Dr Jad Abbass

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Jad Abbass
About
Teaching and learning
Research
Leadership and management

About

I am a Lecturer in Data Science at the Department of Computer Science in the Faculty of Engineering, Computing and the Environment. My teaching duties are mainly dedicated to "Applied Data programming" Module for the MSc in Data Science course and "Machine Learning with Python" Module for final Year BSc in Computer Science. In addition, I supervise MSc Dissertations and BSc Final year Projects. Before I joined Kingston University in June 2022, I had been teaching undergraduate and postgraduate Computer Science modules related mainly to Computer Programming, Design and Analysis of Algorithms (Time Complexity), and Artificial Intelligence for more than 13 years in Lebanon. I hold a PhD in Computer Science from Kingston University (KU).

I am planning - through my role in the booming KU's MSc in Data Science course -  to (1) deliver to the workplace 'ready-to-employ' Data Scientists having in-depth practical knowledge in the state-of-the-art techniques and tools, and (2) to widespread the very useful applications of Data Science in the local, national and international communities to include some SME that are still 'falling behind'.

On the research side, besides my interests in AI/Machine Learning, I have an extensive experience in Computational Biology, in particular, Protein Structure Prediction (PSP), Estimation of model accuracy (EMA) - previously known as Model Quality Assessment (MQA) - and Structural Alphabets. One of my current aims is to apply Bio-inspired algorithms and paradigms to some aspects of Data Science and Machine Learning.

I am a reviewer for: PLoS ONE, PLoS Computational Biology and PLoS Digital Health, Computational and Structural Biotechnology Journal (Elsevier), Computational Biology and Chemistry (Elsevier), BMC Bioinformatics, Frontiers in Biomedical Sciences, and Journal of Chemical Information and Modelling (ACS) and a member of the Biochemical Society (BS) and the Institute of Electrical and Electronic Engineers (IEEE).

Academic responsibilities

Lecturer in Data Science

Qualifications

  • PhD - Computer Science - Kingston University, London, UK
  • MSc - Computer Science - Lebanese American University, Beirut, Lebanon
  • BSc - Computer Science - Beirut Arab University, Beirut, Lebanon

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: 10.

Article

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 Thu Sep 21 06:55:21 2023 BST.

Leadership and management

University responsibilities

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

Social media

LinkedIn