Mohammad Afikuzzaman | Mathematics | Best Faculty Award

Dr. Mohammad Afikuzzaman | Mathematics | Best Faculty Award

Adelaide University | Australia

Dr. Mohammad Afikuzzaman’s research profile reflects a strong and sustained contribution to applied mathematical modeling and computational simulations, with particular emphasis on fluid mechanics, magnetohydrodynamics, nanofluids, heat and mass transfer, and multicomponent alloy diffusion. The scholarly output includes 18 indexed documents with 318 citations and an h-index of 11, alongside broader visibility on Google Scholar reporting 431 total citations and an h-index of 12 (August 2025). The body of work comprises 22 peer-reviewed journal articles, 7 international conference presentations (oral and poster), and 4 book chapters/books, with an additional 6 manuscripts currently under review. Research contributions demonstrate methodological rigor, advanced numerical and theoretical modeling, and practical relevance, supported by extensive collaboration with industry, government, and academic partners to promote innovation and interdisciplinary engagement. Publications appear in high-impact journals such as Scripta Materialia, Journal of Phase Equilibria and Diffusion, Nanoscale Advances, Journal of Molecular Liquids, and Arabian Journal for Science and Engineering, highlighting both depth and breadth of scientific influence.

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Featured Publications

Chao Wang | Computer Science | Research Excellence Award

Mr. Chao Wang | Computer Science | Research Excellence Award

North China University of Technology | China

Mr. Chao Wang is an accomplished researcher whose work spans vehicular networks, IoT security, blockchain mechanisms, and food engineering applications, reflecting a multidisciplinary impact. With 809 citations, an h-index of 12, and 16 i10-index publications, he has established a strong scholarly presence supported by numerous high-impact journal articles and competitive conference papers. His research contributions include advanced blockchain-based frameworks for secure communication, innovative privacy-preserving data-sharing models, anomaly detection algorithms for intelligent vehicles, and distributed system security. He has also co-authored influential studies on anti-glycation mechanisms, food bioactive compounds, and cellular protection. His publications from 2021 to 2025 demonstrate consistent output across IEEE Transactions, Future Generation Computer Systems, Food Biomacromolecules, and other reputable venues. His work on collaborative quality control, CAN bus anomaly detection, distributed GAN attack resistance, and multi-party payment channels represents notable advancements in secure systems. He has also contributed to reviews on AGEs inhibition, IoV security, NGS applicability, and blockchain-enabled vehicular applications. Beyond technical innovation, his research extends to biologically focused studies that explore glycation inhibition, fermentation mechanisms, and cellular oxidative protection. Across domains, his scholarly contributions continue to advance secure intelligent systems, data integrity solutions, and interdisciplinary applications, reinforcing his role as a productive and influential researcher.

Profiles : Orcid | Google Scholar

Featured Publications

Bao, C., Niu, Z., He, B., Li, Y., Han, S., Feng, N., Huang, H., Wang, C., Wang, J., & others. (2025). A novel high‐protein composite rice with anti‐glycation properties prepared with crushed rice flour, whey protein and lotus seed proanthocyanidins. Food Biomacromolecules, 2(1), 23–34.

He, Y., Zhou, Z., Wu, B., Xiao, K., Wang, C., & Cheng, X. (2024). Game-theoretic incentive mechanism for collaborative quality control in blockchain-enhanced carbon emissions verification. IEEE Transactions on Network Science and Engineering.

Li, Q., Xiao, K., Yi, C., Yu, F., Wang, W., Rao, J., Liu, M., Zhang, L., Mu, Y., Wang, C., & others. (2024). Inhibition and mechanism of protein nonenzymatic glycation by Lactobacillus fermentum. Foods, 13(8), 1183.

Wang, C., Xu, X., Xiao, K., He, Y., & Yang, G. (2024). Traffic anomaly detection algorithm for CAN bus using similarity analysis. High-Confidence Computing, 4(3), 100207.

Xiao, K., Li, J., He, Y., Wang, X., & Wang, C. (2024). A secure multi-party payment channel on-chain and off-chain supervisable scheme. Future Generation Computer Systems, 154, 330–343.

Feng, N., Feng, Y., Tan, J., Zhou, C., Xu, J., Chen, Y., Xiao, J., He, Y., Wang, C., & others. (2023). Inhibition of advance glycation end products formation, gastrointestinal digestion, absorption and toxicity: A comprehensive review. International Journal of Biological Macromolecules, 249, 125814.

