Education 🎓📚

Jeon is a “triple alumnus” of Kyungnam University, Changwon. He earned his B.S. in Computer Engineering (2009‑2016) with early research on adaptive fuzzy binarization, followed by an M.S. (2016‑2018) focused on trade‑off and internal‑ensemble strategies for semantic segmentation 🖥️. He then pursued a Ph.D. (2018‑2022), crafting a dissertation that models long‑term potentiation to segment depth images and detect objects of similar color—melding neuroscience inspiration with machine learning innovation 🧠🤖.

Professional Development 🚀

Since March 2022, Jeon has served as a postdoctoral fellow and lecturer in the Robot Vision Laboratory, spearheading the Smart Manufacturing ICT Project while guiding student capstones. Earlier stints as head teaching assistant saw him deliver TensorFlow tutorials and assist AI courses, cementing his reputation as an engaging educator 🎤. Beyond academia, he collaborates with industry partners on predictive‑maintenance apps, VR systems for rural migrants, and real‑time edge‑AI pipelines that judge vehicle loads or monitor cucumber diseases 🥒. His toolbox spans C/C++/C#, Java, Python, web stacks, and the full deep‑learning triad—TensorFlow, PyTorch, and Keras. Active on GitHub and ResearchGate, he shares reproducible code, datasets, and pre‑prints, nurturing an open‑science ethos 🌐. Multilingual in English and Japanese 🗣️, Jeon frequently presents at ICEIC, iFUZZY, and ICAIIC, expanding his global network while refining proposals for smart‑factory funding and cross‑disciplinary AI initiatives.

Research Focus🔍

Jeon’s core pursuit is applied deep learning for intelligent perception. He designs lightweight CNNs, attention‑enhanced U‑Nets, and ViT hybrids to tackle semantic segmentation, object detection, and human‑pose estimation 👁️‍🗨️. Agriculture is a recurring theme: his Att‑NestedUNet and DM‑YOLOv8 series detect weeds, pests, and crop diseases in real time, promising pesticide reductions and yield gains 🌾. In remote sensing, he crafts small‑target detectors for KOMPSAT‑3A imagery and lightweight optical payloads 🛰️, aiding environmental monitoring. Parallel biomedical studies segment skin lesions and diagnose otologic disorders, while industrial projects predict tool wear and production‑line failures ⚙️. Jeon’s philosophy fuses domain knowledge with edge‑friendly AI, optimizing inference speed without sacrificing accuracy. He experiments with neuro‑inspired mechanisms—long‑term potentiation, multiple‑path feature aggregation—to push beyond conventional CNN limits, always validating on tough, imbalanced datasets. The unifying thread is robust, resource‑aware vision that moves from lab to field seamlessly.

Awards & Honors 🏅🎉

Jeon’s innovation has been repeatedly recognized. He clinched the 1st Prize in South Korea’s National R&D Real Challenge Program (2018) 🚀, followed by back‑to‑back Excellent Paper Awards from the Korea Institute of Intelligent Systems (2018, 2019) and a 2017 accolade from the Korea Institute of Information Scientists and Engineers 📜. His marine‑garbage detection and vegetation‑classification studies each earned KIIS honors, while his bronze prize at the Korea Institute of Information Technology (2022) celebrated advances in lightweight AI for smart factories 🏭. Collectively, these distinctions underscore both scholarly rigor and real‑world impact, reinforcing his stature as a rising star in applied computer vision ✨

Publication Top Notes

1. Plant Leaf Recognition Using a Convolution Neural Network

Authors: WS Jeon, SY Rhee
Journal: International Journal of Fuzzy Logic and Intelligent Systems, Vol. 17(1), pp. 26–34
Year: 2017
Citations: 215
Summary:
This study presents a convolutional neural network (CNN)-based method for the automatic recognition of plant leaves. The authors demonstrate that CNNs can effectively extract hierarchical features from leaf images, outperforming traditional machine learning techniques. The proposed model is tested on standard leaf image datasets, achieving high accuracy and robustness. This work significantly contributes to smart agriculture and plant species identification.

