Assoc Prof. Radwa El Shawi is the Head of Data Systems Group at the Institute of Computer Science, University of Tartu. She received her PhD in Information Technology from Sydney University, Australia, in 2013. She received her BSc and MSc degrees in Computer Engineering from the Computer Engineering department at the Arab Academy for Science and Maritime Transport, Egypt, in 2005 and 2008, respectively.
Dr. El Shawi's research interests include Automated Machine Learning (AutoML), Explainable Artificial Intelligence, Big Data analytics, and Medical Informatics. In addition to her teaching responsibilities, Dr. El Shawi has substantially contributed to numerous research projects. Dr. El Shawi's research contributions are evident through her extensive publication record, with over 50 refereed research publications in international journals and conferences. She is an Associate Editor at Evolutionary Intelligence and serves as an editorial board member for several esteemed international journals.
Google Scholar with full publication list can be found here | CV
Teaching: Explainable Automated Machine Learning (LTAT.02.023), Data Engineering (LTAT.02.007), Big Data Management (LTAT.02.003), and Data Systems Research Group Seminar (LTAT.05.013).
- "Big data and machine learning applications: developing a research direction" (Education and Youth Board), 01.1.2024-30.12.2028, budget:1,350,000 euros. (Principle investigator)
- "MachineLearnAthon - Developing Machine Learning Competencies for Interdisciplinary Teams at Universities" (Erasmus+ Cooperation Partnerships, funded by German Academic Exchange Service (DAAD)), 31.12.2022-30.12.2025, budget:400,000 euros. (work-package leader)
- "Connecting sustainable agroecosystems and farming with circular bioeconomy and new technologies" (Era-Net ICT-Agri-Food joint call 2021, funded by Estonian Research Council), 2021- 2024", budget: 94,000.00 euros. (Principle investigator)
- "Holistic Big Data Analytics-as-a-Service Framework" (Mobilitas Pluss Top Researcher Grant, funded by Estonian Research Council), 1.01.2018- 28.02.2023", budget: 747,404,33 EUR euros. (Co-investigator, PI is Prof Sherif Sakr)
- "Towards Usable Machine Learning for Efficient Predictive Analytics in the Healthcare Domain" (Mobilitas Pluss postdoctoral researcher grant, funded by European Regional Development Fund), 1.01.2019-31.12.2020, budget:100,000 euros. (Principal investigator)
Current
- PhD students:
Previous
- MSc students:
- Khatia Kilanava (defended: 2019
- Shota Amashukeli (defended: 2021)
- Hasan Tanvir (defended: 2022)
- Kayahan Kaya (defended: 2022)
- Dmitri Rozgonjuk (defended: 2023)
- Karl-Gustav Kallasmaa (defended: 2023)
- Radwa El Shawi, Moual H. Al-Mallah: Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality (Extended Abstract). IJCAI 2023: 6873-6877
- Alahdab, F., Shawi, R.E., Ahmed, A.I. and Al-Mallah, M.H., 2023. PATIENT-LEVEL EXPLAINABLE MACHINE LEARNING TO PREDICT MAJOR ADVERSE CARDIOVASCULAR EVENTS FROM SPECT MPI AND CCTA IMAGING. Journal of the American College of Cardiology, 81(8_Supplement), pp.1486-1486.
- H.Eldeeb , S. Amashukeli, R. El Shawi. BigFeat: Scalable and Interpretable Automated Feature Engineering Framework. IEEE International Conference on Big Data (Big Data) 2022 Dec 17 (pp. 515-524).
- R. El Shawi, S. Sakr. cSmartML-Glassbox: Increasing Transparency and Controllability in Automated Clustering. IEEE International Conference on Data Mining Workshops (ICDMW) 2022 Nov 28 (pp. 47-54).
- R. El Shawi, S. Sakr. TPE-AutoClust: A Tree-based Pipline Ensemble Framework for Automated Clustering. ICDMW 2022. IEEE International Conference on Data Mining Workshops (ICDMW) 2022 Nov 28 (pp. 1144-1153).