Wu, Q., Kong, Y., Liang, Y., Niu, M., Feng, N., Zhang, C., Qi, Y., Guo, Z., Xiao, J., & others. (2023). Protective mechanism of fruit vinegar polyphenols against AGEs-induced Caco-2 cell damage. Food Chemistry: X, 19, 100736.

Wang, C., Liu, X., He, Y., Xiao, K., & Li, W. (2023). Poisoning the competition: Fake gradient attacks on distributed generative adversarial networks. In Proceedings of the IEEE International Conference on Mobile Ad Hoc and Smart Systems.

Xu, X., Wang, L., Wang, C., Zhu, H., Zhao, L., Yang, S., & Xu, C. (2023). Intelligent connected vehicle security: Threats, attacks and defenses. Journal of Information Science & Engineering, 39(6).

Wang, C., Jiang, L., He, Y., Yang, G., & Xiao, K. (2023). Age of information-based channel scheduling policy in IoT networks under dynamic channel conditions. In China Conference on Wireless Sensor Networks (pp. 88–98).

Zhou, J., Wang, C., Luo, M., Liu, X., Xu, X., & Chen, S. (2023). Spatial-temporal based multi-head self-attention for in-vehicle network intrusion detection system. SSRN 4581213.

Wang, C., Wang, S., Cheng, X., He, Y., Xiao, K., & Fan, S. (2022). A privacy and efficiency-oriented data sharing mechanism for IoTs. IEEE Transactions on Big Data, 9(1), 174–185.

Li, Q., Li, L., Zhu, H., Yang, F., Xiao, K., Zhang, L., Zhang, M., Peng, Y., Wang, C., & others. (2022). Lactobacillus fermentum as a new inhibitor to control advanced glycation end-product formation during vinegar fermentation. Food Science and Human Wellness, 11(5), 1409–1418.

Wu, Q., Liang, Y., Kong, Y., Zhang, F., Feng, Y., Ouyang, Y., Wang, C., Guo, Z., & others. (2022). Role of glycated proteins in vivo: Enzymatic glycated proteins and non-enzymatic glycated proteins. Food Research International, 155, 111099.

Wang, C., Cheng, X., Li, J., He, Y., & Xiao, K. (2021). A survey: Applications of blockchain in the Internet of Vehicles. EURASIP Journal on Wireless Communications and Networking, 2021(1), 77.

Xu, S., Chen, X., Wang, C., He, Y., Xiao, K., & Cao, Y. (2021). A lattice-based ring signature scheme to secure automated valet parking. In Wireless Algorithms, Systems, and Applications.

Michele Buzzicotti | Physics and Astronomy | Best Paper Award

Dr. Michele Buzzicotti | Physics and Astronomy | Best Paper Award

University of Rome Tor Vergata | Italy

Dr. Michele Buzzicotti is an accomplished physicist whose research bridges turbulence modeling, data-driven fluid dynamics, and machine learning applications in complex systems. He has authored 39 scientific documents, accumulating 703 citations across 513 records with an h-index of 17, reflecting his consistent scientific influence in computational physics and atmospheric modeling. Holding a Ph.D. in Physics from the University of Rome Tor Vergata (2017), his doctoral work focused on the effects of Fourier mode reduction in turbulence. Currently serving as a tenure-track faculty member at the Department of Physics, University of Rome Tor Vergata, Dr. Buzzicotti has held visiting research appointments at the Technical University of Eindhoven and the University of Rochester. His projects, such as the €1.3 million Italian Ministry of Research–funded “Data-driven and Equation-based Tools for Complex Turbulent Flows,” showcase his leadership in advancing AI-integrated turbulence studies. His publications in Nature Machine Intelligence, Physical Review Letters, and Europhysics Letters highlight pioneering contributions to stochastic modeling and generative AI for fluid dynamics. A reviewer for top-tier journals and a member of EUROMECH and APS, Dr. Buzzicotti continues to shape the future of theoretical and applied turbulence research through innovative interdisciplinary approaches.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Li, T., Biferale, L., Bonaccorso, F., Buzzicotti, M., & Centurioni, L. (2025). Stochastic reconstruction of gappy Lagrangian turbulent signals by conditional diffusion models. Communications Physics.

