Odjel za matematiku

Antonio Jovanović


PhD student
Department of Mathematics
Josip Juraj Strossmayer University of Osijek
Trg Ljudevita Gaja 6
Osijek, HR-31000, Croatia¸
email: ajovanov at mathos dot hr
office: 8 (ground floor)

 Google Scholar

Research Interests

Machine learning


MSc in mathematics, Mathematics and Computer Science, Department of Mathematics, University of Osijek, Croatia, 2021.
BSc in Mathematics, Department of Mathematics, University of Osijek, Croatia, 2019.


Refereed Proceedings

  1. A. Jovanović, I. Alqassem, N. Chappell, S. Canzar, D. Matijević, Predicting RNA splicing branchpoints, 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO), Opatija, 2022, 383-388
    RNA splicing is a process where introns are removed from pre-mRNA, resulting in mature mRNA. It requires three main signals, a donor splice site (5’ss), an acceptor splice site (3’ss) and a branchpoint (BP). Splice site prediction is a well-studied problem with several reliable prediction tools. However, branchpoint prediction is a harder problem, mainly due to varying nucleotide motifs in the branchpoint area and the existence of multiple branch-points in a single intron. An RNN based approach called LaBranchoR was introduced as the state-of-the-art method for predicting a single BP for each 3’ss. In this work, we explore the fact that previous research reported that 95% of introns have multiple BPs with an estimated average of 5 to 6 BPs per intron. To that end, we extend the existing encoder in the LaBranchoR network with a PointerNetwork decoder. We train our new encoder-decoder model, named RNA PtrNets, on 70-nucleotide-long annotated sequences taken from three publicly available datasets. We evaluate its accuracy and demonstrate how well the predictor can generate multiple branchpoints on the given datasets.

Technical Reports

  1. T. Prusina, D. Matijević, L. Borozan, J. Maltar, A. Jovanović, Compressing Sentence Representation with maximum Coding Rate Reduction (2023)
    In most natural language inference problems, sentence representation is needed for semantic retrieval tasks. In recent years, pre-trained large language models have been quite effective for computing such representations. These models produce high-dimensional sentence embeddings. An evident performance gap between large and small models exists in practice. Hence, due to space and time hardware limitations, there is a need to attain comparable results when using the smaller model, which is usually a distilled version of the large language model. In this paper, we assess the model distillation of the sentence representation model Sentence-BERT by augmenting the pre-trained distilled model with a projection layer additionally learned on the Maximum Coding Rate Reduction (MCR2)objective, a novel approach developed for general-purpose manifold clustering. We demonstrate that the new language model with reduced complexity and sentence embedding size can achieve comparable results on semantic retrieval benchmarks.


Konzultacije (Office Hours): Četvrtak (Thu) 12:00-14:00.  Konzultacije su moguće i po dogovoru.


 Zimski semestar ak.god. 21./22.

 Ljetni semestar ak.god. 21./22.

 Ljetni semestar ak.god. 22./23.


  • Birthplace: Osijek