Jianglong Yang | Artificial Intelligence | Best Researcher Award

Posted on by sciencefather

Dr. Jianglong Yang | Artificial Intelligence | Best Researcher Award

Deputy Director of Collaborative Innovation Center at Beijing Wuzi University, China

Dr. Jianglong Yang is a prominent scholar in the field of logistics and supply chain management, currently serving at Beijing Wuzi University. With a Doctorate in Management, Dr. Yang has developed an outstanding portfolio of interdisciplinary research focused on intelligent logistics systems, optimization of e-commerce warehousing, and green supply chain innovations. His scholarly contributions include top-tier SCI and CSSCI publications, project leadership roles in national and municipal research programs, and a growing presence in China’s logistics innovation and policymaking circles.

Publication Profile 

Scopus

Educational Background 🎓

  • Degree: Doctor of Management

  • Major: Logistics and Supply Chain Management

  • Institution: Not explicitly stated, but affiliated with major universities like Beijing Wuzi University and Beijing University of Technology.

Professional Experience 💼

Dr. Jianglong Yang has held a series of prominent academic and leadership roles across China’s logistics and information management sectors. Since June 2024, he has been serving as the Party Secretary of the Information Management Teachers’ Party Branch at the School of Information, Beijing University of Technology, where he contributes to academic leadership and institutional governance. In August 2022, he was appointed Deputy Director of the Beijing Intelligent Logistics System Collaborative Innovation Center, a position that places him at the forefront of logistics innovation and policy research. He also currently serves as the Deputy Secretary-General of the Beijing System Engineering Society (since March 2024), reflecting his influence in advancing system-level thinking within logistics and engineering domains. In addition, Dr. Yang became an Assistant Researcher at the China Logistics Group Science and Technology Research Institute in June 2023, where he engages in cutting-edge research on national logistics strategies. Most recently, in May 2024, he was appointed Director of the Expert Committee of Beijing Huixu Technology Co., Ltd., further expanding his influence in industry-academic integration and application of intelligent logistics technologies.

Research Interests 🔬

  • Intelligent logistics systems

  • E-commerce packaging and warehousing optimization

  • Genetic algorithms and spatial modeling for logistics

  • Multimodal transportation under the Belt and Road Initiative

  • Green and circular economy in logistics

  • Smart supply chain coordination and service quality improvement

Awards and Honors🏆✨

  1. Outstanding Doctoral Dissertation Award, China Logistics Society Annual Conference (2024)

  2. First Prize, China Logistics Academic Annual Conference (2021) – Global Supply Chain Restructuring Research

  3. Research Awards, China Logistics Society & China Federation of Logistics and Purchasing (2021) – First and Third Prizes

  4. First-Class Doctoral Academic Scholarships, Two consecutive years (2019–2021)

Conclusion🌟

Dr. Jianglong Yang exemplifies the qualities of a leading researcher through his consistent output of high-impact publications, strategic roles in academic and industry organizations, and project leadership in pioneering logistics research. His work contributes significantly to the modernization of China’s intelligent logistics systems, with practical implications for sustainable e-commerce and supply chain management. His research excellence and active participation in the advancement of logistics innovation make him a strong candidate for competitive research awards and international collaborations.

Publications 📚

  • 📘 Monograph

    • Yang Jianglong, Liu Huwei, Zhou Li. Research on Intelligent Optimization of E-commerce Warehousing Packing Decision-making Based on Data Driven. Capital University of Economics and Business Press, Sept. 2023.
      (Academic monograph, 440,000 words – First Author)

  • 🧠 Journal Article

    • Yang Jianglong, Shan Man, Liang Kaibo, et al. “Research on intelligent decision-making of e-commerce three-dimensional packing based on spatial particle model.” Frontiers of Engineering Management Science and Technology, 2024, 43(06): 41–48.
      (First Author, CSSCI Core, A-level Journal)

  • 🧬 Algorithm & AI

    • Yang J, Liu H, Liang K, et al. “Variable neighborhood genetic algorithm for multi-order multi-bin open packing optimization.” Applied Soft Computing, 2024: 111890.
      (SCI Zone 1 TOP, First Author)

  • 🤖 AI in Logistics

    • Yang J, Liu H, Liang K, et al. “A Genetic Algorithm with Lower Neighborhood Search for the Three‐Dimensional Multiorder Open‐Size Rectangular Packing Problem.” International Journal of Intelligent Systems, 2024(1): 4456261.
      (SCI Zone II TOP, First Author)

  • 📦 E-commerce Optimization

    • Yang J, Liang K, Liu H, et al. “Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system.” PeerJ Computer Science, 2023, 9: e1613.
      (SCI Zone 4, First Author)

