Employees

Department of Computer Engineering

Assoc. Prof. Goran Mauša

Head, Department of Computer Engineering

Contact

Email

goran.mausa@riteh.uniri.hr

Phone

+385 51 505 710

(Int. 5710)

Office

2-50

More information

Research interests

  • Applied soft computing
    • Software defect prediction
    • Peptide chemistry
  • Machine Learning

Teaching

  • Introduction to Object-oriented Programming (undergraduate)
  • Software Engineering (undergraduate)
  • Evolutionary Computing (master)
  • Applied Soft Computing (PhD level)

Projects

Current:

  1. 2024 – 2028 COST action Searching for Nanostructured or Pore Forming Peptides for Therapy (SNOOPY), funded by The European Cooperation in Science and Technology (MC member Goran Mauša, grant no. CA23111)
  2. 2024 – 2025 UNIRI project Deep Learning – based Prediction of Therapeutic Peptides, funded by University of Rijeka (PI Goran Mauša, grant no. uniri-23-78)
  3. 2024 – 2025 UNIRI project Design of smart peptide-based materials with emerging functions, funded by University of Rijeka (PI Daniela Kalafatovic, grant no. uniri-23-16)
  4. 2024 – 2025 UNIRI-INOVA interdisciplinary project Artificial Intelligence for Gene Expression Prediction (ANTIGEN), funded by University of Rijeka (PI Goran Mauša, grant no. UNIRI-INOVA-3-23-1)
  5. 2024 – 2025 UNIRI-INOVA interdisciplinary project Making short peptides drug-like through serum stability improvement (MuPept sho), funded by University of Rijeka (PI Daniela Kalafatovic, grant no. UNIRI-INOVA-3-23-2)
  6. 2020 – 2024 COST action Connecting education and research communities for an innovative resource aware society (CERCIRAS), funded by The European Cooperation in Science and Technology (MC member Goran Mauša, grant no. CA19135)
  7. 2020 – 2024 HRZZ young researchers’ career development project, funded by Croatian Science foundation (supervisor: Goran Mauša, grant no. DOK-2020-01-4659)
  8. 2020 – 2025 HRZZ installation research project Design of short catalytic peptides and peptide assemblies (DeShPet), funded by Croatian Science foundation (PI Daniela Kalafatovic, team member: Goran Mauša, grant no. UIP-2019-04-7999)

Past:

  1. 2020 – 2024 HRZZ research project Reliable and Safe Complex Software Systems: From Empirical Principles to Theoretical Models in View of Industrial Applications (RELYSOFT), funded by Croatian Science foundation (PI Tihana Galinac Grbac, team member: Goran Mauša, grant no. IP-2019-04-4216)
  2. 2020 – 2023 ERASMUS+ project Promoting Sustainability as a Fundamental Driver in Software development Training and Education (project partner, local coordinator: Goran Mauša, grant no. 2020-1-PT01-KA203-078646)
  3. 2020 – 2023 UNIRI-plus project for new research directions Applying Machine Learning for the Discovery of Peptides with Catalytic Activity, funded by University of Rijeka (PI Goran Mauša, grant no. uniri-pr-tehnic-19-10)
  4. 2020 – 2023 Applied research project A computational model of flow, flooding and pollution dispersion in rivers and coastal marine areas (KLIMOD), funded by European structural and investment funds (PI Lado Kranjčević, team member: Goran Mauša, grant no. KK.05.1.1.02.0017)
  5. 2022 – 2023 UNIRI-INOVA interdisciplinary project Development of advanced rehabilitation device by using machine learning approach, funded by University of Rijeka (PI Ervin Kamenar, team member: Goran Mauša)
  6. 2020 – 2022 European Horizon 2020 project EUROCC – National Competence Centres in the Framework of EuroHPC (project partner, local PI: Lado Kranjčević, team member: Goran Mauša, grant no. H2020-JTI-EuroHPC-2019-2-951732)
  7. 2020 – 2022 European structure and inovation funds project Dig IT – Development of occupational standards and standard of qualifications in computer science. (project partner, RITEH experts: Ivan Štajduhar, Sandi Ljubić, Goran Mauša, grant no. UP.03.1.1.03.0061).
  8. 2020 – 2021 UNIRI multidisciplinary Covid-19 project Sars-CoV-2 supramolecular mimetics for discovery of peptides that induce viral entrapment (PI Daniela Kalafatović, substitute PI Goran Mauša)
  9. 2018 – 2019 Young researchers initial support project Adapting multi-objective genetic programming for solving complex combinatorial problems, funded by University of Rijeka (PI Goran Mauša, grant no. 18.10.2.1.01)
  10. 2018 – 2019 Bilateral Croatian – Slovenian project An empirical comparison of machine learning based approaches for code smell detection, funded by Croatian Ministry of Science and Education and Slovenian Research Agency (University of Rijeka and University of Maribor, team member: Goran Mauša)
  11. 2017 – 2019 ERASMUS+ project Focusing Education On Composability, Comprehensibility And Correctness Of Working Software, Key Action 2: Cooperation for innovation and the exchange of good practices (project partner, local coordinator: Goran Mauša, grant no. 2017-1-SK01-KA203-035402)
  12. 2014 – 2018 HRZZ installation research project Evolving software systems: analysis and innovative approaches for smart management (EVOSOFT), funded by Croatian Science foundation (PI Tihana Galinac Grbac, team member: Goran Mauša, grant no. UIP-2014-09-7945)

