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Mr. Md Nurul Absur | Computer Vision | Best Researcher Award

Doctoral Student at CUNY Graduate Center, United States.

Md Nurul Absur 🌐, a Bangladeshi‑born computer scientist and current Ph.D. candidate at the CUNY Graduate Center, New York 🇺🇸, is making waves in edge intelligence and multimodal machine learning. Guided by Prof. Saptarshi Debroy, he engineers fast, reliable 3‑D reconstruction pipelines and next‑generation CNNs for biomedical and IoT applications. Before moving stateside, Absur forged a diverse academic path, earning a B.Sc. in Information & Communication Engineering from Bangladesh University of Professionals and an M.S. in Applied Statistics & Data Science at Jahangirnagar University 🧑‍🎓. His industry stint as a financial‑systems developer for Standard Chartered Bank and IPDC Finance sharpened his distributed‑systems skills 💼. Absur’s research has already produced IEEE and Springer publications on CDN optimization, skin‑cancer detection, and edge‑based augmented‑reality surgery 🚀. Beyond the lab, he mentors budding technologists, reviews for flagship venues such as ICLR and IEEE Globecom, and champions open‑source collaboration 🤝, while balancing teaching duties at Hunter College with spirit 🏫.

Professional Profile:

Google Scholar

Suitability For Best Researcher Award – Mr. Md Nurul Absur

Md Nurul Absur stands out as an exceptional candidate for the Best Researcher Award based on his multidimensional contributions to edge intelligence, multimodal machine learning, and biomedical AI. With a solid academic background in engineering, data science, and computer science, he demonstrates a rare integration of theory, innovation, and practical deployment. His diverse experiences across academia, industry, and mentorship, along with a track record of high-impact publications, technical leadership, and global collaborations, make him highly deserving of this recognition.

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.

Md Nurul Absur | Computer Vision | Best Researcher Award

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