  • 🚨 Emergency Logistics

    • Liu Huwei, Zhou Li, Yang Jianglong*. “Research on hierarchical collaborative distribution of emergency materials under sudden public events.” Journal of Engineering Mathematics, 2024, 41(01): 53–66.
      (CSCD Core Journal, Corresponding Author)

  • 🚆 Multimodal Transport Policy

    • Yu Lin, Yang Jianglong. “Problems and policy recommendations for the development of multimodal transport under the ‘Belt and Road’ strategy.” SASAC Research Center, Oct. 2023.
      (Think Tank Report, Second Author)

  • 📡 Smart Picking Systems

    • Zhou Li, Yang Jianglong*. “Research on multi-channel intensive mobile shelf order picking based on genetic algorithm.” Operations Research and Management, 2021, 30(2):7.
      (CSCD Core Journal, Corresponding Author)

  • 🧮 Batch Order Optimization

    • Yang J, Zhou L, Liu H. “Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse.” PLOS ONE, 2021, 16.
      (SCI Zone 2, First Author)

  • 🔐 Security in IoT

    • Yang J, Yang W, Liu H, et al. “Design and Simulation of Lightweight Identity Authentication Mechanism in Body Area Network.” Security and Communication Networks, 2021(3):1–18.
      (SCI Zone 4, First Author)

 

 

 

 

Francisco Mena | Machine Learning | Best Researcher Award

Posted on by sciencefather

Mr. Francisco Mena | Machine Learning | Best Researcher Award

PhD Candidate at University of Kaiserslautern-Landau, Germany

Francisco Mena is a PhD candidate in Computer Science at the University of Kaiserslautern-Landau (RPTU), Germany, with a strong academic and research background in deep learning, multi-view learning, and unsupervised learning. His work focuses on developing scalable and generalizable machine learning models, particularly in complex real-world domains like Earth observation and astroinformatics, where missing data and multi-source fusion are major challenges. Francisco’s research emphasizes minimizing human intervention and domain dependency, aiming for methods that are more robust, adaptable, and explainable.

Publication Profile 

Orcid

Educational Background 🎓

  • PhD in Computer Science
    University of Kaiserslautern-Landau (RPTU), Germany
    Jan. 2022 – Present
    Thesis: Data Fusion in Multi-view Learning for Earth Observation Applications with Missing Views

  • Magíster en Ciencias de la Ingeniería Informática (Equivalent to M.Sc. in Computer Engineering)
    Federico Santa María Technical University (UTFSM), Valparaíso, Chile
    Mar. 2018 – Sep. 2020
    Thesis: Mixture Models for Learning in Crowdsourcing Scenarios
    GPA: 94%

  • Ingeniería Civil en Informática (Equivalent to Computer Engineering)
    UTFSM, Santiago, Chile
    Mar. 2013 – Sep. 2020
    GPA: 80% | Rank: Top 10% – 4th of 66 students

  • Licenciado en Ciencias de la Ingeniería Informática
    UTFSM, Santiago, Chile
    Mar. 2013 – Nov. 2017

  • High School
    New Little College, Santiago, Chile
    Mar. 2008 – Dec. 2012

Professional Experience 💼

  • Student Research Assistant – German Research Centre for Artificial Intelligence (DFKI), Germany
    Mar. 2022 – Present
    Working on Earth observation data for crop yield prediction using Python, QGIS, and Slurm.

  • Lecturer – University of Kaiserslautern-Landau (RPTU), Germany
    Oct. 2024 – Apr. 2025
    Teaching: Machine Learning for Earth Observation within a broader Data Science course.

  • Visiting PhD Researcher – Inria Montpellier, France
    Nov. 2024 – Jan. 2025
    Research in multi-modal co-learning, mutual distillation, and multi-task learning.

  • Academic Roles – Federico Santa María Technical University (UTFSM), Chile
    2014 – 2021
    Lecturer & Assistant roles in:

    • Computational Statistics

    • Artificial Neural Networks

    • Machine Learning

    • Operations Research

    • Mathematics Lab

  • Research Assistant – Chilean Virtual Observatory (ChiVO)
    Jul. 2017 – May 2018
    Astroinformatics projects involving ALMA/ESO datasets and Python-based data reduction.

  • Developer Intern – Farmacia Las Rosas S.A., Chile
    Jan. 2017 – Mar. 2017
    Desktop software automation using Python and QT.

