Abhijeet Das | Engineering | Cutting Edge Scientific Achievement Award

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Dr. Abhijeet Das | Engineering | Cutting Edge Scientific Achievement Award 

Research Consultant at C.V. Raman Global University | India

Dr. Abhijeet Das is a distinguished researcher in Civil and Water Resources Engineering, known for his impactful contributions to hydrology, water quality, and environmental sustainability. With a career spanning academic teaching, consultancy, and international collaborations, he has demonstrated excellence in advancing methods of water quality assessment, climate change analysis, and sustainable water management practices. His interdisciplinary approach integrates hydrological modeling, GIS, and machine learning, creating solutions for both local and global water challenges. Dr. Abhijeet Das has authored books, research articles, and innovative patents, reflecting his vision of applying science and technology to address pressing environmental and societal needs.

Profile:

Orcid 

Education:

Dr. Abhijeet Das holds a Ph.D. in Water Resources Engineering from C.V. Raman Global University, where he specialized in hydrological modeling, water quality management, and GIS-based analysis. He earned his M.Tech in Water Resources Engineering from Biju Patnaik University of Technology, following his B.Tech in Civil Engineering from the same institution. His education built a strong foundation in watershed hydrology, climate change impact assessment, and environmental sustainability. Dr. Abhijeet Das’s academic achievements were marked by high distinctions, and he consistently pursued excellence in research-oriented projects. His progression from undergraduate to doctoral studies reflects a clear dedication to solving water resource challenges.

Experience:

Dr. Abhijeet Das has accumulated valuable experience as a researcher, consultant, and educator in civil and water resources engineering. He has taught undergraduate and postgraduate students at premier engineering institutions, nurturing the next generation of engineers and researchers. His consultancy roles allowed him to lead projects involving hydrological assessments, water resource management, and GIS-based solutions for river basin studies. Beyond national engagements, he has collaborated with international universities, working on projects related to wastewater management, climate impact analysis, and geoinformatics. Dr. Abhijeet Das’s combined academic and professional experience reflects his ability to bridge research, teaching, and practical applications.

Research Interests:

Dr. Abhijeet Das’s research interests encompass watershed hydrology, water resources engineering, and hydrological extremes such as droughts and floods. He focuses on climate change impact assessment and its influence on water security, emphasizing sustainable solutions. His expertise extends to the Food-Energy-Water nexus, applying machine learning, GIS, and remote sensing to optimize water management strategies. Dr. Abhijeet Das also contributes to simulation-optimization modeling, neural networks, and fuzzy logic applications for water quality control. His work highlights environmental impact assessment and sustainable management approaches, ensuring that his research directly supports global goals of resilience, sustainability, and resource conservation.

Awards and Honors:

Dr. Abhijeet Das has been honored with multiple awards for his innovative research and outstanding academic contributions. His papers on water quality assessment and GIS-based modeling have received recognition at prestigious national and international conferences. He has won best paper awards for advancing novel methodologies in multivariate statistical analysis and decision-making approaches for water quality management. Additionally, his poster presentations have earned accolades for their clarity, innovation, and societal relevance. Dr. Abhijeet Das’s recognition extends to his role as a valued reviewer and editor for renowned international journals, reflecting his leadership and credibility within the scientific and engineering community.

Publications:

Title: An optimization-based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance
Year of Publication: 2025

Title: 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
Citation: 2
Year of Publication: 2025

Title: Evaluation and prediction of surface water quality status for drinking purposes using an integrated water quality indices, GIS approaches, and machine learning techniques
Citation: 1
Year of Publication: 2025

Title: Bioplastics: a sustainable alternative or a hidden microplastic threat
Year of Publication: 2025

Title: Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning analysis
Year of Publication: 2025

Title: Geographical Information System–driven intelligent surface water quality assessment for enhanced drinking and irrigation purposes in Brahmani River, Odisha (India)
Citation: 6
Year of Publication: 2025

Title: Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India
Year of Publication: 2025

Conclusion:

Dr. Abhijeet Das has established himself as a dedicated scholar and innovator in water resources and environmental engineering. His research advances the understanding of hydrological systems, water quality monitoring, and climate change adaptation strategies. By integrating modern tools like GIS, machine learning, and multivariate statistical methods, he has proposed solutions with practical implications for sustainable development. Recognized with awards and widely published, Dr. Abhijeet Das represents a new generation of engineers combining academic rigor with societal impact. His body of work exemplifies excellence in research, making him a highly deserving candidate for recognition through this award nomination.

