel, Jean-Christophe (2017) Reduced fragment diversity for alpha and alpha-beta protein structure prediction using Rosetta. Protein & Peptide Letters, 24(3), pp. 215-222. ISSN (print) 0929-8665

Abbass, Jad and Nebel, Jean-Christophe (2015) Customised fragments libraries for protein structure prediction based on structural class annotations. BMC Bioinformatics, 16(136), ISSN (online) 1471-2105

Book Section

Abbass, Jad, Nebel, Jean-Christophe and Mansour, Nashat (2014) Ab initio protein structure prediction: methods and challenges. In: Elloumi, Mourad and Zomaya, Albert Y., (eds.) Biological Knowledge Discovery Handbook: preprocessing, mining and postprocessing of biological data. New Jersey, U.S. : Wiley-Blackwell. pp. 703-724. (Bioinformatics: Computational Techniques and Engineering) ISBN 9781118132739

Conference or Workshop Item

Abbass, Jad and Nebel, Jean-Christophe (2021) Adjusting local conformational sampling for fragment assembly protein structure prediction based on secondary structure complexity. In: 3rd IEEE International Multidisciplinary Conference on Engineering Technology (IMCET 21); 08-10 Dec 2021, Beirut, Lebanon.

Abbass, Jad and Nebel, Jean-Christophe (2020) Enhanced Rosetta-based protein structure prediction for non-beta sheet dominated targets. In: 5th IEEE Middle East and Africa Conference on Biomedical Engineering (MECBME 2020); 27 - 29 Oct 2020, Amman, Jordan.

Abbass, Jad and Nebel, Jean-Christophe (2019) SCOP-Aided Fragment Assembly Protein Structure Prediction. In: 2019 Fourth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA); 03 - 04 Jul 2019, Beirout, Lebanon. (Unpublished)

Abbass, J. and Haraty, R. (2009) Bit-level locking for concurrency control. In: IEEE/ACS International Conference on Computer Systems and Applications; 10-13 May 2009, Rabat, Morocco. ISBN 9781424438075

Thesis

Abbass, Jad (2018) Secondary structure-based template selection for fragment-assembly protein structure prediction. (PhD thesis), Kingston University, .

This list was generated on Tue Jul 2 07:09:40 2024 BST.

Leadership and management

Course Co-Founder and Leader - MSc AI (under validation)

University responsibilities

  • Faculty Research Degrees Committee (Member)
  • Research Staff Development Group (Member)

Social media

LinkedIn