Freitas, A., Um, K., Desbrun, M., Buzzicotti, M., & Biferale, L. (2025). Solver-in-the-loop approach to closure of shell models of turbulence. Physical Review Fluids.

Martin, J., Lübke, J., Li, T., Buzzicotti, M., Grauer, R., & Biferale, L. (2025). Generation of cosmic-ray trajectories by a diffusion model trained on test particles in 3D magnetohydrodynamic turbulence. The Astrophysical Journal Supplement Series.

Li, T., Tommasi, S., Buzzicotti, M., Bonaccorso, F., & Biferale, L. (2024). Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence. International Journal of Multiphase Flow.

Khatri, H., Griffies, S. M., Storer, B. A., Buzzicotti, M., Aluie, H., Sonnewald, M., Dussin, R., & Shao, A. (2024). A scale‐dependent analysis of the barotropic vorticity budget in a global ocean simulation. Journal of Advances in Modeling Earth Systems.

Li, T., Biferale, L., Bonaccorso, F., Scarpolini, M. A., & Buzzicotti, M. (2024). Synthetic Lagrangian turbulence by generative diffusion models. Nature Machine Intelligence.

Li, T., Lanotte, A. S., Buzzicotti, M., Bonaccorso, F., & Biferale, L. (2023). Multi-scale reconstruction of turbulent rotating flows with generative diffusion models. Atmosphere, 15(1), 60.

Storer, B. A., Buzzicotti, M., Khatri, H., Griffies, S. M., & Aluie, H. (2023). Global cascade of kinetic energy in the ocean and the atmospheric imprint. Science Advances.

Li, T., Buzzicotti, M., Biferale, L., Bonaccorso, F., Chen, S., & Wan, M. (2023). Multi-scale reconstruction of turbulent rotating flows with proper orthogonal decomposition and generative adversarial networks. Journal of Fluid Mechanics.

Buzzicotti, M., Storer, B. A., Khatri, H., Griffies, S. M., & Aluie, H. (2023). Spatio‐temporal coarse‐graining decomposition of the global ocean geostrophic kinetic energy. Journal of Advances in Modeling Earth Systems.

Hafiz Mohammad Hasan Babu | Computer Science | Lifetime Achievement in Books Award

Prof. Hafiz Mohammad Hasan Babu | Computer Science | Lifetime Achievement in Books Award

University of Dhaka | Bangladesh

Author Profile

Scopus

Orcid

Google Scholar

Early Academic Pursuits

Prof. Hafiz Mohammad Hasan Babu began his academic journey in the realm of computer science and engineering with a strong foundation from the Brno University of Technology, Czech Republic, where he completed his M.Sc. with a focus on logic network automation. His curiosity for advanced computational systems took him to the Kyushu Institute of Technology in Japan, where he earned his Ph.D. in Computer Science and Electronics. His doctoral work concentrated on data structures for multiple-output functions and their applications in VLSI CAD, under the guidance of Prof. Dr. Tsutomu Sasao. These formative years laid the groundwork for his future innovations in quantum computing, reversible logic, and nanotechnology.

Professional Endeavors

Prof. Hasan Babu's academic career spans several decades and institutions, notably the University of Dhaka, where he served in various capacities, including as professor in the departments of Computer Science and Engineering, and Robotics and Mechatronics Engineering. His early academic roles also included positions at Khulna University. He has been deeply involved in curriculum development, student mentorship, and departmental leadership. Beyond teaching, he also contributed significantly as a research supervisor and played a critical role in developing the academic and research culture of computer science in Bangladesh.

Contributions and Research Focus

A prolific researcher, Prof. Hasan Babu has made groundbreaking contributions in the fields of quantum computing, reversible logic design, DNA computing, and machine learning applications in healthcare and agriculture. His interdisciplinary research integrates electronics, artificial intelligence, and biological systems. His most recent works delve into quantum biocomputing and nanotechnology, as evidenced by his multi-volume publications with Springer Nature and CRC Press. He has also authored numerous peer-reviewed articles on topics such as cardiovascular disease detection using mobile AI, air quality forecasting, and toxic substance identification in fruits through deep learning.

Accolades and Recognition

Prof. Hasan Babu has received numerous prestigious awards recognizing his excellence in research and scholarly contributions. These include the Dhaka University Research Excellence Recognition, the UGC Gold Medal, and the Dr. M. O. Ghani Memorial Gold Medal from the Bangladesh Academy of Sciences. His biography has been featured in “Who's Who in the World, USA.” He has also received international fellowships such as the Japanese Government Scholarship, the DAAD Fellowship from Germany, and a Czechoslovakian Government Scholarship, marking his global academic influence.