2. Fingerprint Pattern Classification Using Convolution Neural Network

Authors: WS Jeon, SY Rhee
Journal: International Journal of Fuzzy Logic and Intelligent Systems, Vol. 17(3), pp. 170–176
Year: 2017
Citations: 63
Summary:
The paper explores fingerprint pattern classification using CNNs to overcome limitations of feature-based techniques. The model successfully distinguishes between fingerprint classes (e.g., whorl, loop, arch), demonstrating the CNN’s capability in biometric recognition tasks. This application shows potential in security and authentication systems, offering a data-driven approach without relying heavily on handcrafted features.

3. Comparative Analysis of Generalized Intersection over Union and Error Matrix for Vegetation Cover Classification Assessment

Authors: H Choi, HJ Lee, HJ You, SY Rhee, WS Jeon
Journal: Sensors and Materials, Vol. 31(11), pp. 3849–3858
Year: 2019
Citations: 21
Summary:
This paper evaluates two metrics—Generalized Intersection over Union (GIoU) and Error Matrix—for assessing vegetation classification from remote sensing data. Using classification outputs of land cover maps, the study highlights how GIoU provides a more nuanced understanding of spatial accuracy compared to traditional methods. It aids in improving evaluation frameworks for environmental monitoring.

4. Analysis of Deep Learning Applicability for KOMPSAT-3A Satellite Image Classification

Authors: SY Rhee, WS Jeon, H Choi
Journal: Journal of the Korean Society for Geospatial Information Science, Vol. 26(4), pp. 69–76
Year: 2018
Citations: 9
Summary:
The research investigates the applicability of deep learning, particularly CNNs, in classifying high-resolution images from the KOMPSAT-3A satellite. The paper demonstrates how deep learning can enhance land use and land cover classification, suggesting improvements in geospatial data interpretation and practical applications in remote sensing.

5. Sugar Beets and Weed Detection Using Semantic Segmentation

Authors: XZ Hu, WS Jeon, SY Rhee
Conference: 2022 International Conference on Fuzzy Theory and Its Applications (iFUZZY), pp. 1–4
Year: 2022
Citations: 7
Summary:
This paper presents a semantic segmentation approach using deep learning to differentiate between sugar beets and weeds in field images. The approach applies a CNN-based segmentation model to pixel-wise classify crops and unwanted plants, facilitating precision agriculture and automated weeding systems. The study supports the development of smart farming technologies.

Conclusion

Dr. Wang‑Su Jeon stands out as a highly deserving recipient of the Best Researcher Award. His work not only advances the frontiers of computer vision and AI but also addresses pressing societal needs with practical, deployable solutions. His blend of academic brilliance, industry collaboration, and social responsibility defines a new generation of researcher-leaders. He is not just a researcher but a catalyst for technological change—both in South Korea and on the global stage.

Education 📚

🎓 Absur’s academic trajectory bridges engineering, data science, and computer science. He is presently pursuing a Ph.D. in Computer Science at the CUNY Graduate Center, New York City 🇺🇸, where he investigates edge‑centric machine‑learning systems under Prof. Saptarshi Debroy. Earlier, he completed an M.S. in Applied Statistics & Data Science at Jahangirnagar University, Bangladesh 🇧🇩, focusing on deep‑ and shallow‑learning methods for biomedical images. His foundational B.Sc. in Information & Communication Engineering from Bangladesh University of Professionals equipped him with solid algorithmic and networking fundamentals 📡.

Professional Development

🛠️ Professional growth is central to Md Nurul Absur’s journey. As a Graduate Research Assistant at the CUNY Graduate Center, he hones advanced experimentation skills in distributed edge platforms, shepherding projects from concept to peer‑reviewed publication. Parallel to that, Absur sharpens his pedagogical craft through repeated Teaching Assistantships and an Adjunct Lectureship at Hunter College 🏫, where he guides students through Operating Systems and Computer Architecture labs. His mentoring of undergraduate researcher Maximilian Jaramazovic and master’s candidate Akash Das demonstrates a strong coaching ethos 🤝. Prior to academia, Absur fortified his software‑engineering toolkit in the fintech sector, building secure, high‑availability financial systems for Standard Chartered Bank and IPDC Finance 💳. He also completed the Project Management Institute’s Agile Fundamentals training, enabling him to coordinate multidisciplinary teams with sprint‑based efficiency ⏱️. Regular attendance at IEEE workshops, travel‑grant‑funded conference presentations, and active reviewing/TPC duties keep his technical perspective fresh and globally connected 🌏, innovative 🚀.

