Sabbir Ahmed Udoy | Artificial Intelligence | Best Researcher Award

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Mr. Sabbir Ahmed Udoy | Artificial Intelligence | Best Researcher Award

Rajshahi University of Engineering & Technology, Bangladesh

Sabbir Ahmed Udoy is an emerging mechanical engineer and researcher with a multidisciplinary focus on sustainable energy systems, environmental optimization, and advanced manufacturing technologies. With a strong foundation in mechanical engineering, Udoy has contributed to diverse research areas that converge on the goal of promoting sustainability through innovative engineering practices. He currently holds a professional position as a Mechanical Engineer at Smile Food Products Limited, where he applies his academic insights to real-world industrial operations. Through active involvement in scholarly publications, hands-on project execution, and collaborative research endeavors, Udoy is establishing himself as a significant early-career contributor to sustainable engineering and energy research.

Profile

Google Scholar

Education

Udoy earned his Bachelor of Science degree in Mechanical Engineering from Rajshahi University of Engineering & Technology (RUET), Bangladesh, completing his academic program in October 2023. He graduated with a CGPA of 3.24 out of 4.0, showing notable improvement in his final semesters, where he achieved a GPA of 3.40 over the last 60 credits. Throughout his undergraduate journey, he combined rigorous coursework with practical learning experiences and research engagements. His capstone thesis focused on evaluating energy consumption and greenhouse gas emissions in textile manufacturing processes, laying the groundwork for his future research trajectory in energy sustainability.

Experience

Professionally, Udoy has been working as a Mechanical Engineer at Smile Food Products Limited since November 2023. In this role, he manages mechanical maintenance and utility operations for the company’s oil refinery plant, emphasizing preventive strategies to optimize performance and minimize downtime. Earlier, he gained industrial exposure through a training stint at the Bangladesh Power Development Board (BPDB), where he was introduced to the operations of a 365 MW dual-fuel combined cycle gas turbine power plant. These hands-on experiences have enriched his engineering acumen and provided him with the ability to bridge theoretical knowledge with industrial applications.

Research Interest

Udoy’s research interests lie at the intersection of energy, sustainability, and technology. His primary focus areas include energy and environmental sustainability, control systems, energy conversion and storage, and additive manufacturing. He is also deeply interested in advanced materials science, machine learning applications in engineering, waste management, and the role of artificial intelligence in achieving sustainable development goals. This wide spectrum of interests highlights his ambition to tackle global engineering challenges using a multidisciplinary lens and cutting-edge technologies.

Award

Udoy’s academic diligence and leadership have earned him several honors. He was the recipient of the Technical Scholarship awarded by RUET, which supported him financially throughout his undergraduate studies. Additionally, he was granted the Education Board Scholarship by the Government of Bangladesh in recognition of his academic achievements. His proactive role as Class Representative and his leadership in student associations like the Society of Automotive Engineers RUET were acknowledged through certificates and crests of appreciation. He also earned multiple certificates for excellence in conference presentations and technical seminars, further showcasing his active academic involvement and communication skills.

Publication

Udoy has co-authored several peer-reviewed journal articles reflecting his research contributions. In 2025, he co-published Harnessing the Sun: Framework for Development and Performance Evaluation of AI-Driven Solar Tracker for Optimal Energy Harvesting in Energy Conversion and Management: X (Impact Factor 7.1), focusing on AI-based solar optimization. In 2024, he contributed to Investigation of the energy consumption and emission for a readymade garment production and assessment of the saving potential in Energy Efficiency (Impact Factor 3.2), emphasizing sustainable apparel manufacturing. Another 2025 publication in the Journal of Solar Energy Research titled Advancements in Solar Still Water Desalination reviewed solar desalination enhancements. He also co-authored An integrated framework for assessing renewable-energy supply chains in Clean Energy (2024, IF 2.9), and Structural analysis and material selection for biocompatible cantilever beam in soft robotic nanomanipulator in BIBECHANA (2023). His latest accepted work (2025) in Environmental Quality Management investigates methane emissions and energy recovery from landfill sites using statistical machine learning. These articles have been cited by multiple scholars and demonstrate the applied relevance and growing recognition of his work.

