Assoc. Prof. Dr Khrystyna Lipianina-Honcharenko | Computer Science | Young Scientist Award |

Published on by Award and Honors

Assoc. Prof. Dr Khrystyna Lipianina-Honcharenko | Computer Science | Young Scientist Award

Associate professor , at West Ukrainian National University, Ukraine.

Assoc. Prof. Dr. Khrystyna Lipianina-Honcharenko is a dedicated academic and researcher specializing in Information Technology, with a strong foundation in economic cybernetics and artificial intelligence. Based at the West Ukrainian National University in Ternopil, Ukraine, she has steadily progressed through the academic ranks, currently serving as an Associate Professor in the Department of Information Computer Systems and Control. Her work bridges data science, simulation, and socio-economic modeling, with a strong commitment to research excellence and innovation in education and interdisciplinary projects.

Professional Profile

Orcid

Google Scholar

Education 🎓

Dr. Lipianina-Honcharenko holds a Ph.D. in Technical Sciences (2019), with a specialization in Information Technology, and is currently completing her Doctor of Technical Sciences (2025) at West Ukrainian National University. She began her academic journey with a Bachelor’s degree in Economic Cybernetics (2011), followed by a Master’s degree in Information Technologies in Economics (2012). She also completed postgraduate studies in Economic Cybernetics and Informatics between 2013 and 2017. Her education reflects a deep and consistent engagement with technical and analytical disciplines critical to modern digital research.

Experience đź’Ľ

Dr. Lipianina-Honcharenko has over a decade of academic experience. She began as a Laboratory Assistant in the Department of Economic Cybernetics and Informatics (2012–2014), before becoming a Lecturer in the same department (2013–2020). She then served as a Senior Lecturer in the Department of Information Computer Systems and Control (2020–2021) and was promoted to Associate Professor in 2021, a role she currently holds. Her teaching and mentorship have consistently focused on data science, IT systems, and modeling, contributing to the development of future tech-savvy professionals.

Research Interests 🔬

Her research interests include data analysis, simulation, machine learning, modeling and forecasting socio-economic processes, and the development of artificial intelligence methods. She actively contributes to cutting-edge research in cyber-physical systems, and has led or participated in numerous national and international projects, including Erasmus+ initiatives, disinformation detection tools (TruScanAI), and digital heritage visualization through augmented and virtual reality. Her work is known for its interdisciplinary applications, blending technology, economics, and societal impact.

Awards 🏆

While specific named awards are not listed, Dr. Lipianina-Honcharenko’s selection and participation in prestigious European research initiatives such as Erasmus+ “Work4CE”, My Farm, and AURA projects highlight her recognized competence and contribution on an international scale. Her leadership in these multi-national projects reflects her credibility and collaborative skills in the global academic community.

Top Noted Publications đź“š

1. Decision Tree Based Targeting Model of Customer Interaction with Business Page
Authors: H. Lipyanina, A. Sachenko, T. Lendyuk, S. Nadvynychny, S. Grodskyi
Index: CMIS
Citations: 37
Year: 2020
Pages: 1001–1012

2. Economic Crime Detection Using Support Vector Machine Classification
Authors: A. Krysovatyy, H. Lipyanina-Goncharenko, S. Sachenko, O. Desyatnyuk
Index: MoMLeT+ DS 2917
Citations: 25
Year: 2021
Pages: 830–840

3. Assessing the Investment Risk of Virtual IT Company Based on Machine Learning
Authors: H. Lipyanina, V. Maksymovych, A. Sachenko, T. Lendyuk, A. Fomenko, I. Kit
Index: International Conference on Data Stream Mining and Processing
Citations: 24
Year: 2020
Pages: 167–187

4. Targeting Model of HEI Video Marketing Based on Classification Tree
Authors: H. Lipyanina, S. Sachenko, T. Lendyuk, A. Sachenko
Citations: 22
Year: 2020

5. Concept of the Intelligent Guide with AR Support
Authors: K. Lipianina-Honcharenko, R. Savchyshyn, A. Sachenko, A. Chaban, I. Kit, et al.
Index: International Journal of Computing, Vol. 21, No. 2
Citations: 19
Year: 2022
Pages: 271–277

Conclusion

Assoc. Prof. Dr. Khrystyna Lipianina-Honcharenko exemplifies the qualities of a committed young scientist, with a diverse academic background, substantial international research experience, and a clear focus on interdisciplinary innovation. Her growing portfolio of research, especially in AI and cyber-physical systems, makes her a highly suitable candidate for prestigious recognitions such as the Research for Young Scientist Award. With continued development in international communication and publication outreach, she is well-positioned to make an even broader impact in the global scientific arena.