Rajeevan Arunthavanathan | Engineering | Best Researcher Award

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Dr. Rajeevan Arunthavanathan | Engineering | Best Researcher Award

Postdoctoral Researcher at Texas A&M University, United States.

🌍Dr. Rajeevan Arunthavanathan is a distinguished researcher and educator specializing in AI safety, process safety, and ICS cybersecurity. With a Ph.D. in Process Engineering and over a decade of academic and industrial experience, he has developed groundbreaking methods for risk evaluation and safety in critical infrastructures. His prolific publication record includes high-impact journals and book chapters on AI-human conflict, machine learning applications, and process fault diagnosis. Dr. Arunthavanathan has contributed significantly to curriculum development, student mentorship, and project management in academia and industry, positioning himself as a leader in the intersection of AI and process safety.

Profile👤

Education 🎓

🎓Dr. Arunthavanathan completed his Ph.D. in Process Engineering at Memorial University, Canada, in 2022, focusing on AI-driven fault diagnosis in process systems. He earned his MSc in Microelectronics and Communication from Northumbria University, UK, in 2010, graduating with distinction, and a B.Eng. in Electrical and Electronics Engineering from the same institution in 2007. His academic mentors included renowned professors, under whom he honed expertise in AI, control systems, and microelectronics. Throughout his education, he demonstrated excellence through research on AI-human interaction and advanced microelectronics, laying the foundation for his impactful career.🧬🎓

Experience💼

🩺Dr. Arunthavanathan has extensive experience in academia and industry. At Texas A&M University, he researches AI safety and mentors graduate students. Previously, at C-CORE, Canada, he developed ML models for data noise cleaning and smart ice management. He served as a senior lecturer at SLIIT, Sri Lanka, revising engineering curricula to meet international accreditation standards. His industrial experience includes work as a trainee engineer at Perry Slingsby Systems, UK, where he contributed to advanced underwater surveillance systems. His teaching spans multiple institutions, offering courses in process safety, microelectronics, and programming, blending theory with practical applications.👨‍🔬🌍

Research Interests 🔬

🔬Dr. Arunthavanathan’s research lies at the nexus of AI safety, process safety, and industrial control systems (ICS) cybersecurity. He develops innovative models to evaluate AI efficiency and mitigate risks in human-AI collaboration. His work on fault diagnosis, risk assessment, and operational technology cybersecurity addresses pressing challenges in critical infrastructure. His focus extends to integrating machine learning for noise cleaning in data systems and applying AI in Industry 4.0 technologies. With a commitment to enhancing process safety and addressing cyber threats, his research bridges theoretical advancements with practical applications for safer industrial operations. 🌿🧪

Awards and Honors 🏆

🏆Dr. Arunthavanathan has received numerous accolades, including being named a Fellow of the School of Graduate Studies at Memorial University (2022). His MSc degree was conferred with distinction by Northumbria University (2010). He serves as an editor for leading journals like Sensors and AI and reviews manuscripts for high-impact publications, including IEEE Access. His professional memberships with IEEE and AIChE reflect his standing in the academic community. These achievements underscore his dedication to advancing AI, process safety, and engineering education through impactful research and professional service. 🏆🎉

Conclusion 🔚 

Dr. Rajeevan Arunthavanathan is a strong contender for the Best Researcher Award, given his impactful contributions to AI safety, process fault diagnosis, and industrial control systems. His expertise, combined with a commitment to education and industry applications, exemplifies the qualities of an outstanding researcher. Recognizing his achievements will inspire further advancements in safety and AI-driven solutions for critical infrastructure.