Research Focus 

🔍 Absur’s research orbits the nexus of edge intelligence, multimodal interaction, and computer vision, targeting ultra‑low‑latency AI for resource‑constrained environments. He designs adaptive content‑delivery‑network architectures that dynamically balance throughput, energy, and fairness across distributed nodes 🌐. In parallel, his work on reliable 3‑D reconstruction leverages reinforcement‑learned camera selection and multi‑view stereo, paving the way for on‑device AR/VR experiences 🕶️. Biomedical AI remains a complementary pillar: he builds efficient CNNs for skin‑cancer and anomaly detection, integrating GAN‑based data augmentation to maximize accuracy with limited samples 🩺. Within IoT mobility, Absur exploits YOLOv8 and WiFi channel features to monitor road unevenness and static objects without Doppler shifts 🚗. Cross‑cutting all projects is an emphasis on interpretability, security, and real‑time guarantees, informed by earlier fintech experience protecting critical infrastructure 🔒. His goal is to democratize trustworthy machine learning by pushing cloud‑grade intelligence to the extreme network edge, from clinics to city streets 🌆.

Awards & Honors 🏆

🏅 Absur’s emerging excellence is already recognized by several distinctions. He holds a competitive Graduate Assistantship A at the CUNY Graduate Center and was selected for the prestigious IS‑Excellence Fellowship in his first semester 🎓. IEEE awarded him a travel grant to present his secure 3‑D reconstruction work at the 2024 Symposium on Edge Computing ✈️. Continuous invitations to serve as reviewer and TPC member for ICLR, IEEE Globecom, ISBI, and related conferences further underscore peer respect for his scholarship 🌟.

Publication Top Notes

📄 1. Revolutionizing Image Recognition: Next-Generation CNN Architectures for Handwritten Digits and Objects

• Authors: MN Absur, KFA Nasif, S Saha, SN Nova

• Published In: 2024 IEEE Symposium on Wireless Technology & Applications (ISWTA)

• Pages: 173–178

• Citations: 14

• Year: 2024

• Summary: Proposes advanced convolutional neural network architectures to significantly enhance accuracy in handwritten digit and object recognition tasks. Innovations include improved feature extraction and lightweight computation suitable for edge environments.

📄 2. Anomaly Detection in Biomedical Data and Images Using Various Shallow and Deep Learning Algorithms

• Author: MN Absur

• Published In: Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2021

• Pages: 45–58

• Citations: 12

• Year: 2022

• Summary: Focuses on the comparative evaluation of deep learning vs. traditional methods for anomaly detection in biomedical datasets, demonstrating the superiority of hybrid CNN-LSTM models in medical imaging contexts.

📄 3. Order Dependency in Sequential Correlation

• Authors: KFA Nasif, MN Absur, M Al Mamun

• Published In: 2019 3rd International Conference on Electrical, Computer, and Communication Engineering (ECCE)

• Citations: 12

• Year: 2019

• Summary: Analyzes time-series datasets with sequential correlation and proposes new methods for understanding order dependency in system logs and sensor data.

📄 4. Leveraging Deep Learning for Improved Sentiment Analysis in Natural Language Processing

• Authors: A Kulkarni, VSBH Gollavilli, Z Alsalami, MK Bhatia, S Jovanovska, MN Absur, et al.

• Published In: 2024 3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON)

• Citations: 3

• Year: 2024

• Summary: Employs transformers and hybrid deep neural networks for multilingual sentiment classification in social media texts, with implications for real-time opinion mining.

📄 5. Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

• Authors: MN Absur, S Saha, SN Nova, KFA Nasif, MRU Nasib

• Published In: 2025 International Conference on Computing, Networking and Communications (ICNC)

• Citations: 1

• Year: 2025

• Summary: Introduces an innovative multi-metric framework for content delivery networks (CDNs), improving latency, fault tolerance, and dynamic load balancing through edge AI and intelligent routing.

Conclusion

Md Nurul Absur is a rising force in next-generation AI research. His pioneering work in edge-centric intelligence and multimodal systems, combined with a demonstrated commitment to education, mentorship, and community service, position him as a strong and deserving candidate for the Best Researcher Award. His contributions are not only academically rigorous but socially and technologically transformative—making him a role model for emerging researchers worldwide.