Impact and Influence

Throughout his academic life, Prof. Hasan Babu has significantly influenced the fields of computer science, electronics, and artificial intelligence. His innovations in reversible logic and DNA computing have shaped research methodologies and applications in both academia and industry. He has been instrumental in advancing computational methods that address real-world problems, particularly in environmental monitoring, biomedical diagnostics, and agricultural automation. His role as a mentor to doctoral and master’s students further amplifies his impact on the next generation of scholars.

Legacy and Future Contributions

Prof. Hasan Babu’s extensive scholarly contributions, particularly in the emerging domains of quantum AI and biocomputing, position him as a thought leader in futuristic technologies. His upcoming publications promise to offer new paradigms in nanotechnology and molecular-level computing. As he continues to mentor new researchers and expand the boundaries of interdisciplinary science, his legacy will be defined by his relentless pursuit of innovation and his dedication to fostering a globally relevant research ecosystem.

List of Book Publications



Books Published in 2025:

1. Quantum AI Emerging Technologies: Driving Innovation and Shaping the Future of Nanotechnology, Volume I, Springer Nature, Singapore.

2. Quantum AI Emerging Technologies: Driving Innovation and Shaping the Future of Nanotechnology, Volume II, Springer Nature, Singapore.

3. Quantum Biocomputing in Quantum Biology, Volume I, Springer Nature, Singapore.

4. Quantum Biocomputing in Quantum Biology, Volume II, Springer Nature, Singapore.

Book Published in 2024:
5. DNA Logic Design: Computing with DNA, World Scientific Publishing Co Pte Ltd., Singapore.

Books Published in 2023:
6. Multiple-Valued Computing in Quantum Molecular Biology, Volume I, CRC Press, USA.
7. Multiple-Valued Computing in Quantum Molecular Biology, Volume II, CRC Press, USA.

Books Published in 2022:
8. VLSI Circuits and Embedded Systems, CRC Press, USA.
9. Control Engineering Theory and Applications (Co-authored with Md. Jahangir Alam, Guoqing Hu, and Huazhong Xu), CRC Press, USA.

Books Published in 2020:
10. Quantum Computing: A Pathway to Quantum Logic Design, 2nd Edition, IOP Publishers, Bristol, UK.
11. Reversible and DNA Computing, Wiley Publishers, UK.



Journal Publications


Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach
Authors: Samrat Kumar Dey, Khandaker Mohammad Mohi Uddin, Arpita Howlader, Md Mahbubur Rahman, Hafiz Md Hasan Babu, Nitish Biswas, Umme Raihan Siddiqi, Badhan Mazumder
Journal: Neuroscience Informatics (Elsevier)
Year: 2025


Empowering early detection: A web-based machine learning approach for PCOS prediction
Authors: Md. Mahbubur Rahman, Ashikul Islam, Forhadul Islam, Mashruba Zaman, Md Rafiul Islam, Md Shahriar Alam Sakib, Hafiz Md Hasan Babu
Journal: Journal of Informatics in Medicine (Elsevier)
Year: 2024


Computer vision based deep learning approach for toxic and harmful substances detection in fruits
Authors: Abdus Sattar, Md. Asif Mahmud Ridoy, Aloke Kumar Saha, Hafiz Md. Hasan Babu, Mohammad Nurul Huda
Journal: Heliyon (Cell Press)
Year: 2024


A Comprehensive Approach to Detecting Chemical Adulteration in Fruits Using Computer Vision, Deep Learning, and Chemical Sensors
Authors: Abdus Sattar, Md. Asif Mahmud Ridoy, Aloke Kumar Saha, Hafiz Md. Hasan Babu, Mohammad Nurul Huda
Journal: Journal of Intelligent Systems with Applications (Elsevier)
Year: 2024


A Voice assistive mobile application tool to detect cardiovascular disease using machine learning approach
Authors: Khandaker Mohammad Mohi Uddin, Samrat Kumar Dey, Hafiz Md Hasan Babu
Journal: Biomedical Materials & Devices (Springer US)
Year: 2024


Conclusion

Prof. Hafiz Mohammad Hasan Babu embodies the spirit of academic excellence and innovation in computer science. With a career rich in scholarly output, international collaborations, and student mentorship, he has become a beacon of transformative research and a visionary in integrating quantum theory with computational systems. His work continues to influence the scientific community both in Bangladesh and globally, promising continued advancements in technology and applied sciences.