Publications Top Notes 📚

An analysis of process fault diagnosis methods from safety perspectives

Authors: R. Arunthavanathan, F. Khan, S. Ahmed, S. Imtiaz

Citations: 126

Year: 2021

A deep learning model for process fault prognosis

Authors: R. Arunthavanathan, F. Khan, S. Ahmed, S. Imtiaz

Citations: 120

Year: 2021

Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique

Authors: R. Arunthavanathan, F. Khan, S. Ahmed, S. Imtiaz, R. Rusli

Citations: 76

Year: 2020

Autonomous fault diagnosis and root cause analysis for the processing system using one-class SVM and NN permutation algorithm

Authors: R. Arunthavanathan, F. Khan, S. Ahmed, S. Imtiaz

Citations: 52

Year: 2022

Industry 4.0-based process data analytics platform

Authors: T.R. Wanasinghe, M.G. Don, R. Arunthavanathan, R.G. Gosine

Citations: 10

Year: 2022

Machine Learning for Process Fault Detection and Diagnosis

Authors: R. Arunthavanathan, S. Ahmed, F. Khan, S. Imtiaz

Citations: 9

Year: 2022

Vehicle monitoring controlling and tracking system by using Android application

Authors: A. Rajeevan, N.K. Payagala

Citations: 8

Year: 2016

Artificial intelligence–Human intelligence conflict and its impact on process system safety

Authors: R. Arunthavanathan, Z. Sajid, F. Khan, E. Pistikopoulos

Citations: 7

Year: 2024

Process safety 4.0: Artificial intelligence or intelligence augmentation for safer process operation?

Authors: R. Arunthavanathan, Z. Sajid, M.T. Amin, Y. Tian, F. Khan, E. Pistikopoulos

Citations: 7

Year: 2024

Statistical approaches and artificial neural networks for process monitoring

Authors: M. Alauddin, R. Arunthavanathan, M.T. Amin, F. Khan

Citations: 6

Year: 2022

 

Jingxin Zhang | Engineering | Best Researcher Award

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Jingxin Zhang | Engineering | Best Researcher Award

Lecturer at Southeast University, China.

Dr. Jingxin Zhang is a Lecturer at Southeast University with expertise in fault detection, diagnosis, and process monitoring. She has made significant strides in photovoltaic power forecasting and the application of advanced data-driven methodologies in industrial processes. With a robust academic background and industry experience, Dr. Zhang has established a reputation for blending theoretical research with practical implementation. Her work on multimode process monitoring, which incorporates continual learning, sets her apart as an innovator in her field. Dr. Zhang’s research has resulted in 17 journal publications, nine patents, and significant collaborations with global institutions, enhancing her impact in both academia and industry. She has also led several research and industry projects, showcasing her ability to tackle real-world challenges effectively. Dr. Zhang is an active member of professional societies such as IEEE and the Chinese Association of Automation, further emphasizing her commitment to advancing her field.

📚 Profile

ORCID

Google scholar

🎓 Education

Dr. Jingxin Zhang’s academic journey began at Harbin Engineering University, where she earned her Bachelor of Engineering degree in Electrical Engineering and Automation. She furthered her studies with a Master’s degree in Control Science and Engineering from Harbin Institute of Technology. Her academic pursuits culminated in a Ph.D. in Control Science and Engineering from Tsinghua University, one of China’s most prestigious institutions. Throughout her education, Dr. Zhang focused on developing advanced methodologies for process control, fault detection, and automation, laying the foundation for her impactful career. Her strong educational background has enabled her to contribute to both theoretical advancements and practical applications in her field. As a lifelong learner, Dr. Zhang continues to push the boundaries of her expertise, applying her knowledge to cutting-edge research in data-driven process monitoring and photovoltaic power forecasting.

