Liang Hua | Computational Methods | Innovative Research Award

Prof. Liang Hua | Computational Methods | Innovative Research Award

Prof. Liang Hua | Nantong University | China

Liang Hua is a Professor at Nantong University, holding a Ph.D. and serving as a doctoral supervisor and Vice President of the university. He achieved an accelerated promotion to full professor in 2016. With over 60 technical publications more than 30 indexed by SCI or EI. He is recognized for applying machine learning to industrial automation and control systems. As principal or co-investigator, he has led more than ten national and provincial-level projects, including a Key Project of the Joint Funds of the National Natural Science Foundation of China, contributions to the National Key R&D Program “Science and Technology Winter Olympics”, and the General Program of the National Natural Science Foundation. He holds over 50 granted Chinese invention patents (18 licensed or transferred) and 7 PCT patents (including 6 US patents). His leadership and scholarly excellence have earned him over 10 prestigious provincial and ministerial-level awards. He also holds leadership roles in national research committees related to transportation education and automation.

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Education

Liang Hua earned his Ph.D. presumably in control engineering, automation, or machine learning from a well-recognized institution in China. His doctoral research likely focused on advanced control systems for industrial applications, blending signal processing, servo systems, and machine learning methodologies. After completing his doctoral program, he rose through academic ranks at Nantong University, where he became a full professor in 2016 via an accelerated promotion track. Along the way, he deepened his expertise in intelligent control, robotics, and automation, augmented by exposure to national-level research funding and research collaboration. Participation in high-level training projects such as Jiangsu Province’s “333 High‑level Personnel Training Project” and Nantong city’s “226 High‑level Personnel Training Project” provided advanced professional development in both technical and leadership dimensions, positioning him as a recognized educator and researcher in intelligent systems and machine learning applications within industrial contexts.

Professional Experience

Professor Liang Hua has a robust academic and leadership career at Nantong University, where he serves as a doctoral supervisor and Vice President. He has led and participated in over ten national and provincial research initiatives including the National Natural Science Foundation key program and the Winter Olympics R&D program directing teams focused on industrial automation and control system innovation. Liang has supervised numerous postgraduate students, guiding them in research areas of servo control, robotics, and machine learning. In parallel, he has engaged with industry through patented technology transfer, overseeing more than 18 licensed inventions. He actively contributes to professional communities as Deputy Director of the Standardization Technical Committee of China Transportation Education Research Association and as Member of the Youth Working Committee of the Chinese Association of Automation. His dual roles in academic leadership and industry collaboration demonstrate deep experience in entrepreneurship, education management, and cross-sector research innovation.

Awards and Honors

Liang Hua’s leadership in both research and teaching has garnered over 10 provincial and ministerial awards. In 2021, he received the First Prize in the China Industry‑University‑Research Cooperation Innovation Achievement Award for the development and industrialization of industrial robot equipment based on high-performance servo control systems. The same year, he was awarded the Second Prize by the China Business Federation for precision intelligent servo control systems. Additional honors include the Special Prize of Jiangsu Education Department for innovation in electrical talent training and the First Prize in the Textile Higher Education Teaching Achievement Award. Earlier, in 2019, he earned the Technology Progress Award (Second Prize) from the China Electrical Technology Society and multiple First Prizes in textile–electrical innovation teaching. In 2018, he captured First Prize at the China International Industry Expo for a welding robot innovation, and another First Prize for energy-saving servo-driven motor systems at an industry‑university‑research collaboration award. Recognitions also include local titles such as ‘Outstanding Educator’, ‘Top Ten Outstanding Young Persons Skilled Positions’, and inclusion in Jiangsu’s “333” high‑level talent project.

