Prof. Wen Jiang | Artificial Intelligence | Best Researcher Award

Northwestern Polytechnical University, China.

Prof. Wen Jiang is a distinguished researcher and academic with a Ph.D. from Northwestern Polytechnical University, Xi’an, China (2009). She currently serves as a professor in the School of Electronics and Information at Northwestern Polytechnical University. Her work focuses on cutting-edge areas like information fusion, artificial intelligence, remote sensing image processing, and intelligent algorithm security, making her a leader in her field.

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Education 🎓

Prof. Wen Jiang has an impressive academic background in information systems and engineering. She earned her Ph.D. in Information Systems from Northwestern Polytechnical University, Xi’an, China, in 2009, where her research focused on innovative data systems and intelligent technologies. Prior to that, she completed her Master’s degree in Information Engineering at Information Engineering University, Zhengzhou, China, in 1997, gaining in-depth knowledge of advanced engineering concepts. She began her academic journey with a Bachelor’s degree in Information Engineering from the same university in 1994, building a strong foundation for her pioneering contributions to the field.

Experience 🏫

Prof. Wen Jiang is a Professor at the School of Electronics and Information, Northwestern Polytechnical University, where she has made a significant impact in academia. She is highly regarded for mentoring aspiring researchers and leading innovative projects in advanced technologies. Her leadership and expertise have been instrumental in driving forward research in areas like artificial intelligence, information fusion, and algorithm security..

Research Interests 🔍

Information Fusion:
Integrating data from diverse sources to enable smarter and more efficient decision-making processes, crucial for applications in defense, healthcare, and industry.

Artificial Intelligence:
Advancing machine learning and intelligent systems to solve complex problems and enhance automation across various domains.

Remote Sensing Image Processing:
Developing cutting-edge tools for environmental monitoring, urban planning, disaster management, and mapping applications.

Intelligent Algorithm Security:
Ensuring the robustness, reliability, and safety of AI-driven solutions to address vulnerabilities in critical systems.

Publications Top Notes 📚

A New Data Augmentation Method Based on Mixup and Dempster-Shafer Theory IEEE Transactions on Multimedia, 2024
Contributors: Zhuo Zhang, Hongfei Wang, Jie Geng, Xinyang Deng, Wen Jiang. Link

A Novel Air Target Intention Recognition Method Based on Sample Reweighting and Attention-Bi-GRU IEEE Systems Journal, 2024
Contributors: Yu Zhang, Weichen Ma, Fanghui Huang, Xinyang Deng, Wen Jiang. Link

Causal Intervention and Parameter-Free Reasoning for Few-Shot SAR Target Recognition IEEE Transactions on Circuits and Systems for Video Technology, 2024, Contributors: Jie Geng, Weichen Ma, Wen Jiang. Link

CMSE: Cross-Modal Semantic Enhancement Network for Classification of Hyperspectral and LiDAR Data IEEE Transactions on Geoscience and Remote Sensing, 2024, Contributors: Wenqi Han, Wang Miao, Jie Geng, Wen Jiang. Link

Dual-Path Feature Aware Network for Remote Sensing Image Semantic Segmentation IEEE Transactions on Circuits and Systems for Video Technology, 2024, Contributors: Jie Geng, Shuai Song, Wen Jiang. Link

Dr. Lane Schultz |Atomic Modeling | Best Researcher Award

University of Wisconsin-Madison, United States

Lane E. Schultz, Ph.D. is a materials scientist and engineer specializing in computational materials science, with a focus on machine learning applications in materials research. He recently completed his Ph.D. at the University of Wisconsin-Madison and has a robust portfolio in high-performance computing, metallic glass research, and advanced simulations. Lane is skilled in various programming languages and tools, such as Python, C++, Docker, and Linux, contributing to his success in both academic and industry projects.

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Education 🎓

Lane earned his Ph.D. in Materials Science and Engineering from the University of Wisconsin-Madison in 2024, with a GPA of 3.70/4.0. Prior to that, he completed his M.S. in Materials Science and Engineering at the same institution in 2020, also with a 3.70 GPA. He began his academic career with a B.S. in Engineering from Fort Lewis College in 2017, graduating with a stellar GPA of 3.99/4.0. Lane’s academic journey has been marked by excellence, earning multiple awards for his scholarly achievements.

Experience 🛠️

Lane has extensive research experience, including a pivotal role as a Research Assistant at the Computational Materials Group at UW-Madison (2018-2024). During this time, he contributed to the development of a domain of applicability method for machine learning models in materials science, enabling better prediction of material properties. Additionally, he was instrumental in constructing and managing scientific computing clusters and developed a high-throughput workflow to model metallic glass forming ability. Lane’s experience also extends to hands-on experimental work during his Summer Undergraduate Research Fellowships at Purdue and Fort Lewis College, where he developed Python tools and designed sensor packages.

Research Interests 🔬

1. Machine Learning for Materials Property Prediction & Applicability Domain Assessment
Lane’s research focuses on integrating machine learning techniques to predict material properties more accurately and efficiently. A significant part of his work involves developing methods to assess the applicability domain of these machine learning models, ensuring that predictions are reliable and robust across different datasets. This approach helps identify the boundaries within which models can make accurate predictions, enhancing the trustworthiness of AI-driven materials science.

2. High-Throughput Simulations for Metallic Glass Formation
Lane specializes in using high-throughput simulations to explore the formation of metallic glasses. By leveraging large-scale computational models, he aims to predict critical cooling rates and other factors that influence glass formation. His work in this area contributes to a deeper understanding of the atomic-level behaviors that dictate the properties of metallic glasses, which are essential for developing new materials with unique mechanical and thermal properties.

3. Materials Informatics for Data-Driven Methodologies
Combining his expertise in computational materials science and informatics, Lane develops data-driven methodologies to accelerate materials discovery. His research in materials informatics involves building algorithms that can extract patterns and insights from extensive materials datasets. By applying these insights, Lane helps streamline the process of identifying novel materials with desirable properties, pushing the boundaries of what’s possible in materials engineering.

Awards 🏆

PPG Fellowship, University of Wisconsin-Madison

Ying Yu Chuang Graduate Support Award, UW-Madison

Sigma Pi Sigma, Physics Honor Society

Order of the Engineer, Fort Lewis College

Dean’s Council Freshman 4.0 Award, Fort Lewis College

Publications Top Notes 📚

Machine learning metallic glass critical cooling rates through elemental and molecular simulation-based featurization. Journal of Materiomics (2024). Link

Molecular dynamic characteristic temperatures for predicting metallic glass forming ability. Computational Materials Science (2022). Link

Accelerating ensemble uncertainty estimates in supervised materials property regression models. Computational Materials Science (2025). Link

Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science. Journal of Open Source Software (2024). Link

Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features. Chemistry of Materials (2022). Link