Associated Faculty

The work we conduct at Case School of Engineering’s Solar Durability and Lifetime Extension (SDLE) Research Center wouldn’t be possible without the efforts of our dedicated research team. On this page, you can see which faculty members are involved with our work at Case Western Reserve University. We also encourage you to get to know our post doc and student researchers.

Faculty

Jonah Bachman

PhD Student in Materials Science & Engineering, SDLE Research Center

After getting my B.S. in mechanical engineering and minor in applied data science from CWRU, I worked at Los Alamos National Laboratory engineering sample environments for the MST-8 neutron scattering team. I was drawn towards the analytical side of experimentation and returned to CWRU for my PhD in materials science, where I am continuing to work in neutron and x-ray scattering, just this time on the data side. I eventually hope to leverage my test engineering, automation, and data skills to become a 'full-stack' researcher, ideally in data-driven complex systems.

Erika Barcelos

Research Assistant Professor, Materials Science and Engineering

Develops geospatial predictive models and geospatial data science tools to study climate and environmental related phenomena and their impacts in nature. Develop data management and data integration frameworks for data and metadata to enable FAIRification.

Laura Bruckman

Associate Professor, Materials Science and Engineering

Develops predictive lifetime models for materials degradation related to stress conditions and induced degradation mechanisms evaluated by quantitative spectroscopic characterization of materials

Yangxin Fan

PhD Student in Computer & Data Science, SDLE Research Center

PhD Student, SDLE Research Center

Roger French

Director SDLE Research Center, CWRU

Faculty Director, Applied Data Science Program, CWRU

Professor, Materials Science and Engineering

Director, SDLE Research Center

Applies data science and analytics to energy and materials science research problems

Kyle Henrikson

PhD Student in Materials Science & Engineering, SDLE Research Center

Kristen Hernandez

PhD Student in Materials Science & Engineering, SDLE Research Center

I am a PhD/MS Student in Material Science pursuing higher education. Graduate from Youngstown State University (YSU) in Chemistry with dual minors in Physics and Mathematics, I embarked on an early career with Element Materials Technology, a leading provider of materials and product qualification testing services. The evaluation of materials . I am involved in MDS3 mission as a material scientist that focuses on advanced manufacturing challenges that can be solved using data science methods.

Hein Htet Aung

PhD Student in Materials Science & Engineering, SDLE Research Center

With a background in Mechanical Engineering and Materials Science, Hein loves applying data science to gain insights from systems at macro and micro levels. For his Master’s research, he worked on data-driven modeling for degradation of acrylic polymers and released an R software package. After his internship with Lawrence Livermore National Lab during his Ph.D. program, Hein is working to expand his research in DIW systems to model the error behavior of the build platform during the printing process and contribute towards the development of data-driven digital twins. Hein aspires to work in an interdisciplinary research area where he can explore novel ideas through data science.

Mingjian Lu

PhD Student in Computer & Data Sciences, SDLE Research Center

Mingjian specializes in the intersection of deep learning and material science. His research is primarily focused on applying advanced deep learning techniques to tackle complex challenges in materials science. Mingjian's work involves utilizing sophisticated algorithms in data mining and machine learning to process and analyze large sets of data, particularly in the realm of image processing. His contributions include the application of deep learning methodologies for object detection and segmentation, which are critical in understanding and solving material-related problems. By integrating deep learning with material science, Mingjian is at the forefront of developing innovative solutions for material degradation, life extension, and other crucial aspects of material science. His work not only advances the field of computational data science but also has tangible impacts on the practical applications of materials in various industries.

Rosalind Madorsky

Operations Manager,

Supports the SDLE Research Center by coordinating student researcher personnel, managing the core facility, and performing other financial and administrative tasks

Mohommad Redad Mehdi

PhD Student in Materials Science & Engineering, SDLE Research Center

Redad is a Materials scientist by trade and loves understanding fundamental atomic phenomena underlying various material behaviours and mechanical properties. As a PhD student at MDS3, he is applying cutting-edge data science and machine learning to simplify the analysis of colossal synchrotron XRD data. He want to keep solving these fundamental materials science problems using computer and data science advances in the future.

Arafath Nihar

PhD Student in Computer & Data Sciences, SDLE Research Center

Ayorinde Olatunde

PhD Student, SDLE Research Center

As a PhD student in Applied Mathematics with a background in Statistics, Ayorinde is currently researching with MDS3, focusing on Uncertainty Quantification and extensions to other topical areas where statistical solutions for Material Data Science problems are needed. While collaborating with other field experts, he employs statistical methodologies and machine learning to optimize processes and contribute to interdisciplinary research initiatives.

Gabriel Ponon

Masters Student in Materials Science & Engineering, SDLE Research Center

I am a BS/MS student in materials science and engineering with a keen interest in materials data science. My research primarily focuses on developing algorithms and tools for accelerating the analysis of 2D XRD diffraction data, leveraging deep learning, ontologies and FAIR principles. Through the program, I aim to develop my skills to be able to generalize my work to wider applications in synchrotron research. My overarching goal is to approach contemporary materials characterization techniques with an algorithmic data-driven perspective to improve scalability and utility.

Alp Sehirlioglu

Department Chair, Department of Materials Science and Engineering

Professor, Materials Science and Engineering

Develops new materials through exploitation of interfaces to control functionality and exploration of multi-functionality for energy-related applications

Richard Tomazin

Engineer 3,

Provides engineering support for the Materials Science & Engineering Department, Advanced Manufacturing and Mechanical Reliability Center, AMMRC, and the Solar Durability and Lifetime Extension Center, SDLE.

Quynh Tran

Postdoctoral Scholar, SDLE Research Center

Pawan Tripathi

Research Assistant Professor, Materials Science and Engineering

Develop data analysis techniques, tools, and pipelines for high dimensional datasets in materials science such as synchrotron XRD, XCT, and advanced manufacturing to reveal insights, and correlations and improve predictive modeling leveraging deep learning, artificial intelligence, and statistical methods.

Materials Data Science, Advanced Manufacturing, Synchrotron Data Analysis, Image Processing, Deep Learning, Artificial Intelligence, Uncertainty Quantification, Atomistic Simulation

Weiqi Yue

PhD Student in Computer & Data Science, SDLE Research Center

My research focus lies in the realm of 2D X-ray diffraction data analysis, where I leverage computer vision and deep learning techniques to extract complex features. Additionally, I am actively involved in the development of federated learning frameworks, ensuring collaborative model training across multiple clients while prioritizing privacy protection. My overarching goal is to delve into the theoretical foundations of deep learning algorithms, identifying and optimizing models tailored to specific tasks in diverse areas.

Materials Data Science for Stockpile Stewardship (MDS) Affiliated Faculty

Center for Advancing Sustainable and Distributed Fertilizer Production (CASFER) Affiliated Faculty

Center on Materials Data Science for Reliability and Degradation (MDS-Rely) Affiliated Faculty