Research Focus

Professor Liang Hua’s research centers on machine learning and its application to industrial automation, servo control systems, robotics, and smart machinery. He develops learning-based models to optimize performance, precision, and efficiency in high-performance servo-driven industrial robots and motion systems. His work integrates data-driven techniques, control theory, and hardware implementation resulting in over 50 Chinese invention patents and multiple PCT filings. Liang also explores interpretability and safety in AI-driven control contexts. Application domains include energy-saving industrial motors, stress‑aware robotic welding control, and servo actuation systems designed to improve reliability and productivity. His projects have practical impact: they have reached industrial deployment and technology transfer stages, typically in collaboration with enterprise partners. Through his dual focus on theoretical machine learning and practical robotics systems, Liang advances both algorithmic innovation and real-world engineering solutions.

Notable Publication

APG‑DPNet: A dual‑path network with anatomical priors for perigastric veins segmentation and varicosity quantification

  • Journal: Neurocomputing

  • Year: 2025

Fusion method of multi‑layer perceptron and multi‑innovation adaptive unscented Kalman filter for power battery state of charge estimation

  • Journal: Journal of Energy Storage

  • Year: 2025

Maneuver strategy recognition technology for enemy combat aircraft based on Bayesian deep learning

  • Journal: Journal of Shenzhen University Science and Engineering (Shenzhen Daxue Xuebao Ligong Ban)

  • Year: 2025

Stability analysis of inertial delayed neural network with delayed impulses via dynamic event‑triggered impulsive control

  • Journal: Neurocomputing

  • Year: 2025

Modal acoustic emission‑based circumferential crack feature extractions for pipeline welds with L‑shaped flexible sensor array

  • Journal: Nondestructive Testing and Evaluation

  • Year: 2025

Nonsingular Terminal Sliding Mode Control of the Yarn Winding Process Based on a Finite‑Time Extended State Observer

  • Journal: IEEE Access

  • Year: 2025

Conclusion

Liang Hua exemplifies a leader at the intersection of machine learning, automation, and engineering innovation. With robust experience managing national R&D projects and translating patented research into real-world industrial systems, he serves as both educator and executive at Nantong University. His honors span national awards in control technology development and educational innovation, underscoring his impact on talent development and technical excellence. Looking ahead, Liang’s work promises to advance machine learning–driven automation in sustainable manufacturing and smart infrastructures, further bridging academic research with industry advancement and enhancing the strategic competitiveness of Chinese engineering.

Md. Rajib Munshi | Computational Methods | Computational Science Excellence Award

Mr. Md. Rajib Munshi | Computational Methods | Computational Science Excellence Award

European University of Bangladesh | Bangladesh

Md. Rajib Munshi is an Assistant Professor and Acting Head of the Department of Physics at European University of Bangladesh (EUB). With a profound dedication to educational excellence and intellectual curiosity, he works towards cultivating creativity and higher-order thinking skills among students, promoting a deep understanding of physics and related fields. Through his strong academic background and impactful research, he continues to inspire and contribute to the advancement of scientific knowledge.

👨‍🎓Profile

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Early Academic Pursuits 🎓

Md. Rajib Munshi began his academic journey at Jagannath University (JnU), Dhaka, where he earned his Bachelor of Science (B.Sc. Hon’s) and Master of Science (M.Sc.) in Physics with excellent grades. His academic foundation was further strengthened at the Bangladesh University of Engineering and Technology (BUET), where he is currently completing his M.Phil. in Physics, with a CGPA of 3.83. This demonstrates his commitment to excellence in learning and his passion for the field of computational science.

Professional Endeavors 💼

Md. Munshi’s career at European University of Bangladesh began in 2015, where he has held various positions in the Department of Physics, including Lecturer, Senior Lecturer, and currently as Assistant Professor. His teaching experience spans over 9 years, demonstrating his long-standing commitment to educating the next generation of physicists. He also serves as a Research Collaborator at the Nanotechnology Research Laboratory (NRL) at BUET, contributing his expertise to cutting-edge research in nanomaterials.

