Nidhi Bhatia | Data-Driven Decision Making | Best Researcher Award

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Ms. Nidhi Bhatia | Data-Driven Decision Making | Best Researcher Award

Doctoral researcher at IIT DELHI, India

Nidhi Bhatia is a dedicated academician and researcher with a keen interest in marketing, sustainability, and social issues surrounding women’s health and hygiene. Her research focuses on understanding consumer behavior, social marketing strategies, and sustainability in business and education. With a strong background in teaching and research, she has contributed significantly to her field through publications, conferences, and collaborative projects. She is currently pursuing her Ph.D. in Marketing at the Department of Management Studies, IIT Delhi, under the supervision of Prof. Biswajita Parida.

Profile

Scopus

Education

Nidhi Bhatia has a strong academic foundation with diverse educational qualifications. She completed her 10th and 12th education from the CBSE board in 2001 and 2003, respectively. She earned a Bachelor of Science degree in Statistics, Mathematics, and Physics in 2006, followed by a Master of Science in Physics, Electronics, and Communication in 2008. She later pursued an MBA in Marketing and Human Resources in 2010. Her academic achievements also include qualifying for the National Eligibility Test (NET) in 2011. She enrolled in the Ph.D. program at IIT Delhi on December 28, 2019, with her research focusing on the antecedents and consequences of tabooness perception around women’s health and hygiene, with an expected submission by March 2025.

Experience

With over a decade of teaching experience, Nidhi Bhatia has served as an Assistant Professor at various institutions. She began her academic career at Radha Govind Engineering College (2010-2011) and later joined Meerut Institute of Engineering and Technology (2011-2016). From 2017 to 2019, she worked as a guest faculty member at the University of Petroleum and Energy Studies, Dehradun. Additionally, she has delivered Entrepreneurship Development Program (EDP) lectures at the National Institute for Entrepreneurship and Small Business Development (NIESBUD) in Noida and Dehradun. Her teaching expertise includes marketing, consumer behavior, and business strategy.

Research Interests

Nidhi Bhatia’s research interests span various aspects of marketing, consumer behavior, and sustainability. Her primary research focuses on social taboos related to women’s health and hygiene, aiming to understand their impact on consumer perception and marketing strategies. She is also interested in sustainable education, green business practices, and digital marketing. Through her research, she seeks to bridge the gap between societal norms and business practices, advocating for inclusive and sustainable policies in marketing and education.

Awards

Nidhi Bhatia has received several recognitions for her contributions to research and academia. She was awarded the Best Paper Award at the International Hybrid Conference on Diversity, Equity & Inclusion: Creating a Value-Based Sustainable Future at IILM Jaipur. Her work on sustainability, marketing, and women’s health has been widely recognized at various international conferences and academic forums.

Publications

Bhatia N., Parida B. (2024). “Taboo in Business and Society: Past, Present and Future.” Global Business Review (Under Review, Sage Publications).

Manchanda K., Bhatia N., Parida B. (2024). “Noteworthiness of Sustainable Education in Higher Education: A Qualitative Study.” European Journal of Education (Published, Wiley-Blackwell, Scopus Indexed, IF 4.5, Cite Score 2.8).

Bhatia N., Parida B. (2024). “Smart Cities’ Approaches to Menstrual Hygiene Management.” 8SCS-2024, IET Smart Cities Symposium, University of Bahrain (Accepted, IET Inspec, IEEE Xplore, Elsevier’s Scopus).

Bhatia N., Parida B. (2023). “Health is Wealth: Importance of Healthcare Management in Smart Cities.” 7SCS-2023, IET Smart Cities Symposium, University of Bahrain (Published, IET Digital Library, IEEE Xplore, Scopus Indexed).

Bhatia N., Manchanda K., Parida B. (2023). “Menstruation Effect on Well-Being: Exploring the Mediating Role of Physical Pain and Psychological Anguish.” ICISAS, Curtin University, Dubai (Published, Springer).

Bhatia N., Parida B. (2023). “Unleashing Societal Norms Around Women’s Health and Hygiene with Social Marketing Strategies.” ANZMAC 2023, New Zealand (Published, Conference Proceedings).

Bhatia N., Parida B. (2023). “Social Marketing Strategies Employed by NGOs to Bring Menstrual Health Awareness.” World Social Marketing Conference, Cali, Colombia (Published, Conference Proceedings).

Conclusion

Nidhi Bhatia is a passionate researcher and educator whose work focuses on marketing, consumer behavior, and social issues related to women’s health and sustainability. Through her research, she seeks to drive positive change in society by addressing taboos and encouraging sustainable business practices. With her extensive teaching experience and numerous publications, she continues to contribute significantly to the academic community. Her dedication to education and research underscores her commitment to fostering meaningful engagement and understanding in her field.

Ali Nawaz Sanjrani | Big Data Analytics | Global Data Science Award

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Dr. Ali Nawaz Sanjrani | Big Data Analytics | Global Data Science Award

Assistant Professor at University of Electronic Science and Technology of China, China

Dr. Ali Nawaz Sanjrani is a dedicated academician and scholar with over 18 years of interdisciplinary experience spanning research, teaching, and industrial project management. His expertise lies in reliability engineering, quality control, health and safety management, and complex machine diagnostics. As a professional with a strong commitment to excellence, Dr. Sanjrani has made significant contributions to engineering education and industrial advancements. His research primarily focuses on reliability monitoring, fault diagnosis, and the application of machine learning in predictive maintenance.

