Yang Liu | Mathematics | Research Excellence Award

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Assist. Prof. Dr. Yang Liu | Mathematics | Research Excellence Award

Great Bay University | China

Dr. Yang Liu is a researcher in numerical optimization and scientific computing, with a focus on scalable algorithms for large-scale nonconvex problems. His work integrates numerical linear algebra, tensor methods, and Krylov subspace techniques to advance efficient optimization frameworks. He has contributed to the development of Newton-MR methods, pseudoinverse solution recovery, and adaptive regularization strategies for higher-order tensor models. His research emphasizes matrix-free and high-performance implementations, with contributions incorporated into advanced solver libraries. Dr. Yang Liu actively engages in international collaborations and contributes to peer-reviewed journals and conferences in optimization and applied mathematics.

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Featured Publications


Convergence of Newton-MR under inexact Hessian information

– SIAM Journal on Optimization, 2021 | Cited by: 25

Newton-MR: Inexact Newton method with minimum residual sub-problem solver

– EURO Journal on Computational Optimization, 2022 | Cited by: 22

MINRES: From negative curvature detection to monotonicity properties

– SIAM Journal on Optimization, 2022 | Cited by: 14

Descent properties of an Anderson accelerated gradient method with restarting

– SIAM Journal on Optimization, 2024 | Cited by: 11 

Rowena Merkel | Mathematics | Young Scientist Award

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Mrs. Rowena Merkel | Mathematics | Young Scientist Award

University of Education Freiburg | Germany

Rowena Merkel, M.Ed., is an emerging scholar and Ph.D. candidate at the Pädagogische Hochschule Freiburg, Germany, specializing in mathematics didactics and cognitive learning research. Her academic journey reflects strong interdisciplinary training, having earned a Master of Education and a Bachelor’s degree in Mathematics and Spanish from the Albert-Ludwigs-Universität Freiburg. Currently pursuing her doctoral research in Psychology and Mathematics Didactics, Rowena focuses on understanding how digital modeling tools can foster effective cognitive learning processes in developing students’ conceptual understanding of fractions. She has served as an Academic Associate at the Pädagogische Hochschule Freiburg, where she contributed to advancing digital pedagogical innovations and teaching methodologies. Her research interests span mathematics education, cognitive engagement, digital learning environments, and instructional design in STEM education. Rowena’s recent publication, Learning Activities in a Dynamic Learning Environment to Foster a Basic Fraction Concept (International Journal of Science and Mathematics Education, 2025 cited by 7 articles*), exemplifies her contributions to developing research-based digital tools for mathematics instruction. Her academic achievements demonstrate a commitment to evidence-based education, blending theory and practice to enhance cognitive development through technology-enhanced learning. Through her innovative approach, Rowena Merkel aims to bridge psychology and pedagogy, making complex mathematical concepts accessible to diverse learners. Her work holds promise for shaping the future of mathematics education, positioning her as a dynamic and deserving nominee for the Young Scientist Award.

Profile: ORCID | LinkedIn

Featured Publications

Merkel, R., Leuders, T., Reinhold, F., & Loibl, K. (2025). Learning activities in a dynamic learning environment to foster a basic fraction concept. International Journal of Science and Mathematics Education. https://doi.org/10.1007/s10763-025-10602-6