Soopil Kim | Computer Science | Best Researcher Award

Dr. Soopil Kim | Computer Science | Best Researcher Award

Daegu Gyeongbuk Institute of Science and Technology | South Korea

Author Profile

Scopus

Orcid

Early Academic Pursuits 🎓

Dr. Soopil Kim's academic journey began with a Bachelor of Engineering in Robotics and Mechatronics Engineering from Daegu Gyeongbuk Institute of Science & Technology (DGIST), where he graduated Cum Laude. He continued his studies at DGIST, pursuing a Master’s and Ph.D. in the same field, focusing on medical image segmentation. His research during these years emphasized label-efficient segmentation models and limited pixel-level annotation, laying a strong foundation for his future work in deep learning and computer vision.

Professional Endeavors 💼

Dr. Kim's career has seen significant milestones, including a role as a Visiting Student at Stanford University's CNSLAB under the supervision of Prof. Kilian M. Pohl and Ehsan Adeli. Currently, he is a Post-Doctoral Research Fellow at the Medical Image & Signal Processing Lab (MISPL) at DGIST, where he works under Prof. Sang Hyun Park. His professional trajectory reflects a commitment to advancing the field of computer vision through innovative research and collaboration.

Contributions and Research Focus 🔬

Dr. Kim’s research is at the forefront of deep learning and computer vision. His work addresses the challenges of image segmentation with partially labeled datasets by developing federated learning strategies and few-shot segmentation techniques. His notable contributions include the creation of a medical image segmentation model that integrates meta-learning and bi-directional recurrent neural networks, a semi-supervised segmentation model based on uncertainty estimation, and a transductive segmentation model for industrial imaging. These advancements aim to improve the efficiency and accuracy of image segmentation processes.

Accolades and Recognition 🏆

Dr. Kim has received several awards that highlight his exceptional contributions to the field. Notably, he was ranked 3rd among 40 teams in the SNUH Sleep AI Challenge in 2021 and was honored with the Outstanding Student Award from the Department of Robotics and Mechatronics Engineering at DGIST in 2022. In 2024, he was recognized at the KCCV Oral/Poster Presentation Doctoral Colloquium for his work on label-efficient segmentation models.

Impact and Influence 🌍

Dr. Kim's research has made a significant impact on the field of computer vision, particularly in the area of image segmentation. His innovative approaches to handling partially labeled datasets and federated learning have the potential to advance both academic research and practical applications in medical imaging and beyond. His work on few-shot learning and uncertainty-aware models addresses critical challenges in the field, contributing to more robust and adaptable segmentation solutions.

Legacy and Future Contributions 🚀

As Dr. Kim continues his research, his focus on improving segmentation models and developing new methodologies promises to shape the future of computer vision. His commitment to exploring federated learning and few-shot learning techniques will likely drive further innovations in the field, offering solutions to complex challenges and enhancing the accuracy of image analysis across various applications.

 

Publications 📘


📄Few-shot anomaly detection using positive unlabeled learning with cycle consistency and co-occurrence features
Authors: Sion An, Soopil Kim, Philip Chikontwe, Jiwook Jung, Hyejeong Jeon, Jaehong Kim, Sang Hyun Park
Journal: Expert Systems with Applications
Year: 2024


📄Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets
Authors: Soopil Kim, Haejun Park, Myeongju Kang, Kilian M. Pohl, Sang Hyun Park
Journal: Medical Image Analysis
Year: 2024


📄FedNN: Federated learning on concept drift data using weight and adaptive group normalizations
Authors: Myeongju Kang, Soopil Kim, Kwang-Hyun Jin, Kilian M. Pohl, Sang Hyun Park
Journal: Pattern Recognition
Year: 2024


📄Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
Authors: Soopil Kim, Sion An, Philip Chikontwe, Kilian M. Pohl, Sang Hyun Park
Conference: Proceedings of the AAAI Conference on Artificial Intelligence
Year: 2024


📄Uncertainty-aware semi-supervised few shot segmentation
Authors: Soopil Kim, Philip Chikontwe, Sion An, Sang Hyun Park
Journal: Pattern Recognition
Year: 2023


 