Journal publications

  1. M. Njirjak, L. Žužić, M. Babić, P. Janković, E. Otović, D. Kalafatovic, G. Mauša (2024). Reshaping the discovery of self-assembling peptides with generative AI guided by hybrid deep learning, Nature Machine Intelligence, https://doi.org/10.1038/s42256-024-00928-1
  2. M. Negovetić, E. Otović, D. Kalafatovic, G. Mauša (2024). Efficiently Solving the Curse of Feature-space Dimensionality for Improved Peptide Classification, Digital Discovery, Vol. 3, pp. 1182 – 1193, https://doi.org/10.1039/D4DD00079J
  3. T. Bazina, G. Mauša, S. Zelenika, E. Kamenar (2024). Reducing Hand Kinematics by Introducing Grasp-Oriented Intra-Finger Dependencies, Robotics, Vol. 13(6), 82, https://doi.org/10.3390/robotics13060082
  4. G. Mauša, M. Njirjak, E. Otović, D. Kalafatovic (2023). Configurable soft computing-based generative model: The search for catalytic peptides, MRS Advances, pp. 1–7, https://doi.org/10.1557/s43580-023-00629-8
  5. P. Janković, E. Otović, G. Mauša, D. Kalafatovic (2023). Manually curated dataset of catalytic peptides for ester hydrolysis, Data in Brief, Vol. 48, 109290, https://doi.org/10.1016/j.dib.2023.109290
  6. B. Gašparović, L. Morelato, K. Lenac, G Mauša, A. Zhurov, V. Katić (2023). Comparing Direct Measurements and Three-Dimensional (3D) Scans for Evaluating Facial Soft Tissue, Sensors, Vol. 23 (5), pp. 2412, https://doi.org/10.3390/s23052412
  7. M. Babić, P. Janković, S. Marchesan, G. Mauša, D. Kalafatovic (2022). Esterase Sequence Composition Patterns for the Identification of Catalytic Triad Microenvironment Motifs, Journal of Chemical Information and Modeling, Vol. 62 (24), pp. 6398-6410, https://doi.org/10.1021/acs.jcim.2c00977
  8. B. Gašparović, J. Lerga, G. Mauša, M. Ivašić-Kos (2022). Deep Learning Approach For Objects Detection in Underwater Pipeline Images, Applied Artificial Intelligence, Vol. 36 (1), 2146853, https://doi.org/10.1080/08839514.2022.214685
  9. I. Lučin, S. Družeta, G. Mauša, M. Alvir, L. Grbčić, D. Vukić Lušić, A. Sikirica, L. Kranjčević (2022). Predictive modeling of microbiological seawater quality in karst region using cascade model, Science of The Total Environment, Vol. 851 (2), 10, 158009, https://doi.org/10.1016/j.scitotenv.2022.158009
  10. L. Grbčić, S. Družeta, G. Mauša, T. Lipić, D. Vukić Lušić, M. Alvir, I. Lučin, A. Sikirica, D. Davidović, V. Travaš, D. Kalafatovic, K. Pikelj, H. Fajković, T. Holjević, L. Kranjčević (2022). Coastal water quality prediction based on machine learning with feature interpretation and spatio-temporal analysis, Environmental Modelling & Software, Vol. 155, 105458, https://doi.org/10.1016/j.envsoft.2022.105458
  11. E. Otović, M. Njirjak, D. Kalafatovic, G. Mauša (2022). Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides, Journal of Chemical Information and Modeling, Vol. 62, 12, pp. 2961–2972, https://doi.org/10.1021/acs.jcim.2c00526
  12. I. Erjavac, D. Kalafatovic, G. Mauša (2022). Coupled encoding methods for antimicrobial peptide prediction: How sensitive is a highly accurate model?, Artificial Intelligence in the Life Sciences, Vol. 2, 100034, https://doi.org/10.1016/j.ailsci.2022.100034
  13. M. Njirjak, E. Otović, D. Jozinović, J. Lerga, G. Mauša, A. Michelini, I. S̆tajduhar (2022). The Choice of Time–Frequency Representations of Non-Stationary Signals Affects Machine Learning Model Accuracy: A Case Study on Earthquake Detection from LEN-DB Data, Mathematics, Vol. 10(6), 965, pp. 1 – 17, https://doi.org/10.3390/math10060965
  14. E. Otović, M. Njirjak, D. Jozinović, G. Mauša, A. Michelini, I. S̆tajduhar (2022). Intra-domain and cross-domain transfer learning for time series data – How transferable are the features?, Knowledge-Based Systems, Vol. 239, 107976, pp. 1 – 18, https://doi.org/10.1016/j.knosys.2021.107976
  15. D. Kalafatovic; G. Mauša; D. Rešetar Maslov; E. Giralt (2020). Bottom-Up Design Approach for OBOC Peptide Libraries, Molecules, Vol. 25 (15), pp. 1 – 15, https://doi.org/10.3390/molecules25153316
  16. D. Kalafatovic; G. Mauša; T. Todorovski; E. Giralt (2019). Algorithm-supported, mass and sequence diversity-oriented random peptide library design, Journal of Cheminformatics, Vol. 11, 11:25, pp. 1 – 15, https://doi.org/10.1186/s13321-019-0347-6
  17. G. Mauša; T. Galinac Grbac (2017). Co-evolutionary Multi-Population Genetic Programming for Classification in Software Defect Prediction: an Empirical Case Study, Applied soft computing, Vol. 55, pp. 331 – 351, https://doi.org/10.1016/j.asoc.2017.01.050
  18. G. Mauša, T. Galinac Grbac, B. Dalbelo Bašić (2016). A Systematic Data Collection Procedure for Software Defect Prediction, Computer Science and Information Systems, Vol. 13 (1), pp. 173 –197, https://doi.org/10.2298/CSIS141228061M