Dr Francesco Romor | Numerical analysis | Best Researcher Award |

Published on by Award and Honors

Dr. Francesco Romor | Numerical analysis | Best Researcher Award

Postdoctoral researcher , at Weierstrass Institute,Germany

Dr. Francesco Romor is a postdoctoral researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. He works within the Numerical Mathematics and Scientific Computing research group led by Prof. Volker John. His expertise lies in advanced computational methods, scientific machine learning, and mathematical modeling with applications spanning from fluid dynamics to medical image analysis. Known for combining mathematical rigor with modern machine learning techniques, Dr. Romor is establishing himself as a leading voice in the next generation of scientific computing experts.

profile

Scopus

Education

Dr. Romor earned his Ph.D. in Mathematical Analysis, Modeling, and Applications from the International School for Advanced Studies (SISSA), Trieste, Italy, in 2023. His doctoral research, supervised by Prof. Gianluigi Rozza, focused on “Nonlinear Parameter Space and Model Order Reductions enhanced by Scientific Machine Learning.” He previously obtained a Master’s degree in Mathematics (2019) and a Bachelor’s degree in Mathematics (2017), both with the highest honors (110/110 cum laude) from the University of Trieste. His academic path reflects a strong and consistent foundation in theoretical and applied mathematics.

Experience

Since November 2023, Dr. Romor has been serving as a postdoctoral researcher at WIAS, where he collaborates closely with experts such as Dr. Alfonso Caiazzo. In 2023, he completed a short research visit to the Massachusetts Institute of Technology (MIT) as part of the MISTI MIT-Italy FVG Project, working with Prof. Youssef Marzouk. His professional engagements reflect a growing international footprint and recognition in high-level research environments.

Research interest

Dr. Romor’s research centers on reduced-order modeling, nonlinear parameter space reduction, scientific machine learning, and high-dimensional numerical simulations. He is particularly interested in hybridizing classical numerical methods with deep learning tools, such as convolutional autoencoders and graph neural networks, to solve complex partial differential equations (PDEs) efficiently. His recent work extends to data assimilation in biomedical applications, including modeling aortic coarctation using shape registration and neural networks. These innovations push the boundary of how science and AI can co-evolve for real-world problem-solving.

Awards

While specific awards are not listed, Dr. Romor’s profile includes significant professional recognitions. These include being selected for a prestigious research visit to MIT and multiple invitations to speak at international conferences such as ECCOMAS, SIAM CSE, and AICOMAS. Such invitations are indicators of esteem within the scientific community and recognition of his impactful contributions.

Publications

1. Friedrichs’ systems discretized with the DGM: domain decomposable model order reduction and Graph Neural Networks approximating vanishing viscosity solutions
Authors: Francesco Romor, Davide Torlo, Gianluigi Rozza
Journal: Journal of Computational Physics
Article ID: 113915
Year: 2025

2. Explicable hyper-reduced order models on nonlinearly approximated solution manifolds of compressible and incompressible Navier-Stokes equations
Authors: Francesco Romor, Giovanni Stabile, Gianluigi Rozza
Journal: Journal of Computational Physics
Volume: 524, Article ID: 113729
Year: 2025

3. Generative Models for the Deformation of Industrial Shapes with Linear Geometric Constraints: model order and parameter space reductions
Authors: Guglielmo Padula, Francesco Romor, Giovanni Stabile, Gianluigi Rozza
Journal: Computer Methods in Applied Mechanics and Engineering
Volume: 423, Article ID: 116823
Year: 2024

4. A local approach to parameter space reduction for regression and classification tasks
Authors: Francesco Romor, Marco Tezzele, Gianluigi Rozza
Journal: Journal of Scientific Computing
Volume: 99, Issue: 3, Article ID: 83
Year: 2024

5. Non-linear Manifold Reduced-Order Models with Convolutional Autoencoders and Reduced Over-Collocation Method
Authors: Francesco Romor, Giovanni Stabile, Gianluigi Rozza
Journal: Journal of Scientific Computing
Volume: 94, Article ID: 74
Year: 2023
DOI: 10.1007/s10915-023-02128-2

Conclusion

In summary, Dr. Francesco Romor exemplifies the qualities of a forward-thinking and high-impact researcher in computational science. With a strong mathematical foundation, international experience, innovative applications of machine learning, and a robust publication record, he is well-positioned for prestigious research honors and academic recognition. His work not only advances numerical methods but also connects disciplines—from automotive engineering to biomedical modeling—making him a valuable asset to the global research community.