Heng-Guo Zhang | Applied Mathematics | Best Researcher Award

Posted on by Math Scientist

Assoc. Prof. Dr. Heng-Guo Zhang | Applied Mathematics | Best Researcher Award

Associate Professor at Shandong University, China

Dr. Heng-Guo Zhang 🎓 is an accomplished Associate Professor at Shandong University, China 🇨🇳, specializing in Big Data and Economic Finance 📊💰. With a Ph.D. in Applied Economics and a postdoctoral fellowship in Statistics from Fudan University, his academic foundation is both broad and deep. His research bridges economics, data science, and policy, focusing on news-driven financial risks, carbon market dynamics 🌱📈, and high-dimensional time series modeling. Dr. Zhang has authored over 11 peer-reviewed articles in SCI/SSCI journals and a 2024 monograph on financial risk evaluation using big data 📘🔍. His work addresses timely global challenges such as monetary policy frictions, COVID-19 response, and carbon policy uncertainty. Passionate about data-driven policymaking and economic modeling, Dr. Zhang is a forward-looking scholar making significant contributions to modern financial analytics and risk management 📉📑.

Professional Profile 

Scopus Profile

🎓 Education

Dr. Heng-Guo Zhang has a strong interdisciplinary academic background. He earned his Ph.D. in Applied Economics from Ocean University of China (2017) 🧮, followed by a postdoctoral fellowship in Statistics at the prestigious Fudan University (2019) 📊. Prior to this, he completed his M.S. in Information Science (2011) and B.S. in Library Science (2008) from Sun Yat-sen University 🏫. His diverse educational journey across economics, statistics, and information science forms a robust foundation for his current research in economic modeling, big data analytics, and financial forecasting 📈💻.

 🏛️ Professional Experience

Dr. Zhang currently serves as an Associate Professor at the Center for Economic Research, Shandong University (since 2019) 🏛️. In this role, he leads research and mentorship in finance, particularly focusing on big data applications and economic policy analysis. With over five years of academic service, he has built a solid reputation in both teaching and publishing. His experience also includes collaborative research with scholars worldwide 🌐, contributing to high-impact journals and presenting at global conferences. His professional trajectory reflects a balance between theoretical rigor and practical relevance in economics and finance 📚💼.

🔬 Research Interest

Dr. Zhang’s research revolves around Big Data and Economic Finance 📊💵. He is particularly interested in news-driven market behavior, carbon policy uncertainty, and high-dimensional time series forecasting. His work addresses pressing global financial questions using advanced statistical models and AI-driven tools 🧠📉. Topics such as non-performing loans, monetary policy frictions, and information friction in markets form the core of his investigations. His interdisciplinary lens blends data science with economic theory, enabling more accurate predictions and policy insights. Dr. Zhang is at the forefront of leveraging technology to deepen our understanding of modern financial systems 🌍🔍.

🏅 Awards and Honors

While detailed awards are not listed, Dr. Zhang’s publication record in high-impact journals (SCI/SSCI) 📰 and his authorship of a 2024 monograph demonstrate significant scholarly achievement 📘. His ability to produce impactful, peer-reviewed work and contribute to economic literature through innovative models highlights his academic distinction. Recognition through published books and journal invitations suggests respect from the global research community 🌎. Given his trajectory, Dr. Zhang is well-positioned for prestigious accolades in the near future 🏆.

🛠️ Research Skills

Dr. Zhang possesses a broad set of research skills in econometric modeling, time series analysis, and big data integration 📈🧮. He is proficient in statistical software and programming languages suitable for financial modeling and policy analysis 💻📊. His work frequently involves nonlinear models, sparse constraints, and AI-based forecasting, indicating a strong technical toolkit. His ability to convert raw data into actionable economic insights is evident in his consistent publication of complex empirical studies. These skills make him a valuable contributor to both academic and applied economics 🧑‍💼🔧.

Publications Top Note 📝

  • An Adaptive Evolutionary Causal Dynamic Factor Model
    • Authors: Wei Q., Zhang H. G.
    • Year: 2025
    • Source: Mathematics (SCI), 13(11):1891

  • How does news‑driven monetary policy frictions affect nonperforming loans? Taking Chinese commercial banks as an example
    • Authors: Zhang H. G., Wang S., Xie Y.
    • Year: 2025
    • Source: The North American Journal of Economics and Finance (SSCI), 76:102353

  • News‑driven bubbles in futures markets
    • Authors: Zhang H. G., Li T.
    • Year: 2023
    • Source: Journal of Energy Markets (SCI), 16(2):55–78

