Shah Dad Hasil | Computational Methods | Research Excellence Award

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Mr. Shah Dad Hasil | Computational Methods | Research Excellence Award

University of Electronic Science and Technology of China | China

Mr. Shah Dad Hasil is an emerging researcher in computer science currently pursuing graduate studies at the University of Electronic Science and Technology of China. His academic work lies at the intersection of artificial intelligence, bioinformatics, and computational drug discovery, where he focuses on designing intelligent computational tools to accelerate biomedical research. His work integrates machine learning algorithms, molecular docking techniques, and molecular dynamics simulations to evaluate molecular interactions and predict the biological activity of potential therapeutic compounds. A major focus of his current research is the development of AI-based predictive models targeting Trypanosoma cruzi, the parasite responsible for Chagas disease, aiming to support the discovery of new antiviral and antiparasitic drug candidates. Hasil has also demonstrated interdisciplinary research interests, contributing to studies in renewable energy technologies and computational cryptography. His research outputs have appeared in several peer-reviewed publications, including articles published in the Journal of Marine Science and Engineering. In addition to his research contributions, he has strong programming expertise in Python and C++ and practical experience working with deep learning frameworks such as TensorFlow and PyTorch. His long-term academic goal is to pursue a Ph.D. and advance AI-driven methodologies that address critical challenges in drug discovery and biomedical science.

 

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Featured Publications

Dr. Adarsh Kumar Shukla | Computational Methods | Young Scientist Award

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Dr. Adarsh Kumar Shukla | Computational Methods | Young Scientist Award

Dr. Bhimrao Ambedkar Government Medical College | India

Dr. Adarsh Kumar Shukla is a motivated and results-driven professional with expertise in Environmental Toxicology, Clinical Bioinformatics, NGS Data Analysis, Pathway Analysis, Computer-Aided Drug Design, Structural Biology, Metabolomics, Computational Biology, and Computational Chemistry. Currently serving as Scientist-B at the Multidisciplinary Research Unit, Dr. Bhimrao Ramji Ambedkar Government Medical College, Kannauj, he has prior experience as a Scientist and Research Associate in Paediatric Cardiac Research at Sri Sathya Sai Sanjeevani Research Foundation, as well as academic and project coordination roles in biotechnology and medicinal plant research. He has coordinated national-level academic events and completed FDPs focused on healthcare innovation. Dr. Shukla has contributed significantly to Health-, Plant-, and Food-Informatics research, presented papers at several conferences, and holds a granted Indian patent for the development of crab-apple-based jelly sheets. His research interests encompass structural bioinformatics, human genomics, and nutritional biotechnology, with strong proficiency in in-silico tools, laboratory instrumentation, and data analysis. Skilled in programming languages such as R, Biopython, and Bioperl, he combines computational and experimental approaches to address complex biomedical challenges.sics.

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Featured Publications

Shukla, A. K., & Kukshal, P. (2025). Computational simulations aided prioritization of genomic targets for congenital heart disease (CHD) against developmental toxicity. Reproductive Toxicology, 108, 108940.

Shukla, A. K., & Kumar, A. (2025). A chemoinformatics study to prioritization of anticancer orally active lead compounds of pearl millet against adhesion G protein-coupled receptor. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 334, 125960.

Jha, R. K., Shukla, A. K., Kumari, A., & Kumar, A. (2025). Virtual screening of potential orally active anti-bacterial compounds of finger millet. Vegetos, 38(3), 1237–1248.

Jain, S., Shukla, A. K., Deepika, Panwar, S., Kumari, A., Yadav, A. K., & Kumar, A. (2024). Revolutionizing disease treatment through bioengineered probiotics and glucagon‐like peptide 1 (GLP‐1) based strategies: A path towards effective cures. Food Bioengineering, 2024, 1.

Panwar, S., Pal, S., Shukla, A. K., Kumar, A., & Sharma, P. K. (2024). Identification of micronutrient deficiency related miRNA and their targets in Triticum aestivum using bioinformatics approach. Ecological Genetics and Genomics, 31, 100236.

jianzhao Wu | Computational Methods | Best Researcher Award

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Assoc. Prof. Dr. jianzhao Wu | Computational Methods | Best Researcher Award

Huazhong University of Science and Technology | China

Jianzhao Wu  is a renowned mechanical engineer specializing in laser manufacturing technologies and sustainability-focused research. His academic and professional journey has spanned several prestigious institutions, including the National University of Singapore (NUS) and Huazhong University of Science and Technology (HUST), where he obtained his PhD in Mechanical Engineering. Wu has made significant contributions to the fields of laser-arc hybrid welding, laser additive manufacturing, and optimization algorithms for manufacturing processes. His works have been widely recognized and published in high-impact journals.

