Danheng Gao | Deep Learning | Research Excellence Award

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Prof. Dr. Danheng Gao | Deep Learning | Research Excellence Award

Associate Researcher at Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences | China

Prof. Dr. Danheng Gao is a distinguished researcher at the Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, China, specializing in deep learning and its integration with advanced optical and photonic systems. His research bridges the disciplines of machine learning, surface-enhanced Raman spectroscopy (SERS), nonlinear optics, and ultrafast photonics, with a strong emphasis on intelligent data-driven strategies for real-world analytical applications. Prof. Gao has made notable contributions to the development of rapid identification and sensing technologies by combining artificial intelligence with spectroscopic techniques, significantly enhancing accuracy, speed, and automation in chemical and food analysis. His work in ultrafast photonics further explores the convergence of nonlinear optical phenomena with intelligent control systems, enabling breakthroughs in high-speed optical signal processing and precision measurement. Through high-impact publications in leading journals such as Food Chemistry, his research demonstrates strong interdisciplinary value across photonics, artificial intelligence, and applied chemistry. With growing citation impact, Prof. Gao is recognized for advancing intelligent optical sensing, machine-learning-driven spectroscopy, and next-generation photonic technologies.

Profile: Scopus

Featured Publications

  1. Gao, D., et al. (2025). A rapid wine brand identification method based on the joint application of SERS and machine learning techniques.

  2. Gao, D., et al. (2025). Advancements in ultrafast photonics: Confluence of nonlinear optics and intelligent strategies.
    Citation Count: 6

Bhavesh Kataria | AI and Machine Learning | AI & Machine Learning Award

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Dr. Bhavesh Kataria | AI and Machine Learning | AI & Machine Learning Award

Post-Doctoral Fellow at Emory University | United States

Dr. Bhavesh Kataria is a highly accomplished academician, researcher, and innovator in Computer Engineering, recognized globally for his leadership in Artificial Intelligence, Machine Learning, and Digital Image Processing. His professional journey spans academia and research institutions across India and the United States, including his role at Emory University, where he contributes to advanced AI-driven healthcare analytics and digital pathology solutions. With a Ph.D. focused on Optical Character Recognition of Sanskrit Manuscripts using Convolutional Neural Networks, Dr. Kataria has combined technical precision with deep domain expertise to address challenges in multilingual text recognition and medical imaging. His scholarly portfolio includes numerous publications in reputed international journals, multiple granted patents, and several authored books covering cutting-edge topics in AI, cloud computing, and web technologies. An active member of prestigious organizations such as IEEE and ACM, he serves on editorial boards of international journals and as a reviewer for globally recognized publishers like Springer Nature and Science Publishing Group. He has also chaired sessions and reviewed Ph.D. theses, contributing significantly to the academic ecosystem. Dr. Kataria’s pioneering innovations, such as AI-based network visualization tools, smart teaching devices, and healthcare monitoring systems, underscore his commitment to translational research and practical AI applications. Honored with awards including the Best Researcher Award and Teaching Excellence Award, he exemplifies a blend of scholarly excellence, innovation, and mentorship. His dedication to advancing intelligent systems and promoting interdisciplinary research continues to inspire global collaboration in emerging computational technologies.

Profiles: Scopus | ORCID

Featured Publications

Kataria, B., & Jethva, H. B. (2024, September 30). Decentralized security mechanisms for AI-driven wireless networks: Integrating blockchain and federated learning.

Kataria, B. (2024, June 2). Automated detection of tuberculosis using deep learning algorithms on chest X-rays.

Shivadekar, S., Kataria, B., Hundekari, S., Wanjale, K., Balpande, V. P., & Suryawanshi, R. (2023). Deep learning based image classification of lungs radiography for detecting COVID-19 using a deep CNN and ResNet 50.

Shivadekar, S., Kataria, B., Limkar, S., Wagh, K., Lavate, S., & Mulla, R. (2023, June 15). Design of an efficient multimodal engine for preemption and post-treatment recommendations for skin diseases via a deep learning-based hybrid bioinspired process.

Kataria, B., Jethva, H. B., Shinde, P. V., Banait, S. S., Shaikh, F., & Ajani, S. (2023, April 30). SLDEB: Design of a secure and lightweight dynamic encryption bio-inspired model for IoT networks.