  • Does carbon policy uncertainty affect economic growth? Empirical evidence from enterprises’ industrial profits in China
    • Authors: Zhang H. G., Wenti Du, Yuchi Xie
    • Year: 2023
    • Source: Economic Computation & Economic Cybernetics Studies & Research (SSCI), 57(3):283–294

  • Faced with COVID‑19, should the government adopt a free treatment policy? Evidence from China
    • Authors: Zhang H. G.
    • Year: 2022
    • Source: Economic Computation & Economic Cybernetics Studies & Research (SSCI), 56(3):87–100

  • Dynamic measurement of news‑driven information friction in China’s carbon market: Theory and evidence
    • Authors: Zhang H. G., Tingting Cao, Li H., Xu T.
    • Year: 2021
    • Source: Energy Economics (SCI), 95:104994

  • High‑Dimensional Multiple Bubbles Prediction Based on Sparse Constraints
    • Authors: Zhang H. G., Wu L.
    • Year: 2019
    • Source: IEEE Access (SCI), 7:38356–38368

  • Can onshore spot market progress influence offshore NDF market development for the CNY?
    • Authors: Su C. W., Wang K. H., Zhang H. G., Nian R.
    • Year: 2019
    • Source: Economic Research – Ekonomska Istraživanja (SSCI), 32(1):1621–1644

  • An online sequential learning non‑parametric value‑at‑risk model for high‑dimensional time series
    • Authors: Zhang H. G., Wu L., Song Y., Su C. W., Wang Q., Su F.
    • Year: 2018
    • Source: Cognitive Computation (SCI), 10(2):187–200

  • Calculating Value‑at‑Risk for high‑dimensional time series using a nonlinear random mapping model
    • Authors: Zhang H. G., Su C. W., Song Y., Qiu S., Xiao R., Su F.
    • Year: 2017
    • Source: Economic Modelling (SSCI), 67:355–367

  • Is exchange rate stability beneficial for stabilizing consumer prices in China?
    • Authors: Su C. W., Zhang H. G., Chang H. L., Nian R.
    • Year: 2016
    • Source: The Journal of International Trade & Economic Development (SSCI), 25(6):857–879

✅ Conclusion

Dr. Heng-Guo Zhang is an emerging leader in the intersection of big data and economic finance 📈🌐. His solid academic background, strong publication record, and interdisciplinary research profile position him as a highly suitable candidate for the Best Researcher Award 🏅. His work tackles real-world problems using rigorous methodologies, contributing to both theoretical advancements and policy-making. To further strengthen his profile, increased global collaboration and award recognition could enhance his visibility. Overall, Dr. Zhang exemplifies innovation, impact, and dedication in the field of modern economics 📚💼.

Yangshanshan Liu | Applied Mathematics | Best Researcher Award

Posted on by Math Scientist

Dr. Yangshanshan Liu | Applied Mathematics | Best Researcher Award

Post Doc at Nankai University, China

Dr. Yangshanshan Liu 🎓 is a postdoctoral researcher at the Chern Institute of Mathematics, Nankai University, specializing in Celestial Mechanics, Central Configurations, and Hamiltonian Systems 🌌. With a Ph.D. and Master’s degree from Sichuan University under the guidance of Prof. Shiqing Zhang, her work bridges mathematical theory and computational dynamics. She has published in high-impact journals such as SIAM J. Appl. Dyn. Syst. and J. Geom. Phys. 📚. A former award-winning high school math teacher 🧑‍🏫, Dr. Liu combines educational dedication with scholarly excellence. Her presentations at leading conferences like ICMS and AIMS 2024 🌍 reflect growing international recognition. With a passion for algebraic geometry and programming 💻, Dr. Liu is a rising researcher contributing meaningfully to the global mathematics community through both innovation and outreach.

Professional Profile 

Education 🎓

Dr. Yangshanshan Liu holds a Ph.D. and Master’s degree in Mathematics from Sichuan University, China, where she studied under Prof. Shiqing Zhang. Her doctoral thesis focused on Central Configurations in the Newtonian n-Body Problems with Homogeneous Potentials, while her master’s research addressed symmetric configurations in the planar five-body problem. She earned her Bachelor of Science in Mathematics from Liaoning University, with a thesis exploring stock index volatility using the ARCH model 📈. Her academic journey reflects a strong foundation in both pure and applied mathematics, complemented by analytical and computational rigor. Dr. Liu’s consistent academic excellence is marked by scholarships and recognition at all levels of her education, establishing her as a highly qualified and promising researcher in the mathematical sciences 📘.