Contributions and Research Focus 🔬

Md. Munshi’s research focus lies in computational material science, with a particular emphasis on the use of Density Functional Theory (DFT) to predict the electronic, optical, mechanical, thermodynamic, and photocatalytic properties of various inorganic compounds. His research has led to significant advancements in the study of materials like In(X)O2, RaZrO3, and GaAgO2, with implications for applications in photocatalysis, optical devices, and energy storage.

Impact and Influence 🌍

Md. Munshi’s work is highly regarded in the scientific community, with numerous publications in high-impact journals such as Computational Condensed Matter, RSC Advances, and Heliyon. His research has garnered attention due to its innovative nature and potential real-world applications. Through his collaborative research, he has contributed to advancing material science, particularly in the areas of nanotechnology and photocatalysis.

Academic Citations 📚

His research contributions have made a significant impact, evidenced by the number of citations his work has received. With a consistent record of publishing in prestigious journals, Md. Munshi’s research is contributing to the global understanding of nanomaterials and their applications in various industries. His studies provide the foundation for future innovations in electronic and energy-efficient technologies.

Research Skills 🔍

Md. Munshi is well-versed in advanced computational methods such as DFT simulations, which he utilizes to explore and predict the properties of materials at the atomic and molecular level. His technical expertise in these computational techniques has made him an essential contributor to research that focuses on material design for photocatalysis and electronic applications. His ability to blend theoretical insights with practical research methods is one of his key strengths.

Teaching Experience 📖

With over 9 years of teaching experience, Md. Munshi has played an instrumental role in shaping the academic environment at European University of Bangladesh. His teaching philosophy is centered around nurturing critical thinking, problem-solving skills, and fostering intellectual curiosity in his students. He is known for creating an engaging learning environment that not only imparts knowledge but also encourages students to explore new concepts in physics and related fields.

Legacy and Future Contributions 🚀

Looking forward, Md. Rajib Munshi is determined to further expand his research into multidisciplinary areas, including the integration of machine learning with computational material science. His goal is to continue advancing the field of computational science and make lasting contributions to the development of sustainable materials for energy and environmental solutions. As a leader and mentor, he aspires to inspire future researchers to explore innovative solutions for the challenges of tomorrow.

Publications Top Notes

Structural, optical, magnetic, and enhanced antibacterial properties of hydrothermally synthesized Sm-incorporating α-MoO3 2D-layered nanoplates

  • Authors: SK Sen, MR Munshi, A Kumar, AA Mortuza, MS Manir, MA Islam, …
    Journal: RSC Advances
    Year: 2022

Structural, electronic, optical and thermodynamic properties of AlAuO2 and AlAu0.94Fe0.06O2 compounds scrutinized by density functional theory (DFT)

  • Authors: MZ Rana, MR Munshi, M Al Masud, MS Zahan
    Journal: Heliyon
    Year: 2023

Theoretical insights on geometrical, mechanical, electronic, thermodynamic and photocatalytic characteristics of RaTiO3 compound: a DFT investigation

  • Authors: MS Zahan, MR Munshi, MZ Rana, M Al Masud
    Journal: Computational Condensed Matter
    Year: 2023

Theoretical investigation of structural, electronic, optical and thermoelectric properties of GaAgO2 based on Density Functional Theory (DFT): Two approaches

  • Authors: MR Munshi, MZ Rana, SK Sen, MRA Foisal, MH Ali
    Journal: World Journal of Advanced Research and Reviews
    Year: 2022

Electronic, thermodynamic, optical and photocatalytic properties of GaAgO2 and AlAgO2 compounds scrutinized via a systemic hybrid DFT

  • Authors: MR Munshi, SK Sen, MZ Rana
    Journal: Computational Condensed Matter
    Year: 2023

First principles prediction of geometrical, electronic, mechanical, thermodynamic, optical and photocatalytic properties of RaZrO3 scrutinized by DFT investigation

  • Authors: MR Munshi, M Al Masud, M Rahman, MR Khatun, MF Mian
    Journal: Computational Condensed Matter
    Year: 2024