- R. El Shawi, & M. Al‐Mallah. Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality. Journal of Artificial Intelligence Research, 2022;75: 833-855. link
- R.El Shawi, K. Kilanava, and S. Sakr. "An interpretable semi-supervised framework for patch-based classification of breast cancer." Scientific Reports 12, no. 1 (2022): 1-15. link
- K. Viacheslav, K. Voormansik, R. Elshawi, and S. Sakr. "Exploiting time series of Sentinel-1 and Sentinel-2 to detect grassland mowing events using deep learning with reject region." Scientific Reports 12, no. 1 (2022): 1-15.[https://www.nature.com/articles/s41598-022-04932-6.pdf |link]
- R. El Shawi, H. Lekunze , S. Sakr. cSmartML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Clustering. IEEE BigData 2021. link
- R. ElShawi, Y. Sherif, M. Al‐Mallah, S. Sakr. Interpretability in healthcare: A comparative study of local machine learning interpretability techniques. Computational Intelligence. 2021 Nov;37(4):1633-50. link
- R. Elshawi, A. Wahab, Barnawi, S. Sakr. DLBench: a comprehensive experimental evaluation of deep learning frameworks. Cluster Computing. 2021 Feb 7:1-22. link
- R. El Shawi, Y. Sherif, S. Sakr. Towards Automated Concept-based Decision TreeExplanations for CNNs. In EDBT 2021 (pp. 379-384). link
- A. Abd Elrahman, M. El Helw, R. Elshawi, S. Sakr. D-SmartML: A Distributed Automated Machine Learning Framework. In2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) 2020 Nov 1 (pp. 1215-1218). link
- H Eldeeb, S. Amashukeli, R. Elshawi. An Empirical Analysis of Integrating Feature Extraction to Automated Machine Learning Pipeline. InInternational Conference on Pattern Recognition 2021 Jan 10 (pp. 336-344). Springer, Cham. link
- S. Amashukeli, R. Elshawi, S. Sakr. iSmartML: An Interactive and User-Guided Framework for Automated Machine Learning. In HILDA 2020 : Workshop on Human-In-the-Loop Data Analytics.link
- R. Elshawi, MH Al-Mallah, S. Sakr. On the interpretability of machine learning-based model for predicting hypertension. BMC medical informatics and decision making. 2019 Dec;19(1):1-32. link
- S. Dyrmishi, R. Elshawi, S. Sakr. A decision support framework for automl systems: A meta-learning approach. In 2019 International Conference on Data Mining Workshops (ICDMW) 2019 Nov 8 (pp. 97-106). IEEE. link
- R. Elshawi, M. Al-Mallah, S. Sakr (2019), On the interpretability of machine learning-based model for predicting hypertension. BMC medical informatics and decision making 19 (1). [PDF]
- R. Elshawi, Y. Sherif, M. Al-Mallah, S. Sakr (2019), ILIME: Local and Global Interpretable Model-Agnostic Explainer of Black-Box Decision. Proceedings of the 23 European Conference on Advances in Databases and Information Systems (ADBIS). [PDF]
- N. Mahmoud, Y. Essam, R. Elshawi, S. Sakr (2019), DLBench: An Experimental Evaluation of Deep Learning Frameworks. Proceedings of the IEEE International Congress on Big Data (BigDataCongress). [PDF]
- R. Elshawi, Y. Sherif, M. Al-Mallah, S. Sakr (2019), Interpretability in HealthCare A Comparative Study of Local Machine Learning Interpretability Techniques. Proceedings of the 32nd International Symposium on Computer-Based Medical Systems (CBMS). [PDF]
- T. Daghistani, R. Elshawi, S. Sakr, A. Ahmed, A. Al-Thwayee, M. Al-Mallah (2019), Predictors of In-hospital Length of Stay among Cardiac Patients: A Machine Learning Approach. International Journal of Cardiology, Elsevier. [PDF]
- R. Elshawi, S. Sakr, D. Talia, P. Trunfio (2018), “Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Service”. Journal of Big Data Research, Elsevier. [PDF]
- S.Sakr, R. Elshawi, Amjad Ahmed, Waqas T. Qureshi, Clinton Brawner, Steven Keteyian, Michael J. Blaha, and Mouaz H. Al-Mallah. "Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project." PloS one 13, no. 4 (2018). [PDF].