Bipin Kumar | Earth and Planetary Sciences | Best Researcher Award

Dr. Bipin Kumar | Earth and Planetary Sciences | Best Researcher Award

Indian Institute of Tropical Meteorology | India

Author Profile

Scopus

Early Academic Pursuits

Dr. Bipin Kumar's academic journey began with a Bachelor of Science in Physical Sciences from the University of Allahabad in 1995, followed by a Master of Science in Mathematics from IIT Kanpur in 1998. He then pursued an MS in Research (Mathematics) at the National University of Singapore in 2006, focusing on computational methods for phase-field models. His quest for deeper expertise led him to earn a Ph.D. in Computing from Dublin City University in 2009, with a thesis on high-performance computing for multiphase fluid flows.

Professional Endeavors

Dr. Kumar’s career spans over two decades, encompassing roles in academia, research institutions, and industry. His notable positions include:

  • Scientist and Research Guide at IITM, Pune, India.
  • Associate Professor at Savitribai Phule Pune University.
  • Visiting Scientist at NCAR, Boulder, USA, and Visiting Faculty at McGill University, Canada.
  • Associate Faculty at the International Center for Theoretical Sciences, Bengaluru, India.
  • Scientist at Max-Planck-Institute for Meteorology, Germany.

He has held various teaching and research positions, contributing to advancements in high-performance computing, data science, and atmospheric physics.

Contributions and Research Focus

Dr. Kumar's research is centered around:

  • Data Science and AI/ML: Developing parallel Python routines and deep learning algorithms for weather forecasting, data downscaling, fire forecasting, and more.
  • Atmospheric Physics: Studying cloud droplet and aerosol dynamics using DNS.
  • High-Performance Computing (HPC): Enhancing parallel code for CFD problems, 3D visualization, and parallel I/O optimization.
  • Numerical Linear Algebra: Creating parallel algorithms for solving large linear systems of equations.

Accolades and Recognition

Dr. Kumar has received several prestigious awards:

  • DCU Teaching Excellence Nominee Award (2008)
  • Microsoft Postgraduate Research Scholarship (Ireland, 2007-08)
  • DCU Dean’s Connect Scholarship (Ireland, 2006-09)
  • NUS Research Scholarship (Singapore, 2004-06)
  • CSIR Senior Research Fellowship (India, 2004)

Impact and Influence

Dr. Kumar’s work has significantly influenced fields such as HPC, data science, and atmospheric physics. His contributions to developing computational methods for complex fluid flows and forecasting systems have advanced our understanding of cloud dynamics and weather patterns. His research has impacted both theoretical and practical aspects of meteorology and data analysis.

Legacy and Future Contributions

Dr. Kumar aims to broaden his impact through continued research and teaching. By leveraging his expertise in HPC, data science, and cloud microphysics, he aspires to address critical challenges in earth science and contribute to the development of innovative solutions for climate and environmental issues.

 

   Publications

  • Deep learning-based bias correction of ISMR simulated by GCM
    Authors: Sumanta Chandra Mishra Sharma, Bipin Kumar, Adway Mitra, Subodh Kumar Saha
    Journal: Atmospheric Research
    Year: 2024

 

  • Harnessing deep learning for forecasting fire-burning locations and unveiling PM2.5 emissions
    Authors: Gaikwad, S., Kumar, B., Yadav, P.P., Rao, S.A., Ghude, S.D.
    Journal: Modeling Earth Systems and Environment
    Year: 2024

 

  • Machine learning based quantification of VOC contribution in surface ozone prediction
    Authors: Kalbande, R., Kumar, B., Maji, S., Rathore, D.S., Beig, G.
    Journal: Chemosphere
    Year: 2023

 

  • On the modern deep learning approaches for precipitation downscaling
    Authors: Kumar, B., Atey, K., Singh, B.B., Nanjundiah, R.S., Rao, S.A.
    Journal: Earth Science Informatics
    Year: 2023

 

  • A modified deep learning weather prediction using cubed sphere for global precipitation
    Authors: Singh, M., Acharya, N., Patel, P., Nanjundiah, R.S., Niyogi, D.
    Journal: Frontiers in Climate
    Year: 2023

 

 

 

 

 

 

