Predrag Matavulj

Predrag Matavulj

Position:Research trainee

Academic Rank: Research trainee in the field of technical/technological sciences -electronics, telecommunications and Information Technologies

Google Scholar

Predrag Matavulj is a doctoral student of Informatics with strong knowledge in probability theory and statistics, numerical optimization, machine learning and neural networks, most of which he acquired during his Bachelor studies in Mathematics and Master studies in Data Science at Faculty of Sciences, University of Novi Sad. While working on his Master’s thesis, in March 2018, Predrag started his work as a junior researcher at BioSense Institute, where he utilized deep learning techniques to identify and classify bioaerosols. Since November 2018 Predrag has been teaching deep learning as a teaching assistant responsible for the Data Science Master’s course at the Faculty of Sciences. He is involved in several national and international projects including Antares, Dragon, agROBOfood, PROMIS.

Center:

proba 2

CIT

  1. Tešendić D., Boberić Krstićev D., Matavulj P., Brdar S., Panić M., Minić V., Šikoparija B. 2020. RealForAll: real-time system for automatic detection of airborne pollen, Enterprise Information Systems, DOI: 10.1080/17517575.2020.1793391
  2. Šaulienė, I., Šukienė, L., Daunys, G., Valiulis, G., Vaitkevičius, L., Matavulj, P., Brdar, S., Panić, M, Šikoparija, B., Clot, B., Crouzy, B., Sofiev, M. 2019. Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps. Atmospheric Measurement Techniques, 12, 3435-3452, doi: 10.5194/amt-12- 3435-2019
  3. Šikoparija, B., Mimić, G., Matavulj, P., Panić, M., Simović, I., Brdar, S. 2019. Short communication: Do we need continuous sampling to capture variability of hourly pollen concentrations?, Aerobiologia, doi: 10.1007/s10453-019-09575-1