Sanja Brdar

Sanja Brdar

Position:Head of the Center for Information Technologies

Academic Rank: Research associate in the field of technical/technological sciences-electronics, telecommunications, and information technologies

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Sanja Brdar is a senior researcher and head of the Centre for Information Technologies at BioSense Institute. She received a PhD degree from the University of Novi Sad in 2016. Prior to starting her PhD studies, she spent two years working in the software industry with a major in databases design and development. In 2010 she was awarded a ten-month Basileus fellowship for joining the Bioinformatics Laboratory at University of Ljubljana which set the foundations for the PhD thesis on data/knowledge fusion in bioinformatics. Currently, she is actively involved in many projects including H2020 Antares, Dragon, Bestmap and Flexigrobots and teaches two master level courses at Faculty of Sciences. Her research interests include machine learning, explainable AI and bioinformatics. She works on ensemble methods, data fusion, clustering, and predictive modelling with applications in biology, agriculture, environmental sciences and health. She enjoys participating in worldwide data science challenges and with the team of researchers made significant results: placed third in Nokia Mobile Data Challenge (2012), finalist of Orange, France, Data for Development Challenge (2013), finalist/winner/third place of Syngenta Crop Challenge (2016, 2017, 2019) and finalist of UNDP Depopulation challenge (2020).

Center:

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CIT

1. DRAGON -Data Driven Precision Agriculture Services and Skill Acquisition
2. ANTARES- Centre of Excellence for Advanced Technologies in Sustainable Agriculture and Food Security
3. BESTMAP-Behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy
4. SHEALTHY- Non-Thermal physical technologies to preserve fresh and minimally processed fruit and vegetables
5. BREATHE – Real-time detection and quantification of bioaerosols relevant for human and plant health
6. AITool4WYP – Artificial intelligence-driven tool for early wheat yield prediction 
7. Sensing technologies for integrative monitoring of agricultural production
8. DIATOMIC- Digital Innovation Hubs boosting European Microelectronics Industry
9. IoF2020 – Internet of Food and Farm 2020
10. KATANA – Emerging industries as key enablers for the adoption of advanced technologies in the agrifood sector
11. CYBELE-FOSTERING PRECISION AGRICULTURE AND LIVESTOCK FARMING THROUGH SECURE ACCESS TO LARGE-SCALE HPC-ENABLED VIRTUAL INDUSTRIAL EXPERIMENTATION ENVIRONMENT EMPOWERING SCALABLE BIG DATA ANALYTICS
12. FLEXIGROBOTS-Flexible robots for intelligent automation of precision agriculture operations
13. eLTER PPP- eLTER preparatory phase project
14. Detection of usurped state-owned agricultural land and detection of burning of crop residues on the territory of APV
15. Smart-AKIS- European Agricultural Knowledge and Innovation Systems (AKIS) towards innovation-driven research in Smart Farming Technology
16. Soil Health Guards – Cultivating Soil Connectivity: Empowering Farmers and Youth Community as Citizen Scientists to Monitor Soil Health and Biodiversity
  1. Mimić, G., Brdar, S., Brkić, M., Panić, M., Marko, O., & Crnojević, V. (2020). Engineering Meteorological features to Select Stress tolerant Hybrids in Maize. Scientific reports, 10(1), 1-10.
  2. Lugonja, P., Brdar, S., Simović, I., Mimić, G., Palamarchuk, Y., Sofiev, M., Šikoparija, B. (2019). Integration of in situ and satellite data for top-down mapping of Ambrosia infection level, Remote Sensing of Environment, vol. 235, 111455.
  3. Marko, O., Brdar, S., Panić, M., Šašić, I., Despotović, D., Knežević, M., & Crnojević, V. (2017). Portfolio optimization for seed selection in diverse weather scenarios. PloS one, 12(9), e0184198. IF 2.766
  4. Brdar, S., Gavrić, K., Ćulibrk, D., & Crnojević, V. (2016). Unveiling spatial epidemiology of HIV with mobile phone data. Scientific reports, 6(1), 1-13.
  5. Brdar, S., Crnojević V., Zupan, B. (2015). Integrative clustering by non-negative matrix factorization can reveal coherent functional groups from gene profile data, IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2014.2316508 IF 2.093