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Early Academic Pursuits 🎓

Wu’s academic career began with a Master’s degree in Mechanical Engineering at Ningbo University, where he explored cutting performance and chip control in Polycrystalline Diamond (PCD) tools. His research interests were initially shaped around tool performance and tribology, paving the way for his later work in laser processing and sustainability. His excellence in research was quickly recognized, with awards such as the National Scholarship and the “Self-strengthening Star” Nomination Award for university students.

Professional Endeavors 💼

Wu’s professional development saw a significant leap when he joined Huazhong University of Science & Technology (HUST), where he worked on cutting-edge research in digital manufacturing and environmentally sustainable technologies. As a Joint Ph.D. student at NUS, Wu collaborated on international projects with Manchester University and Loughborough University to promote low-carbon laser processing technologies. His research involves carbon emission modeling, multi-objective optimization using machine learning algorithms, and laser surface treatment.

Contributions and Research Focus 🔬

Wu’s research focuses on several key areas, including:

  • Low-carbon Laser Manufacturing: He is particularly interested in laser-arc hybrid welding, laser cleaning, and laser additive manufacturing, seeking to optimize these processes for environmental sustainability while maintaining high mechanical properties.
  • Optimization Algorithms: Wu uses machine learning, deep learning models, and convolutional neural networks (CNN) to develop advanced algorithms that optimize the efficiency of manufacturing processes and reduce energy consumption.
  • Tribology and Chip Control: He has conducted pioneering studies in chip breaking mechanisms for PCD tools, particularly in turning operations, focusing on tribological properties and surface textures for improved tool performance.

Research Skills 🔧

Wu has developed expertise in the following key areas:

  • Laser Processing Technologies: Mastery in laser-arc hybrid welding and additive manufacturing techniques for sustainability.
  • Optimization Algorithms: Skilled in data-driven models, ensemble learning, and meta-modeling to optimize manufacturing systems.
  • Carbon Emission Modeling: Advanced techniques to measure and reduce carbon emissions in laser-based processes.
  • Tribology and Surface Engineering: In-depth understanding of tribological properties and laser-textured surfaces for enhanced tool life and performance.

Teaching Experience 📚

Wu has mentored and supervised several undergraduate and postgraduate students in their research projects. His teaching experience at both HUST and NUS has allowed him to guide students in areas related to laser technologies, tribology, and sustainable manufacturing. His involvement in both teaching and research enables him to integrate theoretical knowledge with practical applications, preparing students for the evolving demands of the manufacturing industry.

Legacy and Future Contributions 🔮

Wu is poised to make substantial contributions to sustainable manufacturing and green technologies in the coming years. His work in laser-based technologies has already influenced the global manufacturing landscape, and he continues to explore innovative solutions for low-carbon processes.

Publications Top Notes

Multi-Objective Parameter Optimization of Fiber Laser Welding Considering Energy Consumption and Bead Geometry

  • Authors: Jianzhao Wu, Ping Jiang, Chaoyong Zhang, et al.
    Journal: IEEE Transactions on Automation Science and Engineering
    Year: 2021

Data-driven Multi-objective Optimization of Laser Welding Parameters of 6061-T6 Aluminum Alloy

  • Authors: Jianzhao Wu
    Journal: Journal of Physics: Conference Series
    Year: 2021

Tribological Properties of Bronze Surface with Dimple Textures Fabricated by the Indentation Method

  • Authors: Jianzhao Wu, Aibing Yu, Qiujie Chen, et al.
    Journal: Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology
    Year: 2020

Study on Position of Laser Cladded Chip Breaking Dot on Rake Face of HSS Turning Tool

  • Authors: Jianzhao Wu, Chenchun Shi, Aibing Yu, et al.
    Journal: International Journal of Machine Tools and Manufacture
    Year: 2017

Comparisons of Tribological Properties Between Laser and Drilled Dimple Textured Surfaces of Medium Carbon Steel

  • Authors: Jianzhao Wu, Aibing Yu, Chenchun Shi, et al.
    Journal: Industrial Lubrication and Tribology
    Year: 2017