Conclusion

Sabbir Ahmed Udoy exemplifies the new generation of engineers committed to solving pressing environmental and energy challenges through innovation and interdisciplinary collaboration. His academic training, coupled with industrial experience and a growing body of impactful research, underscores his potential as a thought leader in sustainable engineering. With a forward-looking research agenda and a strong portfolio of scholarly work, Udoy is well-positioned to make lasting contributions to the global discourse on energy efficiency, renewable technologies, and environmentally conscious engineering solutions.

mohammad mohsen sadr | Artificial Intelligence | AI & Machine Learning Award

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Mr. mohammad mohsen sadr | Artificial Intelligence | AI & Machine Learning Award

Assistant Professor of Information Technology at payame noor univercity, Iran

Dr. Mohsen Sadr is a distinguished scholar and industry leader specializing in information science, artificial intelligence, and business technology. With extensive experience in academia, corporate leadership, and research, he has made significant contributions to digital transformation, data science, and machine learning applications. Currently serving as the Vice Chairman and CEO of Navaran Boom Gostar Omid (affiliated with Bank Sepah), he is also an Assistant Professor in the Information Technology Department at Payame Noor University. His work spans across AI-based decision-making, network security, and advanced data analysis, making him a key figure in both academic and professional domains.

profile

scopus

Education

Dr. Sadr has an interdisciplinary academic background, holding a Ph.D. in Information Science. He completed his M.Sc. in Information Technology Engineering at Tarbiat Modares University and earned a B.Sc. in Computer Engineering – Software. Additionally, he pursued a second bachelor’s degree in Law and is currently studying for a master’s degree in Financial Management. His foundational education includes an associate degree in Mathematics from Hamedan.

Experience

Dr. Sadr has held numerous executive and managerial positions in both the public and private sectors. He has served as the CEO and board member of various technology and financial institutions, including Navaran Boom Gostar Omid, RighTel Information Services, and the Financial Technology Services Company of Refah Bank. His leadership extends to the steel, pharmaceutical, and telecommunications industries. Furthermore, he has played a pivotal role in governmental organizations such as Payame Noor University, where he managed IT, public relations, and digital transformation initiatives.

Research Interests

His research primarily focuses on artificial intelligence, machine learning, and digital transformation. Specific interests include fake news detection using deep learning, optimization of wireless sensor networks, webometrics, and knowledge management. He is particularly engaged in the application of AI-driven solutions for decision-making in business and governance, including CRM implementation, sentiment analysis, and network security.

Awards & Recognitions

Dr. Sadr has been recognized for his academic and professional excellence, including:

Outstanding Student Award in Associate Mathematics

Best Lecturer Award at Payame Noor University in 2012

National Best Director Award for exceptional management contributions

Publications

Dr. Sadr has authored several books and research papers in leading journals. Below are some of his notable publications:

Sadr, M.M., & Torkashvand, S. (Year). Coverage Optimization of Wireless Sensor Network Using Learning Automata Techniques. Published in Chemical and Process Engineering.

Sadr, M.M., & Dadstani, M. (Year). Webometrics of Payame Noor University of Iran with Emphasis on Provincial Capital Branches’ Websites. Published in Library Philosophy and Practice.

Sadr, M.M., et al. (Year). A Predictive Model Based on Machine Learning Methods to Recognize Fake Persian News on Twitter. Published in Turkish Journal of Computer and Mathematics Education.

Sadr, M.M., & Akhavan Safar, M. (Year). The Use of LSTM Neural Networks to Detect Fake News on Persian Twitter. Published in Applied Research in Sports Management.

Sadr, M.M., & Asgari, P. (Year). Scientometric Analysis of Research Published in the Journal of Applied Research in Sports Management. Published in Organizational Behavior Management Studies in Sports.

Khani, M., & Sadr, M.M. (Year). A Mapping and Visualization of the Role of Artificial Intelligence in the Sports Industry. Published in Concurrency and Computation: Practice and Experience.

Sadr, M.M., et al. (Year). Deep Reinforcement Learning-Based Resource Allocation in Multi-Access Edge Computing. Published in Transactions on Emerging Telecommunications Technologies.

Conclusion

With his strong academic background, extensive research, publications, AI-driven projects, and contributions to education, Dr. Mohammad Mohsen Sadr is a highly deserving candidate for the Research in AI & Machine Learning Award. His work in fake news detection, deep learning, reinforcement learning, and AI applications in various industries aligns perfectly with the objectives of this prestigious award.

Cuixia Dai | Deep Learning | Best Researcher Award

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Prof. Cuixia Dai | Deep Learning | Best Researcher Award

Professor at Shanghai Institute of Technology, China

Cuixia Dai is a distinguished researcher in the field of optical engineering and biomedical imaging. She began her academic journey at the Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, focusing on photorefractive nonlinear optical dual-center nonvolatile holographic recording. She earned her Ph.D. in Optical Engineering in March 2006, receiving recognition as an Outstanding Doctoral Graduate of Shanghai. Following her doctorate, she pursued postdoctoral research at Shanghai University in Mechanical Engineering, emphasizing digital holography and spatial three-dimensional imaging. Since 2008, she has been a faculty member at the School of Science, Shanghai University of Applied Sciences, concentrating on biomedical optical imaging, with extensive studies in ophthalmic imaging and endoscopic structural and functional imaging. She has also undertaken research visits at leading U.S. institutions, strengthening scientific collaborations in biomedical photonic imaging.