Muhammad Muqeet Rehman | Neural Networks | Best Researcher Award

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Dr. Muhammad Muqeet Rehman | Neural Networks | Best Researcher Award

Brain Pool Fellow (Postdoc) at Jeju National University, South Korea

Dr. Muhammad Muqeet Rehman is a distinguished researcher and educator specializing in electronic and mechatronics engineering. His expertise spans the fabrication and characterization of triboelectric nanogenerators (TENGs) for self-powered sensing and biomedical applications. With a remarkable research record, Dr. Rehman has authored over 50 SCI research publications, boasting an H-index of 22 and approximately 1900 citations within a decade. His academic journey includes significant roles at Jeju National University (JNU), South Korea, and GIK Institute of Engineering Sciences and Technology, Pakistan. As a dedicated mentor and educator, he has supervised numerous PhD and MS students while leading impactful research projects in sustainable electronics and sensor technology.

Profile

Scopus

Education

Dr. Rehman pursued his PhD in Mechatronics Engineering from Jeju National University, South Korea, where he excelled in research on printed electronic devices, achieving a CGPA of 4.4/4.5. Prior to this, he completed his MS in Electronic Engineering at GIK Institute of Engineering Sciences and Technology, Pakistan, with a CGPA of 3.5/4.0, where he explored memristive devices. His undergraduate education in Electronic Engineering at GIK Institute provided a strong foundation in multidisciplinary engineering concepts. His academic journey has been marked by scholarships and awards for outstanding academic performance and research contributions.

Professional Experience

Dr. Rehman has held various prestigious positions, including Postdoctoral Researcher and Lecturer at Jeju National University under the National Research Foundation of South Korea. He has also served as a Brain Pool Fellow and Lecturer, contributing to groundbreaking research in nanogenerators and multifunctional sensors. Previously, as an Assistant Professor at GIK Institute, Pakistan, he played a pivotal role in engineering education and research. His experience includes managing funded research projects, mentoring graduate students, and collaborating with leading researchers globally to advance electronic and materials science technologies.

Research Interests

Dr. Rehman’s research interests encompass triboelectric nanogenerators (TENGs), self-powered multifunctional sensors, biocompatible electronics, and the application of advanced functional materials. His work also extends to flexible and printed electronics, sustainable energy solutions, and eco-friendly semiconductor devices. His interdisciplinary approach integrates materials science, electrical engineering, and biomedical applications, contributing to next-generation self-powered electronic systems and sensor technologies for healthcare and environmental monitoring.

Awards and Recognitions

Dr. Rehman has received multiple accolades for his contributions to research and academia. He is an approved PhD supervisor by the Higher Education Commission (HEC) of Pakistan and has successfully secured national and international research funding. His publications include several top-cited articles in materials science, with many ranked in the top 1% and top 10% of their respective fields. His innovative research in self-powered sensors and biocompatible materials has been recognized at high-profile international conferences and by funding agencies.

Selected Publications

Rehman M.M., Samad Y.A., Gul J., et al. “The Metamorphic Prospects of Graphene and other 2D Nanomaterials in the Adaptation of Memristors.” Progress in Materials Science, 2025. (Cited by: 50)

Iqbal S., Rehman M.M., Abbas Z., et al. “IoT-Driven Remote Patient Monitoring with a Flexible TENG Device Using Polymer-MOF Composites.” Energy & Environmental Materials, 2025. (Cited by: 30)

Saqib M., Rehman M.M., Khan M., et al. “Adaptable Self-Powered Humidity Sensor Based on a Sustainable Biowaste.” Sustainable Materials and Technologies, Under Review. (Cited by: 20)

Rehman M.M., Khan M., Rehman H.M.M., et al. “Sustainable and Flexible Carbon Paper-Based Multifunctional HMI Sensor.” Polymers, 2025. (Cited by: 25)

Ali K.S., Rehman M.M., Iqbal S., et al. “Wireless Flexi-Sensor Using Narrow Band Quasi-Colloidal 3D SnTe for Sensing Applications.” Chemical Engineering Journal, 2024. (Cited by: 40)

Zeb G.J., Cheema M.O., Din Z.M.U., et al. “Machine Learning-Based Classification of Body Imbalance Using Electromyogram.” Applied Sciences, 2024. (Cited by: 15)

Rahman S.A., Khan S.A., Iqbal S., et al. “Hierarchical Porous Biowaste-Based Dual Humidity/Pressure Sensor for Robotic Tactile Sensing.” Advanced Energy and Sustainability Research, 2024. (Cited by: 35)

Conclusion

Dr. Muhammad Muqeet Rehman is a prolific researcher and educator whose contributions to self-powered electronic systems and nanogenerator technology have significantly advanced the field. His expertise in sustainable and multifunctional sensing solutions has led to impactful discoveries and technological advancements. With a strong academic and research background, he continues to inspire and mentor future scientists while leading innovative research that bridges engineering, materials science, and biomedical applications.

