Maliesha's research interests lie at the intersection of machine learning and materials science. During her undergraduate studies, she focused on enhancing face recognition methods by integrating a neural network into the established FaceNet framework, surpassing existing benchmarks. Transitioning to her career, Maliesha initially served as an image processing engineer and later as a software engineer. Currently, she is engaged in developing an automated pipeline for segmenting and characterizing pitting corrosion in aluminum using XCT images, alongside setting a benchmark for image segmentation in materials science. Looking ahead, her aim is to persist in tackling interdisciplinary research challenges employing cutting-edge data science methodologies.

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.

Rishabh is deeply passionate about sustainable materials science. Initially drawn to structural ceramics, inspired by space shuttle tiles, Rishabh later shifted focus toward addressing the global sustainability crisis by working on projects combining functional material properties with environmental sciences. Through the course of his PhD, Rishabh aims to gain expertise in data science to predict the lifetime of electronic components. Post-PhD, blending materials science, environmental sciences, and AI, Rishabh aspires to advocate for true sustainable development, driving innovations that are both technologically advanced and environmentally responsible, ultimately contributing to a more sustainable future.

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.

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.