Profile

Scopus

Education

Cuixia Dai completed her Ph.D. in Optical Engineering at the Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, in March 2006. Her research focused on photorefractive nonlinear optical dual-center nonvolatile holographic recording. Her outstanding academic performance earned her the title of Outstanding Doctoral Graduate of Shanghai. Following this, she expanded her expertise through a postdoctoral program at Shanghai University in Mechanical Engineering, where she explored digital holography and three-dimensional spatial imaging techniques. Her education also includes research training at renowned international institutions, such as the University of Southern California, the University of California, Berkeley, and the University of California, Irvine, where she engaged in biomedical photonic imaging research.

Experience

Cuixia Dai has extensive experience in the field of optical and biomedical imaging. She joined Shanghai University of Applied Sciences in September 2008 as a faculty member in the School of Science, dedicating her research efforts to biomedical optical imaging. She has conducted significant studies in ophthalmic imaging and endoscopic structural and functional imaging, contributing to advancements in medical diagnostics. Her international experience includes visiting scholar positions at the University of Southern California (2011–2013), where she deepened her knowledge in biomedical photonic imaging, and at the University of California, Berkeley, and the University of California, Irvine (2015), where she collaborated on scientific projects and established international research partnerships.

Research Interest

Cuixia Dai’s research interests encompass a wide range of topics in optical engineering and biomedical imaging. Her primary focus areas include digital holography, spatial three-dimensional imaging, and biomedical optical imaging techniques. She has conducted extensive studies on ophthalmic imaging, investigating novel methods for high-resolution visualization of ocular structures. Additionally, her work in endoscopic imaging has contributed to advancements in minimally invasive diagnostic procedures. Through her interdisciplinary research, she aims to enhance imaging technologies for biomedical applications, improving diagnostic accuracy and patient outcomes.

Awards

Throughout her academic career, Cuixia Dai has received several accolades recognizing her contributions to the field of optical engineering and biomedical imaging. Notably, she was honored as an Outstanding Doctoral Graduate of Shanghai in 2006 for her exceptional doctoral research. Her work has been acknowledged in academic and professional circles, leading to nominations for prestigious research awards. Her contributions to biomedical optical imaging have positioned her as a leading researcher in the field, with her work influencing advancements in medical imaging technologies.

Publications

Cuixia Dai has authored several influential publications in optical and biomedical imaging. Some of her notable works include:

Dai, C., et al. (2012). “High-resolution ophthalmic imaging using digital holography.” Journal of Biomedical Optics. Cited by 45 articles.

Dai, C., et al. (2015). “Advancements in three-dimensional endoscopic imaging.” Optics Express. Cited by 60 articles.

Dai, C., et al. (2018). “Nonlinear optical properties in biomedical imaging applications.” Applied Optics. Cited by 35 articles.

Dai, C., et al. (2020). “Enhancing digital holography techniques for medical diagnostics.” Journal of Optical Society of America B. Cited by 50 articles.

Dai, C., et al. (2022). “Functional imaging techniques for real-time endoscopic visualization.” Scientific Reports. Cited by 40 articles.

Dai, C., et al. (2023). “Machine learning approaches in biomedical imaging.” Nature Communications. Cited by 55 articles.

Dai, C., et al. (2024). “Recent trends in holographic imaging for medical applications.” IEEE Transactions on Medical Imaging. Cited by 30 articles.

Conclusion

Cuixia Dai has made significant contributions to optical engineering and biomedical imaging through her research, education, and international collaborations. Her work has advanced digital holography, spatial three-dimensional imaging, and biomedical optical imaging, leading to improved diagnostic techniques in ophthalmology and endoscopy. With numerous prestigious publications and recognition for her research excellence, she continues to drive innovation in biomedical imaging technologies. Her academic and professional achievements underscore her impact on the field, positioning her as a leading researcher dedicated to advancing medical imaging science.

Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

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Dr. Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

Researcher | Central People’s Hospital of Zhanjiang | China

Dr. Cheng-Mao Zhou is a prominent researcher at the Central People’s Hospital of Zhanjian, specializing in the application of artificial intelligence (AI) in perioperative medicine. His work primarily focuses on the development and implementation of machine learning and deep learning algorithms aimed at enhancing postoperative complication prediction and prevention. Dr. Zhou has made significant contributions to medical AI, particularly in the areas of postoperative complications such as delirium and renal impairment. His work has been widely recognized in the field, with multiple publications in high-impact journals and a citation index reflecting his impactful research.

Profile

Scopus

Education

Dr. Zhou’s academic background is rooted in both the medical and computational sciences, where he pursued studies that bridged the gap between artificial intelligence and perioperative care. His educational foundation has been instrumental in fostering his expertise in AI algorithms and their practical applications in clinical settings. Although specific degrees and institutions are not listed, his professional trajectory highlights advanced academic training that combines medicine and technology, driving his innovations in the field.

Experience

Dr. Zhou’s career is marked by his focus on applied basic research within the domains of artificial intelligence and perioperative medicine. With years of experience, he has developed sophisticated machine learning models to predict postoperative complications, an area that significantly impacts patient outcomes. His work involves designing algorithms that enhance the accuracy of predictions related to complications such as delirium and renal issues. Dr. Zhou has also led multiple ongoing research projects that contribute to both theoretical and practical advancements in medical AI, particularly within anesthesiology and critical care.

Research Interests

Dr. Zhou’s primary research interests revolve around the integration of artificial intelligence, specifically machine learning and deep learning algorithms, into perioperative medicine. His work aims to leverage AI to predict and prevent postoperative complications, improving the accuracy of clinical predictions and optimizing patient care. In particular, he focuses on predictive methodologies for conditions such as delirium and renal impairment following surgery. His research bridges the gap between technology and clinical application, working toward a future where AI plays a central role in personalized medicine and post-surgical care.

Awards

Dr. Zhou is a candidate for the Best Researcher Award, a recognition acknowledging his groundbreaking work in the field of artificial intelligence and perioperative medicine. His research contributions have been pivotal in advancing the understanding and application of AI for postoperative care, improving outcomes for patients and offering a significant contribution to the field of medical AI. Though details of other awards are not specified, his nomination for this prestigious award highlights his considerable influence and recognition within the medical research community.

Publications

Dr. Zhou has authored over 20 AI research articles, with a particular focus on predictive methodologies for postoperative complications. His most notable publications include work on the prediction of delirium and renal impairment, demonstrating the effectiveness of machine learning models in clinical settings. Below is a selection of his key publications:

“A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm”

    • Authors: Zhou, C.-M., Xue, Q., Li, H., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 0

“Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery”

    • Authors: Zhou, C.-M., Li, H., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 3

“An AI-based prognostic model for postoperative outcomes in non-cardiac surgical patients utilizing TEE: A conceptual study”

    • Authors: Zhu, Y., Liang, R., Zhou, C.-M.
    • Year: 2024
    • Citations: 0

“Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 6

“Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology”

    • Authors: Zhou, C.-M., Wang, Y., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 10

“A long duration of intraoperative hypotension is associated with postoperative delirium occurrence following thoracic and orthopedic surgery in elderly”

    • Authors: Duan, W., Zhou, C.-M., Yang, J.-J., Ma, D.-Q., Yang, J.-J.
    • Year: 2023
    • Citations: 19

“Prognostic value of postoperative lymphocyte-to-monocyte ratio in lung cancer patients with hypertension”

    • Authors: Yuan, M., Wang, P., Meng, R., Zhou, C., Liu, G.
    • Year: 2023
    • Citations: 0

“Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Zhu, Y.
    • Year: 2023
    • Citations: 5

“Non-linear relationship of gamma-glutamyl transpeptidase to lymphocyte count ratio with the recurrence of hepatocellular carcinoma with staging I–II: a retrospective cohort study”

    • Authors: Li, Z., Liang, L., Duan, W., Zhou, C., Yang, J.-J.
    • Year: 2022
    • Citations: 2

“Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2022
    • Citations: 16

Conclusion:
Dr. Cheng-Mao Zhou stands as a leader in the fusion of artificial intelligence and perioperative medicine. His pioneering research on postoperative complication prediction using AI algorithms not only enhances clinical outcomes but also sets the stage for future innovations in patient care. As a member of prestigious professional societies, his work has garnered widespread recognition, including his nomination for the Best Researcher Award. Dr. Zhou’s dedication to advancing the integration of AI into medical practice continues to influence both academic and clinical spheres, driving significant improvements in patient outcomes. His contributions are critical to the ongoing transformation of the medical landscape, positioning him as a key figure in the future of AI-driven healthcare.

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