We are currently looking for motivated PostDoc, PhD students, and master students to work with us on wide range of topics in data-centric computing. If you are interested to join our group, send us your application including a detailed CV, and publications to radwa [dot] elshawi [at] ut [dot] ee. Please use the title "Joining Data Systems Groups as "your target" @ UT" for your email message.
Welcome to the Homepage of the Data Systems Research Group at University of Tartu.
The Data Systems Group (DSG) at the University of Tartu was founded by Prof. Sherif Sakr in 2018 and is currently led by Assoc Prof. Radwa El Shawi
Our research aims to significantly enhance the efficiency of data-intensive systems and democratize data science, making it accessible to a broader audience. We aspire to achieve this by creating a new generation of systems that empower users to unlock the full potential of their data. This involves investigating ways to develop systems that can better accommodate the latest developments in machine learning and harnessing machine learning to enhance the capabilities of existing systems.
Our group aims to get engaged and contribute to building the next generation of efficient, scalable, and insightful data systems. If you have the same interest, please contact us.
News
2024.07.24
We are happy to announce that Hassan Eldeeb and Radwa El Shawi's paper, 'Empowering Machine Learning with Scalable Feature Engineering and Interpretable AutoML' has been accepted for publication in IEEE Transactions on Artificial Intelligence (IEEE TAI).
Congratulations!
2024.07.24
Exciting news! We would like to congratulate to Radwa El Shawi and Sherif Sakr for publishing a paper FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness data in the Scientific Reports (part of the Nature Publishing Group UK), together with Mouaz H. Al-Mallah from DeBakey Heart & Vascular Center and Steven J. Keteyian, Clinton A. Brawner & Jonathan K. Ehrman from Henry Ford Hospital.
2024.03.27
Mohamed Maher, Esraa Sayed, Omar Sedeek, Ahmed Eldamaty, Amr Kamel and Radwa El Shawi published and presented paper "GizaML: A Collaborative Meta-learning Based Framework Using LLM For Automated Time-Series Forecasting" as part of "The 27th International Conference on Extending Database Technology (EDBT). 2024"
Full authors list:
Link: https://openproceedings.org/2024/conf/edbt/paper-249.pdf
2024.03.27
Our paper "Adaptive Handling of Out-of-order Streams in Conformance Checking" won the best paper award at DOLAP 2024: 26th International Workshop on Design, Optimization, Languages, and Analytical Processing of Big Data, co-located with EDBT/ICDT 2024, March 25, 2024, Paestum, Italy.
Congratulations to Kristo Raun, Riccardo Tommasini and Ahmed Awad!
Link: https://ceur-ws.org/Vol-3653/paper1.pdf