Committees

  • Program committee at conferences and summer schools:
  • Organizing committee at conferences:
  • Program and organization committee for PhD Forum held within conferences
    • SoftCOM (Split, Sept 2022),
    • ConTEL (Zagreb, July 2021)
    • SoftCOM (Hvar, Sept 2020),
    • SoftCOM (Split, Sept 2019),
    • SST (Osijek, Oct 2018),
    • ConTEL (Zagreb, Jun 2017)
    • SoftCOM (Split, Sept 2016)
  • Organizing Chair at SQAMIA 2014 Workshop (Lovran, Sept, 2014)

Awards

  • TEACHING EXELLENCE AWARD for academic year 2022/23 by University of Rijeka (2024)
  • NATIONAL ANNUAL AWARD for RESEARCH EXCELLENCE in 2022 (Croatian Ministry of science and education, November 2023)
  • AWARD for the best researcher in engineering and biotechnical sciences for academic year 2022 by University of Rijeka (2023)
  • BEST PAPER AWARD at SQAMIA 2019 conference (Ohrid, North Macedonia, Spet 2019)
  • AWARD for the best young researcher in engineering and natural sciences for academic year 2016 by University of Rijeka (2017)
  • AWARD for the best research proposal at the summer school SS-SBSE 2016 (Cadiz, Spain, Jul 2016)