Sasank V.V.S | Computer Science | Best Researcher Award

Assist Prof Dr. Sasank V.V.S | Computer Science | Best Researcher Award

K L University | India

Author Profile

Scopus

Early Academic Pursuits

Dr. Sasank V.V.S. exhibited a strong academic foundation from the outset. He completed his secondary education at Jassver English Medium School in 2007 with a First Class distinction, scoring 72.66%. He continued to excel in his Intermediate studies at Mega Junior College, graduating in 2009 with an 84.1% mark, also achieving First Class. His academic journey progressed to higher education at Gitam Institute of Technology, GITAM University, where he obtained his B.Tech in Information Technology in 2013 with a CGPA of 8.15, earning a Distinction. He further advanced his education with an M.Tech in Computer Science and Technology from the same institution, graduating in 2016 with a remarkable 9.11 CGPA, securing the top rank in his department. Dr. Sasank completed his Ph.D. at K.L. University in 2023, marking a significant milestone in his academic career.

Professional Endeavors

Dr. Sasank has a rich professional background in both academia and industry. He began his teaching career as a Teaching Assistant in the CSE Department at Gitam University from October 2015 to April 2016. He then served as an Assistant Professor at the Lendi Institute of Engineering & Technology, VIZIANAGARAM, from June 2016 to April 2017. Following this, he joined Anil Neerukonda Institute of Technology & Sciences (ANITS) as an Assistant Professor and Placement Officer from June 2017 to April 2019. He has been affiliated with K L University since July 2019, initially in the CSE Department and later in the CSIT Department, where he also served as the ERP Registration In-charge. His teaching repertoire includes subjects such as DBMS, Software Engineering, Computer Architecture & Organization, Term Paper, UI/UX Design, and DevOps.

Contributions and Research Focus

Dr. Sasank's research primarily focuses on advanced topics in computer science and engineering. His areas of interest include brain tumor classification, real-time traffic management using IoT and machine learning techniques, and the evolution of modern women in literature. He has published a significant number of papers in reputed journals, including  SCI papers and several Scopus-indexed articles. His notable publications include works on hybrid deep neural networks, automatic tumor growth prediction, and brain tumor classification using modified kernel-based softplus extreme learning machines. Additionally, he has guided numerous B.Tech and M.Tech project batches, contributing to the academic growth of his students.

Accolades and Recognition

Dr. Sasank has received several accolades for his academic and research achievements. He was the top ranker in his M.Tech program at Gitam University in 2016. He has published 18 papers, including SCI, Scopus, and WOS-indexed journals, and has contributed to two book chapters. His innovative research has led to the publication of two patents: one on real-time traffic management using IoT and machine learning techniques, and another on the evolution of modern women in Manju Kapur’s novels. Additionally, he has earned global certifications, including Google Associate Cloud Engineer and AWS Cloud Practitioner, and has presented his research at various international conferences.

Impact and Influence

Dr. Sasank's contributions to the field of computer science and engineering have had a significant impact on both academic and practical applications. His research on brain tumor classification and real-time traffic management has potential real-world implications, advancing the fields of medical imaging and smart city technologies. As an educator, he has influenced many students through his teaching and mentorship, guiding them in their academic and research endeavors.

Legacy and Future Contributions

Dr. Sasank's ongoing research and academic activities are expected to leave a lasting legacy in the field of computer science and engineering. His contributions to brain tumor classification and IoT-based traffic management are poised to influence future research and development in these areas. As he continues to publish and present his work, Dr. Sasank is likely to inspire and mentor the next generation of engineers and researchers, ensuring continued innovation and excellence in his field.

 

Notable Publications

Prostate cancer classification using adaptive swarm Intelligence based deep attention neural network 2024

Effective Segmentation and Brain Tumor Classification Using Sparse Bayesian ELM in MRI Images 2023

Hybrid deep neural network with adaptive rain optimizer algorithm for multi-grade brain tumor classification of MRI images 2022 (14)

An automatic tumour growth prediction based segmentation using full resolution convolutional network for brain tumour 2022 (27)

Hate Speech & Offensive Language Detection Using ML &NLP 2022 (4)

 

 

 

 

Manyu Xiao | Mathematics | Best Researcher Award

Assoc Prof Dr. Manyu Xiao | Mathematics | Best Researcher Award

Northwestern Polytechnical University | China

Author Profile

Scopus

Early Academic Pursuits

Dr. Manyu Xiao began her academic journey at Three Gorges University in China, where she earned her Bachelor of Science in Mathematics and Applied Mathematics in 2003. She then pursued a Master of Science in Computational Mathematics at Northwestern Polytechnical University (NPU), China, from 2003 to 2006. Her master’s research focused on fast and parallel algorithms for information processing under the guidance of Quanyi Lu. Subsequently, Dr. Xiao completed her Doctorate in Engineering Science from Université de Technologie de Compiègne (UTC) in France in 2010, where she specialized in multidisciplinary optimization with model reduction and parallel computing. Her doctoral advisors were Piotr Breitkopf and Rajan Filomeno Colho.

