Marko Panic | Inverse Imaging Problems | Best Researcher Award

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Dr. Marko Panic | Inverse Imaging Problems | Best Researcher Award

Senior Research Associate at BioSense Institute, Serbia📖

Dr. Marko Panić is a Senior Research Associate at the BioSense Institute, University of Novi Sad, Serbia. He specializes in statistical analysis of multi-sensor images with applications in biology, agriculture, environmental sciences, and healthcare. His work focuses on probabilistic graphical models and inverse imaging problems, with significant contributions to international and domestic research projects.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

Dr. Panić obtained his Bachelor’s and Master’s degrees in Electrical and Computer Engineering from the University of Novi Sad in 2009 and 2010, respectively. He earned his Ph.D. in Computer Science Engineering in 2020 under a joint program between the University of Novi Sad and Ghent University.

Professional Experience🌱

Dr. Panić has actively participated in numerous international projects, including HORIZON2020 initiatives such as ANTARES, agROBOfood, FLEXIGROBOTS, CYBELE, and DRAGON. He has also led and contributed to multiple domestic projects funded by the Serbian government and innovation agencies. As the leader of the Computer Vision research group at BioSense Institute, he supervises five Ph.D. students and has successfully collaborated on industry-focused AI-driven solutions. His team has achieved recognition in competitions like the Syngenta Crop Challenge and OpenCV challenges.

Research Interests🔬

Dr. Panić’s research focuses on computer vision, machine learning, hyperspectral imaging, medical imaging, and AI applications in agriculture, biology, and environmental science. His expertise includes Markov random field modeling, MRI reconstruction, and probabilistic graphical models.

Author Metrics
  • Scopus: 420 citations, h-index: 12
  • Google Scholar: 606 citations, h-index: 13

Awards & Honors

  • Awarded as a Distinguished Scientist (Top 10% in the category of scientific associates) in Technical and Technological Sciences.
  • Led the AITool4WYP project funded by the Innovation Fund.
  • Task leader on the BREATH project funded by the Science Fund.
  • Recognized for achievements in Syngenta Crop Challenge and OpenCV Challenges, securing finalist positions and top awards.
Publications Top Notes 📄

1. Automatic pollen recognition with the Rapid-E particle counter: The first-level procedure, experience, and next steps

Authors: I. Šaulienė, L. Šukienė, G. Daunys, G. Valiulis, L. Vaitkevičius, P. Matavulj, M. Panić, et al.
Journal: Atmospheric Measurement Techniques
Volume/Issue: 12(6)
Pages: 3435-3452
Year: 2019
Citations: 113
DOI: Link

2. A new low-cost portable multispectral optical device for precise plant status assessment

Authors: G. Kitić, A. Tagarakis, N. Cselyuszka, M. Panić, S. Birgermajer, D. Sakulski, et al.
Journal: Computers and Electronics in Agriculture
Volume: 162
Pages: 300-308
Year: 2019
Citations: 58
DOI: Link

3. Soybean varieties portfolio optimization based on yield prediction

Authors: O. Marko, S. Brdar, M. Panić, P. Lugonja, V. Crnojević
Journal: Computers and Electronics in Agriculture
Volume: 127
Pages: 467-474
Year: 2016
Citations: 47
DOI: Link

4. RealForAll: Real-time system for automatic detection of airborne pollen

Authors: D. Tešendić, D. Boberić Krstićev, P. Matavulj, S. Brdar, M. Panić, V. Minić, et al.
Journal: Enterprise Information Systems
Volume/Issue: 16(5)
Article ID: 1793391
Year: 2022
Citations: 38
DOI: Link

5. High temporal resolution of airborne Ambrosia pollen measurements above the source reveals emission characteristics

Authors: B. Šikoparija, G. Mimić, M. Panić, O. Marko, P. Radišić, T. Pejak-Šikoparija, et al.
Journal: Atmospheric Environment
Volume: 192
Pages: 13-23
Year: 2018
Citations: 37
DOI: Link

Conclusion

Dr. Marko Panić is a highly accomplished researcher with a strong background in Inverse Imaging Problems, computational vision, and AI applications in environmental science and agriculture. His leadership, publication impact, and project contributions make him an excellent candidate for the Best Researcher Award. Expanding his research into commercial AI applications and interdisciplinary collaborations could further solidify his standing as a global leader in computational imaging.

Changheun Hyun Oh – Medical Image Reconstruction- Best Researcher Award

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Changheun Hyun Oh – Medical Image Reconstruction

Changheun Hyun oh distinguished academic and researcher in the field Deep Learning based Medical Image Reconstruction. In terms of professional experience, he contributed to LG Electronics from 2017 to 2018. Subsequently, he joined the Neuroscience Research Institute of Gachon University in Fall 2018, where he has been actively involved in research endeavors. Throughout his educational and professional journey, he has demonstrated a commitment to advancing the field of Electrical Engineering and contributing to both academic and industrial domains.

 

🌐 Professional Profiles

Educations: 📚🎓

He earned his Ph.D. degree in Electrical Engineering from the Korea Advanced Institute of Science and Technology (KAIST) during the period of Fall 2011 to Fall 2020, under the guidance of Professor HyunWook Park in the Image Computing System Lab. Prior to that, he completed his M.S. degree in Electrical Engineering at KAIST from Fall 2009 to Spring 2011, continuing his research with Professor Park. His academic journey at KAIST began with a B.S. degree in Electrical Engineering, spanning from Spring 2005 to Spring 2009.

conferences

He has actively contributed to numerous international conferences, showcasing his research in the field of Electrical Engineering and Medical Imaging. In 2011, as the first author, he presented a paper on “A Switching Circuit for the Rx Signal in the MRI System” at the International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC). Subsequently, he continued to make significant contributions to conferences such as the International Forum on Medical Imaging in Asia and the Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM).

His research work encompasses diverse topics, including the optimization of RF birdcage coils, the development of an optimum RF shield for simultaneous MRI-PET systems, and innovations in MRI techniques like water-fat separation and diffusion-weighted imaging. He also explored applications beyond medical imaging, such as his involvement in the International Symposium on Electronic Art and the International Society for Music Information Retrieval Conference.

In 2018, he presented a paper on “ETER-net: End to End MR Image Reconstruction Using Recurrent Neural Network” at the First International Workshop on Machine Learning in Medical Image Reconstruction, held in conjunction with the International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI) 2018. His research continued to advance in 2020, where he discussed “A direct MR image reconstruction from k-space via End-To-End reconstruction network using recurrent neural network (ETER-net)” at the Annual Meeting of the ISMRM.

In 2022, as the first author, he presented another breakthrough with “A deep learning based direct mapping method for EPI image reconstruction” at the Annual Meeting of the ISMRM. His active participation in these conferences reflects his dedication to pushing the boundaries of knowledge in his field and applying cutting-edge technologies to advance medical imaging techniques.

 📚Top Noted Publication