Conference papers & posters

  1. G. Mauša (2024) Deep learning for Peptide Classification From Data to Effective Models, EPS 2024
  2. M. Njirjak, D. Kalafatovic, G. Mauša (2024) Artificial intelligence-driven design of β-secretase 1 inhibitors, EPS 2024
  3. M. Njirjak, M. Babić, P. Janković, E. Otović, G. Mauša, D. Kalafatovic (2024) Can we accelerate peptide materials discovery using machine learning?, 7th ML and AI in Bio(Chemical) Engineering Conference
  4. G. Mauša, D. Kalafatovic (2024) Integration of Machine Learning and Genetic Algorithms as a Generative Model, INFORMS Optimization Society Conference (IOS) 2024
  5. G. Mauša, M. Njirjak, E. Otović, D. Kalafatovic (2023) Soft computing for constructive peptide design and peptide activty prediction, Japanese Peptide Symposium 2023
  6. G. Mauša, D. Kalafatovic (2023) Generative Machine Learning Models Applied to Peptide Self-Assembly, Peptide Self-Assembly 2023
  7. G. Mauša, M. Njirjak, E. Otović, L. Žužić, M. Babić, P. Janković, D. Kalafatovic (2023) Soft Computing Guided Discovery of Active Peptides, 28th American Peptide Symposium
  8. E. Otović, J. Lerga, D. Kalafatovic, G. Mauša (2023) Neuroevolution for the Sustainable Evolution of Neural Networks, Proceeding of MIPRO 2023, pp. 1045-1051
  9. M. Babić, P. Janković, A. S. Pina, G. Mauša, D. Kalafatovic (2022) Esterase sequence composition patterns as inspiration for the design of short catalytic peptides, 11th Austrian Peptide Symposium
  10. M. Njirjak, D. Kalafatovic, G. Mauša (2022) Genetic Algorithm-enhanced Parallel Chemical Space Exploration Utilising Multiple Peptide Libraries, 11th Austrian Peptide Symposium
  11. E. Otović, D. Kalafatovic, G. Mauša (2022) Transfer Learning for Improved Peptide Activity Prediction on Small Dataset, 11th Austrian Peptide Symposium
  12. D. Kalafatovic, P. Janković, M. Babić, A. S. Pina, G. Mauša (2022) Esterase Sequence Composition Patters As Inspiration For The Design Of Short Catalytic Peptides, GRC Chemistry and Biology of Peptides 
  13. G. Mauša, E. Otović, M. Njirjak, I. Erjavac, D. Kalafatovic (2022) Soft Computing for Constructive Peptide Design and Peptide Activity Prediction, GRC Chemistry and Biology of Peptides
  14. M. Njirjak, D. Kalafatovic, G. Mauša (2022) Genetic Algorithm-enhanced Parallel Chemical Space Exploration Utilising Multiple Peptide Libraries, 5th RSC Artificial Intelligence in Chemistry Symposium
  15. E. Otović, D. Kalafatovic, G. Mauša (2022) Transfer Learning for Improved Peptide Activity Prediction on Small Dataset, 5th RSC Artificial Intelligence in Chemistry Symposium
  16. M. Babić, G. Mauša, Ž. Svedružić, D. Kalafatovic (2021) Theoretical evaluation of enzyme active sites and catalytic peptides involved in ester hydrolysis, 27th Croatian Meeting of Chemists and Chemical Engineers
  17. M. Njirjak, E. Otović, D. Kalafatovic, G. Mauša (2021) Machine learning guided genetic algorithm for the discovery of novel antimicrobial peptides, 4th RSC Artificial Intelligence in Chemistry Symposium
  18. E. Otović, N. Črnjarić-Žic, G. Mauša (2021) Peptide Activity Prediction Improved by Hellinger Distance, Book of abstracts MFC 2021, pp. 31
  19. E. Otović, M. Njirjak, I. Žužić, D. Kalafatovic, G. Mauša (2020) Genetic Algorithm Parametrization for Informed Exploration of Short Peptides Chemical Space, Proceeding of SoftCOM 2020, pp. 1-3
  20. G. Mauša, T. Galinac Grbac, L. Brezočnik, V. Podgorelec, M. Heričko (2019) Software Metrics as Identifiers of Defect Occurrence Severity, Proceeding of SQAMIA 2019, pp. 9:1-2:9
  21. M. Gradišnik, S. Karakatič, T. Beranič, M. Heričko, G. Mauša, T. Galinac Grbac (2019) The Impact of Refactoring on Maintability of Java Code: A Preliminary Review, Proceeding of SQAMIA 2019, pp. 2:1-2:11
  22. M. Gradišnik, T. Beranič, S. Karakatič, G. Mauša (2019) Adapting God Class thresholds for software defect prediction: A case study, Proceeding of MIPRO 2019, pp. 1537-1542