Professional Endeavors

Dr. Xiao has a rich professional background, marked by significant positions and research projects. Since March 2012, she has been an associate professor in the Department of Applied Mathematics at NPU. Prior to this, she was a post-doctoral fellow at UTC, France, from 2010 to 2011, where she worked on constrained POD model reductions. Her career includes various visiting scholar positions, such as at Université Libre de Bruxelles, Belgium, the University of Alabama in Huntsville, USA, Queen Mary University of London, University of Oxford, University of Cambridge, and University of Helsinki, Finland.

Contributions and Research Focus

Dr. Xiao’s research interests are diverse and impactful, focusing on efficient numerical methods, model reduction, parallel computing, mechanical optimization, and surrogate-based topology optimization. She has led and participated in numerous research projects, including those funded by the National Natural Science Foundation of China and the Natural Science Foundation of Shaanxi Province. Her projects cover areas such as dynamic multi-fidelity reduction for topology optimization, large-scale stress-constrained topology optimization, and the design of lightweight functional graded core structures for thermal protection systems.

Accolades and Recognition

Dr. Xiao’s contributions have been widely recognized. She received the Best Paper Award at the 6th Australian Conference on Computational Mechanics in 2023, and the third prize for outstanding academic paper by the Shaanxi province in 2022. In 2020, she was honored as an International Rising Star by the International Association for Computational Mechanics. Additionally, she has been recognized multiple times by Northwestern Polytechnical University for her teaching excellence and her role as a thesis supervisor.

Impact and Influence

Dr. Xiao’s work has significantly influenced the field of applied mathematics and computational mechanics. Her research on large-scale structural topology optimization and model reduction has advanced the capabilities of computational methods in engineering. Her numerous publications in high-impact journals and contributions to books underscore her influence. Furthermore, her teaching reforms and MOOC projects at NPU have enhanced the internationalization and quality of mathematics education.

Legacy and Future Contributions

Dr. Xiao’s legacy lies in her pioneering research and dedication to education. Her work continues to inspire advancements in computational mathematics and engineering optimization. Future contributions are anticipated from her ongoing and upcoming research projects, including those on the construction of kinetic models for composite materials and large-scale structural design optimization. Her commitment to developing internationalized training modes for mathematical talents ensures that her influence will extend to future generations of scholars and engineers.

 

Notable Publications

📄 Article: Primal–dual on-the-fly reduced-order modeling for large-scale transient dynamic topology optimization

  • Authors: Xiao, M., Ma, J., Gao, X., Cauvin, L., Villon, P.
  • Journal: Computer Methods in Applied Mechanics and Engineering
  • Year: 2024
  • Citations: 0

📄 Article: Towards a data-driven paradigm for characterizing plastic anisotropy using principal components analysis and manifold learning

  • Authors: Jin, J., Cauvin, L., Raghavan, B., Dutta, S., Xiao, M.
  • Journal: Computational Materials Science
  • Year: 2024
  • Citations: 0

📄 Article: Stress-constrained topology optimization using approximate reanalysis with on-the-fly reduced order modeling

  • Authors: Xiao, M., Ma, J., Lu, D., Raghavan, B., Zhang, W.
  • Journal: Advanced Modeling and Simulation in Engineering Sciences
  • Year: 2022
  • Citations: 1

📄 Article: A Gaussian process regression-based surrogate model of the varying workpiece dynamics for chatter prediction in milling of thin-walled structures

  • Authors: Yang, Y., Yang, Y., Xiao, M., Wan, M., Zhang, W.
  • Journal: International Journal of Mechanical System Dynamics
  • Year: 2022
  • Citations: 7

📄 Article: Accelerating Large-scale Topology Optimization: State-of-the-Art and Challenges

  • Authors: Mukherjee, S., Lu, D., Raghavan, B., Xiao, M., Zhang, W.
  • Journal: Archives of Computational Methods in Engineering
  • Year: 2021
  • Citations: 54