Roberts Kadiķis is a senior researcher at the Institute of Electronics and Computer Science (EDI) in the Robotics and Machine Perception Laboratory. Since 2011, he has conducted research in the fields of computer vision and artificial intelligence. Roberts studies and develops, as well as leads projects on machine learning methods for image analysis and generation, and on the practical application of such methods to solving problems in transportation, mobility, biomedicine, and industry. Roberts’s in-depth interests include the creation of synthetic training data using generative artificial intelligence (for example, diffusion models and generative adversarial networks — GANs), as well as the development of computationally efficient computer vision algorithms. The efficiency of object detection algorithms was also the topic of Roberts’s doctoral thesis, which he defended in 2018 at Riga Technical University.
As an expert for the Latvian Council of Science (LZP) in the fields of Natural Sciences (Computer Science and Informatics) and Engineering and Technology (Electrical Engineering, Electronics, Information and Communication Technologies), Roberts also serves on the University of Latvia’s Promotion Council.
Roberts Kadiķis headed the Robotics and Machine Perception Laboratory from its founding in 2019 until 2025 and for four years was the Chair of EDI’s Scientific Council. He currently serves on EDI’s Ethics Committee.
He has successfully supervised one doctoral thesis to defense and is currently supervising the development of five more doctoral dissertations.
Recent projects
- Efficient module for automatic detection of people and vehicles using video surveillance cameras (VAPI) #ERDF
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Intelligent Motion Control under Industry 4.E (IMOCO4.E) #H2020
- AI-improved organ on chip cultivation for personalised medicine (AImOOC) #H2020
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Augmenting and Evaluating the Physical and Digital Infrastructure for CCAM deployment (AUGMENTED CCAM) #Horizon Europe
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Histological recognition and analysis of veterinary tumors surgical margins by using artificial intelligence and multimodal imaging (HAVeT-AI ) #Horizon Europe
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Holographic microscopy- and artificial intelligence-based digital pathology for the next generation of cytology in veterinary medicine (VetCyto) #Horizon Europe
- Soil biological quality index (QBS) determination using machine learning (QBS) #LZP FLPP
Recent publications
- Lulla M., Rutkovskis A., Slavinska A., Vilde A., Gromova A., Ivanovs M., Skadins A., Kadikis R., Elsts A. Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines. Data. 2021; 6(4):38. https://doi.org/10.3390/data6040038
- Maksims Ivanovs, Roberts Kadiķis, Kaspars Ozols. 2021. “Perturbation-based methods for explaining deep neural networks: A survey” Elsevier B.V., https://doi.org/10.1016/j.patrec.2021.06.030
- Rihards Novickis, Aleksandrs Levinskis, Vitalijs Fescenko, Roberts Kadikis, Kaspars Ozols, Anna Ryabokon, Rupert Schorn, Jochen Koszescha, Selim Solmaz, Georg Stettinger, Akwasi Adu-Kyere, Lauri Halla-aho, Ethiopia Nigussie, Jouni Isoaho. "Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project", Appl. Sci. 2022, 12(1), 168;
- Romans Maliks, Roberts Kadikis. Multispectral Data Classification with Deep CNN for Plastic Bottle Sorting. 2021 6th International Conference on Mechanical Engineering and Robotics Research (ICMERR), 2022, pp. 58-65, ISBN: 978-1-6654-0641-3
- Ivanovs, Maksims, Kaspars Ozols, Artis Dobrajs, and Roberts Kadikis. 2022. "Improving Semantic Segmentation of Urban Scenes for Self-Driving Cars with Synthetic Images" Sensors 22, no. 6: 2252. https://doi.org/10.3390/s22062252
- Arents, J., Lesser, B., Bizuns, A., Kadikis, R., Buls, E., Greitans, M. (2022). Synthetic Data of Randomly Piled, Similar Objects for Deep Learning-Based Object Detection. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_59
- Mindaugas Tamošiūnas, Roberts Kadiķis, Mikus Melderis, Romāns Maļiks, Diāna Duplevska, Daira Viškere, Ilze Matīse-van Houtana, Blaž Cugmas. 2023. "Wide-field Raman spectral band imaging of tumor lesions in veterinary medicine" Translational Biophotonics: Diagnostics and Therapeutics III, 12627: pp. 295-302. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12627/1262737/Wide-field-Raman-spectral-band-imaging-of-tumor-lesions-in/10.1117/12.2686917.short
- Valērija Movčana, Arnis Strods, Karīna Narbute, Fēlikss Rūmnieks, Roberts Rimša, Gatis Mozoļevskis, Maksims Ivanovs, Roberts Kadiķis, Kārlis Gustavs Zviedris, Laura Leja, Anastasija Zujeva, Tamāra Laimiņa, Arturs Abols. "Organ-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation" Data , 9(2), pp.28. https://doi.org/10.5281/zenodo.10203721
- Blaz Cugmas, Eva Štruc, Inese Bērziņa, Mindaugas Tamošiūnas, Laura Goldberga, Thierry Olivry, Kārlis Zviedris, Roberts Kadiķis, Maksims Ivanovs, Miran Bürmen, Peter Naglič. "Automated classification of pollens relevant to veterinary medicine" 2024 IEEE 14th International Conference Nanomaterials: Applications & Properties (NAP) https://ieeexplore.ieee.org/document/10739713
- warn Singh Warshaneyan, Maksims Ivanovs, Blaž Cugmas, Inese Bērziņa, Laura Goldberga, Mindaugas Tamosiunas, Roberts Kadiķis. 2025. Automated Pollen Recognition in Optical and Holographic Microscopy Images. 1(1). https://ieeexplore.ieee.org/document/11064260
- Tamošiūnas Mindaugas, Maciulevičius, Martynas, Maļiks Romans, Dupļevska Diāna Viškere Daira, Matīse-van Houtana Ilze, Kadiķis Roberts, Cugmas Blaž, Raišutis Renaldas. 2025. Raman spectral band imaging for the diagnostics and classification of canine and feline cutaneous tumors. Veterinary Quarterly, 45(1): pp.1–17. https://www.tandfonline.com/doi/full/10.1080/01652176.2025.2486771?src=
- Daira Viškere, Mindaugas Tamošiūnas, Romans Maļiks, Diāna Dupļevska, Ilze Matīse-van Houtana, Roberts Kadiķis, Blaž Cugmas. 2025. Virtual Staining From Optical Coherence Tomography to Hematoxylin and Eosin Stained Skin Tumor Samples in Pets.
- Simkuns Arturs, Saltanovs Rodions, Ivanovs Maksims, Kadiķis Roberts. Deep Learning-Emerged Grid Cells-Based Bio-Inspired Navigation in Robotics. 2025. Deep Learning-Emerged Grid Cells-Based Bio-Inspired Navigation in Robotics. Sensors, 25(5). https://www.scopus.com/inward/record.uri?eid=2-s2.0-86000607914&doi=10.3390%2Fs25051576&partnerID=40&md5=9f00c1e98522d8c10d0dc50798f3628e