  23. M. Gradišnik,S. Karakatič, G. Mauša, T. Beranič, M. Heričko (2019) Možnosti vpeljave umetne inteligence v proces razvoja programske opreme, Proceedings of DSI 2019, pp. 1-6
  24. M. Mohović, G. Mauša, T. Galinac Grbac (2018). Using Threshold Derivation of Software Metrics for Building Classifiers in Defect Prediction, Proceedings of SQAMIA 2018, pp. 11:1 – 11:9
  25. M. Miletić, M. Vukušić, G. Mauša, T. Galinac Grbac (2018). Cross-release code churn impact on effort-aware software defect prediction, Proceeding of MIPRO 2018, pp. 1460-1466
  26. G. Mauša, D. Kalafatovic, E. Giralt, T. Galinac Grbac (2017). Decision Support System for Combinatorial Peptide Libraries, Book of abstracts of ENABLE Symposium 2017, pp. 119
  27. G. Mauša; T. Galinac Grbac (2017). The Stability of Threshold Values for Software Metrics in Software Defect Prediction, Proceedings of MEDI 2017,  pp. 81-85
  28. M. Miletić, M. Vukušić, G. Mauša, T. Galinac Grbac (2017). Relationship Between Design and Defects for Software in Evolution, Proceeding of SQAMIA 2017, pp. 10:1 – 10:10
  29. T. Galinac Grbac; G. Mauša (2016). On the distribution of software faults in evolution of complex systems, Proceedings of ECSA 2016, Copenhagen, Denmark: ACM, pp. 2:1 – 2:7
  30. G. Mauša; B. Dalbelo Bašić; T. Galinac Grbac (2016). Critical Level of Data Imbalance For Machine Learning Algorithms In Software Defect Prediction, Proceedings of IWDS 2016, pp. 36-36
  31. G. Mauša; T. Galinac Grbac (2016). Assessing the Impact of Untraceable Bugs on the Quality of Software Defect Prediction Datasets, Proceedings of SQAMIA 2016, pp. 47-56.
  32. E. Rubinic; G. Mauša; T. Galinac Grbac (2015). Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies. Proceedings of SSBSE 2015, Graduate Student Track, pp 347-353.
  33. G. Mauša; N. Bogunović; T. Galinac Grbac; B. Dalbelo Bašić (2015). Rotation Forest in Software Defect Prediction, Proceedings of SQAMIA 2015, pp. 35-43.
  34. D. Spahić; G. Mauša; S. Kraljević Pavelić; T. Galinac Grbac (2015). Data Storage and Analysis System for Conducting Biotechnological Experiments. Proceedings of MIPRO CTI 2015, pp. 528-536.
  35. G. Mauša; T. Galinac Grbac; B. Dalbelo Bašić (2015). Data Collection for Software Defect Prediction – an Exploratory Case Study of Open Source Software Projects. Proceedings of MIPRO CTI 2015, pp. 513-519
  36. I. Štajduhar; G. Mauša (2015). Using String Similarity Metrics for Automated Grading of SQL Statements. Proceedings of MIPRO CIS 2015, pp. 1497-1502.
  37. N. Saulig; Ž. Milanović; G. Mauša (2015). Performance Comparison of Blind Source Separation Algorithms for Nonstationary Signals, Proceedings of IN-TECH 2015.
  38. G. Mauša, T. Galinac Grbac, B. Dalbelo Bašić (2014). Software Defect Prediction with Bug-Code Analyzer – a Data Collection Tool Demo. Proceedings of SoftCOM 2014, pp. 425-426
  39. G. Mauša, P. Perković, T. Galinac Grbac, I. Štajduhar (2014). Techniques for Bug-Code Linking. Proceedings of SQAMIA 2014, pp. 47-55.
  40. T. Galinac Grbac, G. Mauša, B. Dalbelo Bašić (2013). Stability of Software Defect Prediction in Relation to Levels of Data Imbalance. Proceedings of SQAMIA 2013, pp. 1-10.
  41. G. Mauša, T. Galinac Grbac, B. Dalbelo Bašić, M.-O. Pavčević (2013). Hill Climbing and Simulated Annealing in Large Scale Next Release Problem. Proceedings of the IEEE EuroCon 2013. pp. 452-459.
  42. G. Mauša, T. Galinac Grbac, B. Dalbelo Bašić (2012). Overview of Search-based Optimization Algorithms Used in Software Engineering. Proceedings of the IN-TECH 2012. pp. 409-412.
  43. G. Mauša, T. Galinac Grbac, B. Dalbelo Bašić (2012). Multivariate Logistic Regression Prediction of Fault-proneness in Software Modules. Proceedings of the MIPRO 2012. pp. 813-818.

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