Dr. Zhiwei Zhang | Deep Learning Awards | Best Researcher Award

Dr. Zhiwei Zhang | Deep Learning Awards | Best Researcher Award 

Dr. Zhiwei Zhang, AVIC Manufacturing Technology Institute, China

Zhiwei Zhang, is a research engineer specializing in aviation manufacturing technology in China. He holds a bachelor’s and master’s degree in Automation from Shenyang Ligong University and earned his Ph.D. in Instrument Science and Technology from Yanshan University. His research focuses on digital radiographic and industrial CT nondestructive testing, computer vision, and ensemble learning algorithms for additive manufacturing. He has published seven SCI-indexed research papers and holds two authorized patents. Zhiwei Zhang also serves as a reviewer for the Journal of Computational Methods in Sciences and Engineering, reflecting his active contribution to the academic and industrial research community.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award: Zhiwei Zhang

Zhiwei Zhang, a highly skilled research engineer in aviation manufacturing technology, has demonstrated outstanding contributions in the fields of nondestructive testing, computer vision, and ensemble learning for additive manufacturing. His innovative research integrates cutting-edge technologies like digital radiography, industrial CT, and machine learning, addressing critical challenges in the aerospace industry.

🎓 Education

  • 🏫 Bachelor’s Degree in Automation – Shenyang Ligong University

  • 🎓 Master’s Degree in Automation – Shenyang Ligong University

  • 🧪 Ph.D. in Instrument Science and Technology – Yanshan University

💼 Work Experience

  • 👨‍🔧 Research Engineer – Specializing in aviation manufacturing technology in China

  • 🔬 Focus areas include:

    • Digital radiographic and industrial CT nondestructive testing

    • Computer vision

    • Ensemble learning algorithms for additive manufacturing

🏆 Achievements

  • 📄 Published 7 SCI-indexed research papers in high-impact journals

  • 🧾 Granted 2 authorized patents

  • 🧑‍⚖️ Reviewer for the Journal of Computational Methods in Sciences and Engineering

🎖️ Awards & Honors

  • 🏅 Recognized for contributions in nondestructive testing and AI applications in manufacturing
    (Note: Specific award titles not mentioned; can be added if provided.)

Publication Top Notes:

A Hybrid Framework for Metal Artifact Suppression in CT Imaging of Metal Lattice Structures via Radon Transform and Attention-Based Super-Resolution Reconstruction

Complex Defects Detection of 3-D-Printed Lattice Structures: Accuracy and Scale Improvement in YOLO V7

A Prediction Model for Maximum Stress of Additive Manufacturing Lattice Structures Based on Voting-Cascading

Deep convolution IT2 fuzzy system with adaptive variable selection method for ultra-short-term wind speed prediction

An improved meta heuristic IT2 fuzzy model for nondestructive failure evaluation of metal additive manufacturing lattice structure

An improved stacking ensemble learning model for predicting the effect of lattice structure defects on yield stress

Data-driven XGBoost model for maximum stress prediction of additive manufactured lattice structures

Adaptive Defect Detection for 3-D Printed Lattice Structures Based on Improved Faster R-CNN

A Hybrid Model Based on Jensen’s Inequality Theory for 3D Printed Lattice Structures Maximum Stress Prediction

Dr. Minh-Khang Le | Artificial Intelligence Awards | Best Researcher Award

Dr. Minh-Khang Le | Artificial Intelligence Awards | Best Researcher Award 

Dr. Minh-Khang Le, Cedars-Sinai Medical Center, United States

Minh-Khang Le, M.D., Ph.D., is a Postdoctoral Research Scientist in the Department of Pathology and Computational Biomedicine at Cedars-Sinai Medical Center in Los Angeles, California. He obtained his Doctor of Medicine degree from the University of Medicine and Pharmacy at Ho Chi Minh City, graduating in the top 10% of his class, and completed his Ph.D. in Pathology at the University of Yamanashi in Japan. His research focuses on integrating histopathology, molecular profiles, and clinicopathological features to characterize human cancers, particularly lymphoid and hematopoietic neoplasms. Dr. Le has contributed to several projects involving histopathology, molecular analyses, and the development of clinicopathological machine-learning models. As a strong advocate for the transformative potential of artificial intelligence in pathology, he aims to enhance the understanding and treatment of cancer. In addition to his postdoctoral position, he has held research roles at various institutions, including the University of Iowa Hospitals and Clinics and the University of Oklahoma Health Sciences Center. Dr. Le’s work has led to impactful advancements in digital pathology and cancer research.

Professional Profile:

SCOPUS

Researcher Suitability Summary for Best Researcher Award: 

Minh-Khang Le is an exemplary candidate for the Best Researcher Award, showcasing a profound commitment to advancing the field of digital pathology and computational biomedicine. His research is particularly focused on integrating histopathological and molecular profiles to enhance the understanding and characterization of human cancers, especially lymphoid and hematopoietic neoplasms. This multidisciplinary approach not only reflects his extensive knowledge but also his dedication to translating complex data into meaningful clinical insights.

Education 🎓

  • Postdoctoral Research Scientist
    Cedars-Sinai Medical Center, Department of Computational Biomedicine and Pathology
    July 2024 – Present
    8700 Beverly Blvd, Los Angeles, CA, USA
  • Ph.D. Student
    University of Yamanashi, Department of Pathology
    April 2020 – March 2024
    GPA: 3.5/4.0
    1110 Shimokato, Chuo, Yamanashi, Japan
  • Doctor of Medicine
    University of Medicine and Pharmacy at Ho Chi Minh City
    October 2013 – September 2019
    Degree Classification: Good (Top 10% of the Course)
    Ho Chi Minh City, Vietnam

Work Experience 💼

  • Postdoctoral Research Scientist
    Cedars-Sinai Medical Center, Department of Computational Biomedicine and Pathology
    July 2024 – Present
  • Part-time Researcher
    New Energy and Industrial Technology Development Organization (NEDO)
    April 2022 – Present
  • Part-time Researcher
    Department of Pathology, The University of Iowa Hospitals and Clinics, Iowa, USA
    April 2022 – Present
  • Research Assistant
    Department of Pathology, University of Yamanashi
    April 2020 – Present
  • Teaching Assistant
    Department of Pathology, University of Yamanashi
    April 2020 – Present
  • Part-time Researcher
    Department of Neurosurgery, The University of Oklahoma Health Sciences Center, Oklahoma, USA
    April 2021 – March 2022

Achievements, Awards, and Honors 🏆

  • Top 10% of the Course in Doctor of Medicine program at the University of Medicine and Pharmacy at Ho Chi Minh City
  • GPA of 3.5/4.0 in Ph.D. studies at the University of Yamanashi

Publication Top Notes:

Clinical implication of PRAME immunohistochemistry in differentiating melanoma in situ and dysplastic nevus in non-acral nevus-associated melanoma in situ: An institutional experience and meta-analysis

A Novel Artificial Intelligence-Based Parameterization Approach of the Stromal Landscape in Merkel Cell Carcinoma: A Multi-Institutional Study

Comprehensive analysis of distinct circadian clock subtypes of adult diffuse glioma and their associations with clinicopathological, genetic, and epigenetic profiles

CXCL5 expression is associated with active signals of macrophages in the microenvironment of papillary thyroid carcinoma

Severe asthmatic airways have distinct circadian clock gene expression pattern associated with WNT signaling