Professional Experience 💼 

Dr. Liu is currently a postdoctoral researcher at the prestigious Chern Institute of Mathematics, Nankai University, under the supervision of Prof. Chaofeng Zhu. Since July 2023, she has actively contributed to theoretical research in dynamical systems and celestial mechanics. Prior to her academic career, she served as a Senior High School Mathematics Teacher at Rainbow Education (2010–2017), where she was recognized as “Outstanding Teacher of the Year” 🏆. Her professional path demonstrates a rare blend of teaching excellence and deep research engagement. From mentoring students to contributing original findings to high-level mathematical problems, Dr. Liu has shown versatility, leadership, and an unwavering commitment to the dissemination and advancement of mathematical knowledge at every stage of her career.

Research Interests 🔍

Dr. Liu’s research focuses on Celestial Mechanics, particularly the Newtonian n-Body Problem, Central Configurations, and Hamiltonian Systems. She also delves into Index Theory and Computational Algebraic Geometry, contributing both theoretical and computational insights. Her interdisciplinary approach connects classical mechanics with modern mathematical tools, such as programming and symbolic computation 💻. Dr. Liu aims to explore how geometric and topological methods can enrich our understanding of dynamical systems in higher-dimensional spaces. Her interests extend to practical applications and numerical simulations, facilitating broader applicability of abstract mathematical theories. This versatile research scope not only reinforces the depth of her expertise but also signals her ambition to solve complex real-world and theoretical problems in mathematical physics and geometry 🌌.

Awards and Honors 🏅

Dr. Liu has been recognized throughout her academic journey with numerous scholarships and awards. She received annual Ph.D. and Master’s scholarships from Sichuan University (2017–2023), and was named an Outstanding Graduate Student in 2020. She earned the Liu Yingming Scholarship in 2022, a notable recognition within the School of Mathematics. During her undergraduate years at Liaoning University, she received continuous scholarship support from 2006 to 2010 for academic excellence 📚. Her earlier career in education was equally decorated, earning her the “Outstanding Teacher of the Year” award in 2015 at Rainbow Education. These accolades reflect her diligence, talent, and commitment to both learning and teaching, solidifying her reputation as a dedicated and accomplished figure in the field of mathematics 🏆.

Research Skills 🧠

Dr. Liu possesses strong research skills in mathematical modeling, analytical computation, and dynamical systems. She is proficient in programming and computational algebraic geometry, allowing her to analyze and simulate complex n-body interactions with precision 💻. Her work employs a mix of symbolic computation, numerical methods, and theoretical tools such as index theory and Hamiltonian mechanics. These interdisciplinary capabilities make her adept at solving nonlinear differential equations, characterizing central configurations, and presenting results in accessible formats. Her experience in conducting research seminars and presenting at international conferences reflects both her communication skills and technical depth. These capabilities equip Dr. Liu to contribute significantly to emerging mathematical challenges and collaborative global research in applied and theoretical mathematics 🌐.

Publications Top Notes

  • Title: On the Uniqueness of the Planar 5-Body Central Configuration with a Trapezoidal Convex Hull
    Authors: Yangshanshan Liu, Shiqing Zhang
    Year: 2025
    Citation Count: Not yet available (recent publication)
    Source: Journal of Geometry and Physics, Volume 213, Article ID 105494
    DOI: 10.1016/j.geomphys.2025.105494

  • Title: Stacked Central Configurations with a Homogeneous Potential in ℝ³
    Authors: Yangshanshan Liu, Shiqing Zhang
    Year: 2023
    Citation Count: Not yet available
    Source: SIAM Journal on Applied Dynamical Systems, Volume 22, Issue 2, Pages 635–656
    DOI: 10.1137/22M1495032

Conclusion 🔬

Dr. Yangshanshan Liu is a well-rounded and accomplished mathematician with significant potential in the global academic landscape 🌍. Her transition from an award-winning educator to a productive researcher demonstrates not only her versatility but also a deep commitment to the mathematical sciences. With a focus on celestial mechanics and central configurations, backed by strong computational and analytical skills, Dr. Liu’s work addresses complex theoretical problems with clarity and innovation. Her publications in reputable journals, presentations at major conferences, and consistent academic honors position her as a strong candidate for recognition such as the Best Researcher Award 🏅. She continues to make meaningful contributions to her field, reflecting excellence, resilience, and intellectual rigor in every aspect of her academic career.