Research Interests 🔬

  • Machine Learning Foundations:
    Deep Learning, Variational Autoencoders, Neural Networks, Representation Learning, Deep Clustering

  • Methodologies:
    Multi-view Learning, Data Fusion, Latent Variable Modeling, Dimensionality Reduction, Unsupervised Learning

  • Applications:
    Earth Observation, Remote Sensing, Vegetation Monitoring, Crowdsourcing, Neural Information Retrieval, Astroinformatics

Awards and Honors🏆✨

  • PhD Scholarship – RPTU, Germany (2022–present)

  • Scientific Initiation Award (PIIC) – UTFSM, Chile (2019–2020)

  • Master Program Scholarship – UTFSM, Chile (2018–2020)

  • Honor Roll – UTFSM, Chile (2013)

Conclusion🌟

Francisco Mena is a dedicated machine learning researcher whose work blends theoretical rigor with impactful real-world applications. His interdisciplinary approach spans remote sensing, astroinformatics, and crowdsourcing, focusing on creating models that are resilient to missing data, efficient at scale, and minimally reliant on labeled supervision. With a growing publication record, international experience, and teaching background, he is well-positioned to make significant contributions to both academia and applied AI research.

Publications 📚

  1. 📄 Missing data as augmentation in the Earth Observation domain: A multi-view learning approach
    Neurocomputing, 2025-07
    DOI: 10.1016/j.neucom.2025.130175
    👥 Francisco Mena, Diego Arenas, Andreas Dengel

  2. 🌾 Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction
    Remote Sensing of Environment, 2025-03
    DOI: 10.1016/j.rse.2024.114547
    👥 Francisco Mena et al.

  3. 🛰️ Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications
    IEEE JSTARS, 2024
    DOI: 10.1109/JSTARS.2024.3361556
    👥 Francisco Mena, Diego Arenas, Marlon Nuske, Andreas Dengel

  4. 📉 Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
    IGARSS Proceedings, 2024
    DOI: 10.1109/IGARSS53475.2024.10640375
    👥 Francisco Mena et al.

  5. 🛰️ Assessment of Sentinel-2 Spatial and Temporal Coverage Based on the Scene Classification Layer
    IGARSS 2024, 2024-07-07
    DOI: 10.1109/igarss53475.2024.10642213
    👥 Cristhian Sanchez, Francisco Mena et al.

  6. 🌽 Crop Yield Prediction: An Operational Approach to Crop Yield Modeling on Field and Subfield Level with ML Models
    IGARSS 2023
    DOI: 10.1109/IGARSS52108.2023.10283302
    👥 Francisco Mena et al.

  7. 🧩 Feature Attribution Methods for Multivariate Time-Series Explainability in Remote Sensing
    IGARSS 2023
    DOI: 10.1109/IGARSS52108.2023.10282120
    👥 Francisco Mena et al.

  8. 🧹 Influence of Data Cleaning Techniques on Sub-Field Yield Predictions
    IGARSS 2023
    DOI: 10.1109/IGARSS52108.2023.10282955
    👥 Francisco Mena et al.

  9. 🗂️ A Comparative Assessment of Multi-View Fusion Learning For Crop Classification
    IGARSS 2023, 2023-07-16
    DOI: 10.1109/igarss52108.2023.10282138
    👥 Francisco Mena, Diego Arenas, Marlon Nuske, Andreas Dengel

  10. 📊 Predicting Crop Yield with Machine Learning: Input Modalities and Models on Field and Sub-Field Level
    IGARSS 2023, 2023-07-16
    DOI: 10.1109/igarss52108.2023.10282318
    👥 Francisco Mena et al.

Muhammad Kashif Jabbar | Artificial Intelligence | Best Researcher Award

Posted on by sciencefather

Mr. Muhammad Kashif Jabbar | Artificial Intelligence | Best Researcher Award

Doctor Student at Shenzhen University, China

Muhammad Kashif Jabbar is a research-focused professional specializing in medical image processing. With a strong foundation in Electronics and Information Engineering, he has contributed significantly to research, particularly in developing transfer learning-based models for diabetic retinopathy diagnosis. Muhammad Kashif is multilingual, skilled in technical domains, and experienced in international collaborations.

Publication Profile 

Scopus

Educational Background 🎓

  1. Shenzhen University
    • Degree: Ph.D. in Electronics and Information Engineering
    • Specialization: Medical Image Processing
    • Session: September 2018 – June 2022
  2. Beijing University of Technology (BJUT)
    • Degree: Master’s in Information and Communication Engineering
    • Specialization: Medical Image Processing
    • Session: September 2018 – June 2022
  3. Superior University of Lahore
    • Degree: Master’s in Information Technology (MIT)
    • Session: 2014 – 2016

Professional Experience 💼

  • Worked extensively on developing advanced methodologies in medical image processing.
  • Conducted research focusing on diabetic retinopathy diagnosis, utilizing transfer learning techniques.
  • Developed applications in web development and database management.

Research Interests 🔬

  • Medical Image Processing
  • Transfer Learning for Disease Diagnosis
  • Data Security in Medical Imaging (Steganography and Cryptography)
  • Artificial Intelligence and Optimization Algorithms in Healthcare Applications

Awards and Honors🏆✨

  • Passed HSK4 Chinese Language Proficiency Exam (2018).
  • Performed at the 14th BJUT International Day opening ceremony.
  • Recognized for successful completion of the 2019 International Students Exploring Haidian program.