- S. Sakr, R. Elshawi, A. Ahmed, W. Qureshi, C. Brawner, S. Keteyian, M. Blaha, and M. Al-Mallah. "Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project." BMC medical informatics and decision making 17, no. 1 (2017): 174. [PDF].
- M. Al-Mallah, R. Elshawi, A. Ahmed, W. Qureshi, C. Brawner, M. Blaha, H. Ahmed, J. Ehrman, S. Keteyian, S. Sakr “Using machine learning to define the association between cardiorespiratory fitness and all-cause mortality”. American Journal of Cardiology , Volume 120 , Issue 11 , 2078 – 2084. [PDF].
- M. Al-khateeb, W. Qurashi, R. Odeh, A. Ahmed, S. Sakr, R. Elshawi, M. Bdier, M. Al-Mallah. "The Impact of Digoxin on Mortality in patients with Chronic Systolic Heart Failure: A Propensity – Matched Cohort Study ". International Journal of Cardiology, Elsevier, 2017, Volume 228, Issue 1, pp. 214-218. [PDF].
- F. Bajaber, R. El Shawi, O. Batarfi, A. Altalhi, A. Barnawi, S. Sakr. "Big Data 2.0 Processing Systems: Taxonomy and Open Challenges". Journal of Grid Computing, 2016, Volume 14, Issue 3, pp. 379-405. [PDF].
- O. Batarfi, R. Elshawi, A. Fayoumi, A. Barnawi, S. Sakr. “A Distributed Query Execution Engine of Big Attributed Graphs". SpringerPlus Journal, Springer, 2016, Volume 5, Issue 1, pp.1-26. [PDF].
- A. Barnawi, A. Awad, A. Elgammal, R. El Shawi, A. Almalaise, S. Sakr. "Runtime self-monitoring approach of business process compliance in cloud environments ". Cluster Computing, 2015, Volume 18, Issue 4, pp. 1503-1526. [PDF].
- O. Batarfi, R. Elshawi, A. Fayoumi, R. Nouri, S. Beheshti, A. Barnawi, S. Sakr. "Large Scale Graph Processing Systems: Survey and An Experimental Evaluation". Cluster Computing, Springer, 2015, Volume 18, Issue 3. [PDF]
- R. Elshawi, O. Batarfi, A. Fayoumi, A. Barnawi, S. Sakr. "Big Graph Processing Systems: State-of-the-art and Open Challenges". Proceedings of the IEEE BigDataService, 2015. [PDF].
- A. Awad, A. Barnawi, A. Algammal, A. Almalaise, R. Elshawi, S. Sakr. "Runtime Detection of Business Process Compliance Violations: An Approach based on Anti Patterns". Proceedings of the 30th ACM/SIGAPP Symposium On Applied Computing Enterprise Engineering Track, 2015. [PDF].
- A. Barnawi, O. Batarfi, S. Behteshi, R. Elshawi, A. Fayoumi, R. Nouri, S. Sakr. "On Characterizing the Performance of Distributed Graph Computation Platforms". Proceedings of the 6th TPC Technology Conference on Performance Evaluation and Benchmarking, 2014. [PDF].
- C. Otfried, R. El Shawi, and J. Gudmundsson. "A fast algorithm for data collection along a fixed track." Theoretical Computer Science 554 (2014): 254-262. [PDF].
- R. El Shawi, J. Gudmundsson, and C. Levcopoulos. "Quickest path queries on transportation network." Computational Geometry 47, no. 7 (2014): 695-709. [PDF].
- R. El Shawi, and J. Gudmundsson. "Fast query structures in anisotropic media." Theoretical Computer Science 497 (2013): 112-122. [PDF].