 

Halima Bensmail | Applied Mathematics | Best Researcher Award

Posted on by Math Scientist

Prof. Dr. Halima Bensmail | Applied Mathematics | Best Researcher Award

Principal scientist at Qatar Computing Research Institute, Qatar

Dr. Halima Bensmail is a distinguished Principal Scientist at the Qatar Computing Research Institute, specializing in machine learning, bioinformatics, biostatistics, and statistical modeling. With a Ph.D. in Statistics (Summa Cum Laude) from the University Pierre & Marie Curie, she has made significant contributions to Bayesian inference, multivariate analysis, and precision medicine. She has an impressive research record with an H-index of 31, i10-index of 54, and around 140 publications in prestigious journals such as Nature Communications, JASA, and IEEE TNNLS. As the founder of the Statistical Machine Learning and Bioinformatics group at QCRI, she has led groundbreaking projects, including the development of open-source data-driven tools like the PRISQ pre-diabetes screening model and MCLUST clustering algorithm. With extensive academic experience in the USA, France, and the Netherlands, she has mentored numerous postdocs and students, shaping the next generation of researchers. Her expertise and leadership make her a key figure in data science and precision health.

Professional Profile 

Google Scholar
Scopus Profile

Education

Dr. Halima Bensmail holds a Ph.D. in Statistical Machine Learning (Summa Cum Laude) from the University Pierre & Marie Curie (Paris 6), where she specialized in Bayesian inference, spectral decomposition, and mixture models. Her thesis focused on deterministic and Bayesian model-based clustering and classification for data science applications. Prior to that, she earned an M.S. in Machine Learning from the same university, with a focus on probability, financial modeling, and stochastic processes. She also holds a Bachelor’s degree in Applied Mathematics and Statistics from the University Mohammed V in Morocco, where she gained expertise in numerical analysis, stochastic processes, topology, and mathematical programming. Throughout her academic journey, she was mentored by esteemed professors and developed a strong foundation in theoretical and applied statistics. Her educational background has laid the groundwork for her pioneering research in machine learning, bioinformatics, and data-driven modeling for real-world applications.

Professional Experience

Dr. Bensmail is currently a Principal Scientist at the Qatar Computing Research Institute (QCRI), where she leads research in bioinformatics, statistical machine learning, and artificial intelligence. She also serves as a Full Professor in the College of Science and Engineering at Hamad Bin Khalifa University and a Visiting Full Professor at Texas A&M University at Qatar. Previously, she held tenured faculty positions at Virginia Medical School and the University of Tennessee, where she contributed significantly to public health and business administration research. She has also worked as a Research Scientist at the University of Leiden, a scientist at the Fred Hutchinson Cancer Research Center, and a postdoctoral researcher at the University of Washington. With decades of experience across academia and research institutions in the U.S., Europe, and the Middle East, she has built expertise in developing statistical and AI-driven solutions for biomedical and computational challenges.

Research Interests

Dr. Bensmail’s research spans statistical machine learning, bioinformatics, and precision medicine. She has developed novel clustering algorithms, such as an advanced Bayesian clustering model implemented in the MCLUST package, and statistical methods for analyzing Next-Generation Sequencing (NGS) data. She is also interested in computational biology, specifically protein-protein interactions, protein solubility, and structural biology. Her work includes dimensionality reduction techniques like nonnegative matrix factorization and discriminative sparse coding for domain adaptation. In the field of precision medicine, she has designed PRISQ, a statistical model for pre-diabetes screening. Her broader interests include Bayesian statistics, functional data analysis, information theory, and high-dimensional data modeling. With a strong focus on developing real-world data-driven tools, she actively contributes to statistical methodologies that enhance decision-making in medicine, genomics, and artificial intelligence applications.

Awards and Honors

Dr. Bensmail has received numerous accolades for her contributions to machine learning, bioinformatics, and statistical modeling. Her work has been widely recognized, with over 140 peer-reviewed publications and an H-index of 31, demonstrating the impact of her research. She has secured research grants and led major projects in AI-driven healthcare solutions. Her contributions to the field have been acknowledged through invitations to serve as a keynote speaker at international awards and as an editorial board member for high-impact journals. She has also been instrumental in mentoring young researchers, postdoctoral fellows, and doctoral students, fostering the next generation of scientists in AI, statistics, and bioinformatics. Additionally, her work on statistical methods for precision medicine and biomedical informatics has gained international recognition, positioning her as a leading expert in the field of data science for healthcare and computational biology.

Conclusion

Dr. Halima Bensmail is a pioneering researcher in machine learning, statistical modeling, and bioinformatics, with a career spanning leading institutions in the U.S., Europe, and the Middle East. Her contributions to clustering algorithms, high-dimensional data analysis, and precision medicine have made a lasting impact on the fields of AI and computational biology. As a mentor and leader, she has shaped numerous young scientists and postdocs, driving innovation in data science applications. With a robust publication record, influential research projects, and a dedication to developing real-world AI-driven solutions, she stands as a leading figure in statistical machine learning. Her expertise and contributions continue to push the boundaries of knowledge in bioinformatics, artificial intelligence, and healthcare analytics, making her a strong candidate for prestigious research awards and recognition in scientific communities worldwide.

Publications Top Noted