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

Dr. Mohammad Reza Samadi | Manufacturing | Best Faculty Award

Faculty of Mechanics Department, Technical and Vocational University, Tehran, Iran

Dr. Mohammad Reza Samadi is an esteemed academic and researcher in Mechanical Engineering with a focus on Manufacturing and Production. Holding a Ph.D. in Mechanical Engineering, he has extensive experience in areas like welding, non-destructive testing (NDT), and advanced manufacturing processes. His impressive portfolio includes numerous certifications in Biotechnology, Materials and Metallurgy, and Industrial Inspection. As an award-winning researcher, Dr. Samadi has contributed significantly to the academic and industrial spheres through his research in nanocomposites, friction stir welding, and laser welding.

Profile

Scopus

Education🎓

Dr. Mohammad Reza Samadi completed his Ph.D. in Mechanical Engineering with a specialization in Manufacturing and Production, solidifying his expertise in cutting-edge manufacturing technologies. He also holds various international certifications in Biotechnology, Materials and Metallurgy, and Industrial Inspection, demonstrating his commitment to continuous professional development in the field of engineering.

Experience💼

With years of academic and industry experience, Dr. Samadi has excelled in research and teaching. He has authored several textbooks on welding processes, which are widely used in vocational and technical universities for training future engineers. His contributions extend beyond academia, as he is also engaged in applied research projects that enhance industrial manufacturing processes. Additionally, Dr. Samadi serves as a judge and reviewer for scientific conferences and technological projects, further cementing his role as a leader in the academic community.

Research Interests🔬

Welding & Non-Destructive Testing (NDT)🛠️
A significant portion of Dr. Samadi’s research focuses on welding processes, aiming to improve their efficiency and effectiveness. Additionally, his work on non-destructive testing (NDT) seeks to advance the ability to assess the integrity of materials without damaging them, ensuring that manufactured products meet stringent safety and quality standards.

Nanocomposites in Manufacturing🔬
Dr. Samadi explores the application of nanocomposites, materials that combine nanoparticles with polymers or metals to create superior materials with enhanced properties like strength, flexibility, and resistance to wear. His research in this area supports innovations in manufacturing processes, leading to the creation of lighter, stronger, and more durable materials.

Laser Welding Technologies⚙️
Laser welding is another area of interest in Dr. Samadi's research, where he investigates the benefits and challenges of using laser technology for precise, high-quality welding. This technology has applications in industries requiring fine, high-performance manufacturing, including electronics and automotive production.

Bridging Mechanical Engineering and Industrial Applications🔗
Dr. Samadi's interdisciplinary approach bridges the gap between theoretical mechanical engineering and practical industrial applications. His research is not only focused on theoretical advancements but also on their real-world implementation, helping to optimize manufacturing efficiency and improve product quality across a variety of industries.

Awards🏆

Dr. Samadi has been recognized multiple times for his research excellence, including receiving the "Best Researcher" award at both the university and provincial levels.

He has also earned silver medals at international innovation festivals, such as the prestigious Silicon Valley International Festival.

His outstanding contributions to research have been acknowledged globally, with numerous awards for his scientific presentations at national and international conferences.

Publications Top Notes📚

Optimization of FFF process parameters to improve the tensile strength and impact energy of polylactic acid/carbon nanotube composite, Authors: Hardani, H., Afshari, M., Allahyari, F., Afshari, H., Medina, E.M.M. Published in: Polymer Engineering and Science, 2024, 64(10), pp. 5047–5060. Link

Investigation of the effect of sintering process parameters on the corrosion, wear and hardness of W–Cu composite, Authors: Selahshorrad, E., Alavi, S.A., Zangeneh-Madar, K., Yazdanshenas, M., Afshari, M. Published in: Sadhana - Academy Proceedings in Engineering Sciences, 2024, 49(3), 209. Link

Design and optimization of mechanical and electromagnetic properties of GFRP composite, Authors: Talei-Fard, E., Parsa, H., Afshari, M., Samadi, M.R., Afshari, H. Published in: Journal of Materials Science: Materials in Electronics, 2024, 35(22), 1514. Link

Optimizing the sintering process parameters for simultaneous improvement of the compression strength, impact strength, hardness and corrosion resistance of W–Cu nanocomposite, Authors: Samadi, M.R., Zeynali, E., Allahyari, F., Zangeneh-Madar, K., Afshari, M. Published in: Modern Physics Letters B, 2024, 38(20), 2450169. Link

Studying the effects of FDM process parameters on the mechanical properties of parts produced from PLA using response surface methodology, Authors: Afshari, H., Taher, F., Alavi, S.A., Samadi, M.R., Allahyari, F. Published in: Colloid and Polymer Science, 2024, 302(6), pp. 955–970. Link