Jinyuan Mao | Molecular Dynamics Simulations | Research Excellence Award

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Dr. Jinyuan Mao | Molecular Dynamics Simulations | Research Excellence Award

BYD Auto Industry Company Ltd | China

Dr. Jinyuan Mao is a Senior Simulation Engineer whose work bridges computational materials science and advanced machine-learning methodologies to accelerate innovation in modern engineering materials. He earned his PhD in Soft Matter Science in 2024 from the South China University of Technology, where he received rigorous training in molecular modeling and multiscale simulation under expert mentorship. After completing his doctoral studies, he joined BYD Auto Industry Company Ltd. in collaboration with Fudan University, contributing to cutting-edge research that integrates atomic-scale simulations with data-driven modeling strategies. His professional experience spans the study of mechanical behavior in metals, composites, and soft matter, with an emphasis on understanding deformation mechanisms, reliability, and performance enhancement. Dr. Mao’s research interests include machine-learning-accelerated materials discovery, molecular dynamics simulations, mesoscale modeling, and the development of predictive frameworks for complex material systems. He has contributed to high-impact publications in computational materials science and has been recognized for scientific excellence through academic awards and research achievements during his doctoral years. Committed to advancing simulation-driven design, he continues to explore innovative computational approaches that bridge theory and application, supporting the development of next-generation materials for industrial and technological advancement.

Profiles: Orcid | Google Scholar

Featured Publications

Liu, J., Mao, J., Wang, B., Wang, Q., Zhang, N., & Pan, S. (2025). Study on the mechanical properties and critical temperature of FeNiCrMn alloy using MD-ML-MA framework. Journal of Molecular Modeling.

Mao, J., Zhou, J., & Liu, H. (2024). One-pot strategy for the preparation of nanoparticles grafted with bimodal polymers: An in-silico insight. Composites Science and Technology.

Mao, J., Jia, X.-M., Zhang, G., & Zhou, J. (2024). Excluded volume of slide rings in single-chain polyrotaxane. Macromolecules.

Li, C.-X., Mao, J.-Y., Li, S.-J., Wang, Y., & Liu, H. (2023). A long chain-induced depletion effect for abnormal grafting in the preparation of bimodal bidisperse polymer-grafted nanoparticles. Physical Chemistry Chemical Physics.

Mao, J., Hu, Z., Hu, J., Zhu, X., & Xiong, H. (2019). A Density Functional Theory (DFT) study of the acyl migration occurring during lipase-catalyzed transesterifications. International Journal of Molecular Sciences.

Harishchander | Biosensing | Best Researcher Award

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Dr. Harishchander | Biosensing | Best Researcher Award

Amrita Vishwa Vidyapeetham | India

Dr. Harishchander is an accomplished academic and researcher in bioengineering and bioinformatics, recognized for his strong interdisciplinary expertise and impactful scientific contributions. He holds a B.Tech and M.Tech in Bioinformatics and earned his Ph.D. in Bioengineering, building a solid foundation in computational biology, systems biology, and biomedical data analysis. With extensive experience as a researcher, keynote speaker, session chair, judge, and committee member across numerous national and international scientific events, he has actively contributed to advancing research communities. His research interests span computational bioengineering, biomarker analytics, machine learning applications in biology, therapeutic peptide analysis, biological network modeling, microRNA-based regulation, infectious diseases, and integrative genomics. Dr. Harishchander has authored multiple Scopus-indexed publications and received numerous prestigious honors, including the AIRA Award, Outstanding Scientist Award, Best Innovation Award, Young Investigator Award, Best Researcher Award, Distinguished Professor Award, and several Best Paper Awards across diverse domains. His achievements also include global recognition in photography and excellence awards for academic service, innovation, peer reviewing, and scientific contributions. Driven by scientific curiosity and multidisciplinary thinking, he continues to contribute to the advancement of bioengineering research and remains committed to fostering innovation, collaboration, and impactful knowledge dissemination.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Baseer, K. K., Reddy, N. R., Kumar, T. S., Anandaram, H., & Panthakkan, A. (2025). AI, IoT, and Cognitive Computing for Mental Health Enhancement and Object Detection.

Anandaram, H., Shreenidhi, K. S., Joshi, K., Sidhu, K. S., Kiruthigha, R., Singh, V., & Tripathi, A. (2025). A Case Study on Resource Management Industrial IoT Application.

Ashok, P., Bharati, A. P., Velpula, S., Krishna, K. M., Sridevi, S. L., Patchala, S., Gopinath, S., & Anandaram, H. (2025). Integrated 5G and Edge Computing Framework for Low-Latency IoT Applications.

Anandaram, H., Sidhu, K. S., Kiruthigha, R., Pathak, N., Sudhakar, A., Sharma, N., & Joshi, K. (2025). Analysis of the Human Brain Using Basic Computer Interface (BCI) System.

Pathak, N., Sharma, N., Sahoo, D. R., Alam, F., Anandaram, H., & Joshi, K. (2025). Fundamentals Approached Towards Artificial Neural Networks in Brain Computer Interface.

Kausar, M. T. A., Anandaram, H., Sidhu, K. S., Pathak, N., Sudhakar, A., Sharma, N., & Joshi, K. (2025). Introduction of a Cutting-Edge Technology Neural Networks Organization.

Sindhuja, L. S., Shreenidhi, K. S., Anandaram, H., Chandra, S., & Hari, B. S. (2025). Optimizing Composite Material Selection Using the Artificial Bee Colony Algorithm.

Dr. Abhijeet Das | Water Resource Engineering | Distinguished Scientist Award

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Dr. Abhijeet Das | Water Resource Engineering | Distinguished Scientist Award

C.V. Raman Global University (CGU), Bhubaneswar | India

Dr. Abhijeet Das is an accomplished water resource engineer specializing in watershed hydrology, hydrological modeling, water quality control, and climate change impact assessment. He holds a B.Tech in Civil Engineering from ABIT College (BPUT, Rourkela), an M.Tech in Water Resource Engineering from the College of Engineering and Technology (BPUT, Bhubaneswar), and a Ph.D. in Water Resource Engineering from C.V. Raman Global University, Bhubaneswar. With extensive experience in GIS, remote sensing, and geoinformatics, he has contributed significantly to hydrological modeling, flood and drought assessment, and the integration of machine learning techniques for water resource management. Dr. Das has held positions as a GIS Analyst, Teaching Assistant, and Research Consultant, leading spatial data management, hydrological and hydraulic modeling, flood risk analysis, and multi-criteria decision support. His expertise spans numerical modeling, optimization techniques, artificial neural networks, and fuzzy logic for environmental and water systems. He has successfully guided GIS projects, developed dashboards for real-time monitoring, and collaborated with government agencies for watershed and crop management planning. Dr. Das’s research focuses on sustainable water management, climate resilience, and the Food-Energy-Water (FEW) nexus. His contributions have advanced environmental impact assessment and decision-making tools. He continues to drive innovation in water resource engineering and hydrological research, fostering a data-driven approach to sustainable resource management.

Profile: Scopus

Featured Publications

Drinking water resources suitability assessment in Brahmani River, Odisha, based on pollution index of surface water utilizing advanced water quality methods. Scientific Reports.

An optimization-based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance. Discover Environment.

A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in Mahanadi River Basin, Odisha, India. Discover Sustainability.

Water quality assessment and geospatial techniques for the delineation of surface water potential zones: A data-driven approach using machine learning models. Desalination and Water Treatment

Evaluation and prediction of surface water quality status for drinking purposes using an integrated water quality indices, GIS approaches, and machine learning techniques. Desalination and Water Treatment