💼 Experience

Dr. Jingxin Zhang has accumulated extensive experience as both an academic and a researcher. Currently serving as a Lecturer at Southeast University, she has been involved in teaching and mentoring students while advancing her research in fault detection, process monitoring, and renewable energy forecasting. Her academic career has been complemented by active collaboration with industry leaders, making her research highly applicable to real-world industrial processes. Dr. Zhang has led multiple research projects, including five ongoing and four completed ones, focusing on advanced control systems and data-driven fault detection. With nine patents to her name and numerous publications in high-impact journals, her contributions are recognized both in China and internationally. Her involvement in consultancy projects has also strengthened her ability to transfer theoretical knowledge to practical, industry-relevant innovations, positioning her as a rising star in the field of control science and engineering.

🔬 Research Interests

Dr. Jingxin Zhang’s research interests span fault detection, diagnosis, process monitoring, and renewable energy forecasting. A core area of her work lies in developing advanced data-driven approaches to fault detection and diagnosis in complex systems. Her expertise extends to photovoltaic power forecasting, where she applies control science and machine learning techniques to predict energy outputs in industrial-scale solar power systems. Dr. Zhang is particularly interested in continual learning in process monitoring, a novel approach that enables systems to adapt to new data without losing previously learned knowledge. This research is groundbreaking in its ability to improve long-term system efficiency and stability in industries such as energy, manufacturing, and automation. Her work bridges the gap between theoretical advancements in data science and real-world industrial applications, making her research highly impactful in both academic and practical contexts.

🏆 Awards and Honors

Dr. Jingxin Zhang has been recognized for her contributions to control science and engineering through various awards and honors. While specific details of her awards have yet to be widely publicized, her achievements in research and industry collaborations have earned her a solid reputation within the academic community. She has led several prestigious research projects funded by national and provincial organizations, showcasing her leadership and innovation in process monitoring and fault detection. In addition to her research accomplishments, Dr. Zhang has been awarded nine patents, further solidifying her impact on industrial applications. As an active member of professional societies such as IEEE and the Chinese Association of Automation, Dr. Zhang is well-regarded by her peers and continues to be recognized for her groundbreaking work in data-driven approaches to process control. She remains committed to advancing her field and earning additional accolades as her career progresses.

🔚 Conclusion

 Jingxin Zhang is a strong candidate for the Best Researcher Award, particularly due to her innovative contributions to fault detection, data-driven approaches, and industry collaborations. With a focus on continual learning and industrial relevance, her research aligns well with the award’s criteria. Enhancing her academic visibility through more editorial roles and publications could further strengthen her application, positioning her as a leading researcher in her field.

Publications Top Notes 📚

Title: An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring
Author: J Zhang, H Chen, S Chen, X Hong
Year: 2017
Citation: 68

 

Title: Monitoring multimode processes: A modified PCA algorithm with continual learning ability
Author: J Zhang, D Zhou, M Chen
Year: 2021
Citation: 56

 

Title: A data-driven learning approach for nonlinear process monitoring based on available sensing measurements
Author: S Yin, C Yang, J Zhang, Y Jiang
Year: 2016
Citation: 47

 

Title: Multimode process monitoring based on fault dependent variable selection and moving window-negative log likelihood probability
Author: D Wu, D Zhou, J Zhang, M Chen
Year: 2020
Citation: 29

 

Title: Nonlinear process monitoring using a mixture of probabilistic PCA with clusterings
Author: J Zhang, M Chen, X Hong
Year: 2021
Citation: 28

 

Title: Continual learning for multimode dynamic process monitoring with applications to an ultra–supercritical thermal power plant
Author: J Zhang, D Zhou, M Chen, X Hong
Year: 2022
Citation: 24

 

Title: Self-learning sparse PCA for multimode process monitoring
Author: J Zhang, D Zhou, M Chen
Year: 2022
Citation: 16

 

Title: Adaptive cointegration analysis and modified RPCA with continual learning ability for monitoring multimode nonstationary processes
Author: J Zhang, D Zhou, M Chen
Year: 2022
Citation: 15

 

Title: Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation
Author: J Zhang, M Chen, H Chen, X Hong, D Zhou
Year: 2019
Citation: 14

 

Title: Continual learning-based probabilistic slow feature analysis for monitoring multimode nonstationary processes
Author: J Zhang, D Zhou, M Chen, X Hong
Year: 2022
Citation: 11