Certifications

  1. HSK4 Chinese Language Certification – Beijing University of Technology
  2. Graphic Design – ARENA Multimedia, Islamabad Campus (2015)

Conclusion🌟

Muhammad Kashif Jabbar is a highly skilled researcher with a passion for advancing medical technologies using artificial intelligence and image processing techniques. His education and expertise make him a valuable asset to organizations focused on cutting-edge medical research and innovation.

Publications 📚

📡 Radar and Engineering

  1. Enhancing Radar Tracking Accuracy Using Combined Hilbert Transform and Proximal Gradient Methods
    • Authors: Jabbar, A., Jabbar, M.K., Jabbar, A., Mahmood, T., Rehman, A.
    • Journal: Results in Engineering, 2024, 24, 103479.
    • 🌐 Type: Article (Open Access)
    • 📊 Citations: 0

👁️ Ophthalmology and AI

  1. A Retinal Detachment Based Strabismus Detection Through FEDCNN
    • Authors: Jabbar, A., Jabbar, M.K., Mahmood, T., Nobanee, H., Rehman, A.
    • Journal: Scientific Reports, 2024, 14(1), 23255.
    • 🌐 Type: Article (Open Access)
    • 📊 Citations: 0

🔄 Errata and Corrections

  1. Correction to: Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas
    • Authors: Jabbar, A., Naseem, S., Li, J., Rehman, A., Saba, T.
    • Journal: International Journal of Computational Intelligence Systems, 2024, 17(1), 145.
    • 🌐 Type: Erratum (Open Access)
    • 📊 Citations: 1

  2. Correction to: Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images

    • Authors: Jabbar, M.K., Yan, J., Xu, H., Ur Rehman, Z., Jabbar, A.
    • Journal: Brain Sciences, 2024, 14(8), 777.
    • 🌐 Type: Erratum (Open Access)
    • 📊 Citations: 0

🧠 Diabetic Retinopathy and AI Models

  1. Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images
    • Authors: Jabbar, M.K., Yan, J., Xu, H., Rehman, Z.U., Jabbar, A.
    • Journal: Brain Sciences, 2022, 12(5), 535.
    • 🌐 Type: Article (Open Access)
    • 📊 Citations: 49

 

 

 

Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

Posted on by sciencefather

Mr. Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

PhD Student at Politecnico di Torino, Italy

Rafael Natalio Fontana Crespo is a dedicated and sociable Ph.D. student specializing in Computer and Control Engineering at Politecnico di Torino. With a strong academic background in mechatronics and practical experience in electrical energy analysis, he is passionate about tackling complex challenges through innovative solutions. 🌐💡

Publication Profile : 

Orcid

 

🎓 Educational Background :

Rafael is currently pursuing a Ph.D. in Computer and Control Engineering at Politecnico di Torino, Italy, since May 2022. He previously obtained a Master’s Degree in Mechatronic Engineering from the same institution, graduating with 110/110 cum laude in July 2022. His master’s thesis focused on designing and developing a distributed software platform for additive manufacturing. Rafael studied Electromechanical Engineering at the Universidad Nacional de Córdoba, Argentina, where he also completed a double degree program.

💼 Professional Experience :

Rafael gained practical experience during his internship at EPEC (Empresa Provincial de Energía de Córdoba) in Argentina, where he worked in the Statistics and Technical Department from May 2020 to May 2021. He was involved in analyzing thermal images of electrical components to prevent failures, contributing to the overall safety and efficiency of electrical systems.

📚 Research Interests : 

Rafael’s research interests lie at the intersection of computer engineering, control systems, and mechatronics, particularly focusing on additive manufacturing, machine learning applications in energy systems, and the optimization of neural networks.

📝 Publication Top Notes :

      1. Fontana Crespo, R.N., E. Patti, S. Di Cataldo, D. Cannizzaro. (2022). Design and Development of a Distributed Software Platform for Additive Manufacturing. Master’s Thesis, Politecnico di Torino.
      2. Fontana Crespo, R.N. (2023). Machine Learning in Energy Applications. Course Exam Paper, Politecnico di Torino.
      3. Fontana Crespo, R.N. (2023). IoT Platforms for Spatial Analytics in Smart Energy Systems. Course Exam Paper, Politecnico di Torino.
      4. Fontana Crespo, R.N. (2023). Optimized Execution of Neural Networks at the Edge. Course Exam Paper, Politecnico di Torino.
      5. Fontana Crespo, R.N. (2023). Adversarial Training of Neural Networks. Course Exam Paper, Politecnico di Torino.