Muyang Li | Deep learning | Best Researcher Award

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Mr Muyang Li | Deep learning | Best Researcher Award

Tianjin University,  China

Muyang Li is a dedicated researcher at Tianjin University, specializing in the integration of chemical engineering and data science. Currently pursuing his Master’s degree, he has already made significant contributions to the fields of crystallization process optimization, material property prediction, and AI-driven image analysis.

Profile:

🎓 Education:

  • M.S. in Chemical Engineering and Technology (2022–Present), Tianjin University
  • B.S. in Chemical Engineering and Technology (2018–2022), Tianjin University

🔬 Research Focus:

Muyang Li’s research bridges chemical engineering and computer vision, with notable contributions in:

  • Crystallization process optimization using AI and image segmentation.
  • Developing novel methodologies for virtual dataset synthesis and material property prediction.
  • Implementing deep learning techniques (e.g., CNNs, Transformers, YOLOv8) for enhanced industrial applications.

🏆 Achievements:

  • Authored 4 impactful publications in leading journals such as Powder Technology and Chemical Engineering Journal (2024).
  • Recipient of prestigious awards, including the Samsung Scholarship (2020) and First-Class Scholarship for Master Students (2022).
  • Recognized as an Excellent Graduate of Tianjin University (2022).

🧪 Key Research Contributions:

  • Developed frameworks for optimizing crystallization processes via image and data enhancement strategies.
  • Pioneered methods for synthesizing virtual datasets using advanced neural networks like CoCosNet.
  • Advanced deep-learning applications for material properties prediction and dynamic emulsion analysis.

With his innovative approach and interdisciplinary expertise, Muyang Li is making significant strides in integrating chemical engineering with cutting-edge AI technologies.

Publication Top Notes:

1. Enhanced Powder Characteristics of Succinic Acid through Crystallization Techniques for Food Industry Application

  • Authors: Hutagaol, T.J., Liu, J., Li, M., Gao, Z., Gong, J.
  • Journal: Journal of Food Engineering
  • Year: 2025, Volume: 388, Article: 112376
  • Focus: Improved powder properties of succinic acid via advanced crystallization techniques tailored for food industry applications.
  • Citations: 0

2. Modeling and Validation of Multi-Objective Optimization for Mixed Xylene Hybrid Distillation/Crystallization Process

  • Authors: Chen, W., Yao, T., Liu, J., Gao, Z., Gong, J.
  • Journal: Separation and Purification Technology
  • Year: 2025, Volume: 354, Article: 128778
  • Focus: Multi-objective optimization model validation for hybrid distillation/crystallization in mixed xylene processing.
  • Citations: 0

3. A Deep Learning-Powered Intelligent Microdroplet Analysis Workflow for In-Situ Monitoring and Evaluation of a Dynamic Emulsion

  • Authors: Liu, J., Li, M., Cai, J., Gao, Z., Gong, J.
  • Journal: Chemical Engineering Journal
  • Year: 2024, Volume: 499, Article: 155927
  • Focus: Advanced deep-learning workflows for real-time dynamic emulsion monitoring.
  • Citations: 0

4. Predicting Crystalline Material Properties with AI: Bridging Molecular to Particle Scales

  • Authors: Chen, W., Li, M., Yao, T., Gao, Z., Gong, J.
  • Journal: Industrial and Engineering Chemistry Research
  • Year: 2024, Volume: 63(43), pp. 18241–18262
  • Type: Review
  • Focus: Utilizing AI for predicting crystalline material properties from molecular to particle scales.
  • Citations: 0

5. Experiment of Simulation Study on Gas-Solid Fluidization in Martian Environments

  • Authors: Ma, Y., Li, M., Ma, Z., Zhang, L., Liu, M.
  • Journal: Huagong Jinzhan/Chemical Industry and Engineering Progress
  • Year: 2024, Volume: 43(8), pp. 4203–4209
  • Focus: Simulation studies of gas-solid fluidization under Martian environmental conditions.
  • Citations: 0

6. Deep-Learning Based In-Situ Micrograph Analysis of High-Density Crystallization Slurry Using Image and Data Enhancement Strategy

  • Authors: Li, M., Liu, J., Yao, T., Gao, Z., Gong, J.
  • Journal: Powder Technology
  • Year: 2024, Volume: 437, Article: 119582
  • Focus: Application of deep-learning techniques for analyzing high-density crystallization slurry micrographs.
  • Citations: 2