Profile

Orcid

Education

Dr. Sanjrani earned his Ph.D. in Mechanical Engineering from the University of Electronics Science and Technology, Chengdu, China, specializing in reliability monitoring, diagnostics, and prognostics of complex machinery. His doctoral coursework included advanced subjects such as Computer-Aided Manufacturing (CAM), Operations Research (OR), Reliability & Quality Engineering, Automation & Controls, and Finite Element Analysis (FEA). Prior to this, he completed his Master’s degree in Industrial Manufacturing Engineering from NED University of Engineering & Technology, Karachi, with a focus on lean manufacturing. He holds a Bachelor’s degree in Mechanical Engineering from QUEST, Nawabshah, where he developed a strong foundation in mechanical manufacturing and materials engineering.

Experience

Dr. Sanjrani has held several academic and industrial positions, reflecting his diverse skill set and leadership abilities. He served as an Assistant Professor at Mehran University of Engineering and Technology, SZAB Campus, from 2016 to 2020, where he was actively involved in teaching, research, and mentoring students. Additionally, he worked as a visiting faculty member at Indus University, Karachi. His industrial experience includes working as a Quality Assurance Engineer at Descon Engineering Works Limited, Lahore, where he managed quality control processes and implemented international quality management standards.

Research Interests

Dr. Sanjrani’s research interests are centered around machine learning applications in fault diagnosis and predictive maintenance, reliability analysis, and quality engineering. His work integrates artificial intelligence-driven methodologies to enhance the reliability and operational efficiency of high-speed train bearings, microgrids, and other complex mechanical systems. His research also extends to fluid dynamics, heat transfer, and smart manufacturing processes, emphasizing innovative approaches to industrial problem-solving.

Awards and Recognitions

Dr. Sanjrani has been recognized for his academic and research excellence through several prestigious awards. In 2024, he won the 3rd Prize for Academic Excellence at the University of Electronics Science and Technology, China. Additionally, he received the 3rd Prize for Performance Excellence at the same institution. He was also awarded the fully funded Chinese Government Scholarship (CSC) in 2020 for his Ph.D. studies. His industrial contributions have been acknowledged with appreciation certificates from Karachi Shipyard & Engineering Works (KSEW) for achieving multiple international certifications and successful project implementations.

Selected Publications

Sanajrani, A. N. (2025). “High-Speed Train Bearing Health Assessment Based on Degradation Stages Through Diagnosis and Prognosis by Using Dual-Task LSTM With Attention Mechanism.” Quality and Reliability Engineering International Journal, Wiley. DOI: https://doi.org/10.1002/qre.3757

Sanajrani, A. N. (2025). “High-Speed Train Wheel Set Bearing Analysis: Practical Approach to Maintenance Between End of Life and Useful Life Extension Assessment.” Results in Engineering, Elsevier. DOI: https://doi.org/10.1016/j.rineng.2024.103696

Sanajrani, A. N. (2025). “Advanced Dynamic Power Management Using Model Predictive Control in DC Microgrids with Hybrid Storage and Renewable Energy Sources.” Journal of Energy Storage, Elsevier. DOI: https://doi.org/10.1016/j.est.2024.114830

Sanajrani, A. N. (2024). “Dynamic Temporal LSTM-Seqtrans for Long Sequence: An Approach for Credit Card and Banking Accounts Fraud Detection in Banking Systems.” 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). DOI: 10.1109/ICCWAMTIP64812.2024.10873619

Sanajrani, A. N. (2024). “High-Speed Train Health Assessment Based on Degradation Stages and Fault Classification Using Dual-Task LSTM with Attention Mechanism.” The 6th International Conference on System Reliability and Safety Engineering. DOI: 10.1109/SRSE63568.2024.10772528 (EI & Scopus Indexed)

Sanajrani, A. N. (2024). “A C-band Sheet Beam Staggered Double Grating Extended Interaction Oscillator.” IEEE International Conference on Plasma Science (ICOPS). DOI: 10.1109/ICOPS58192.2024.10625809 (EI Indexed)

Sanajrani, A. N. (2023). “Bearing Health and Safety Analysis to Improve the Reliability and Efficiency of Horizontal Axis Wind Turbine (HAWT).” ESREL 2023, Southampton, UK (ISBN: 978-981-18-8071-1).

Conclusion

Dr. Ali Nawaz Sanjrani is a distinguished academic and industry professional with a strong research background in reliability engineering, artificial intelligence, and machine learning applications. His work significantly contributes to the fields of predictive maintenance, fault diagnostics, and industrial automation. With a proven record of academic excellence, numerous international publications, and substantial industrial experience, Dr. Sanjrani continues to drive innovation in engineering and technology. His dedication to bridging the gap between academia and industry ensures impactful contributions to the advancement of modern engineering solutions.

Daojun Liang | Time Series Analysis | Best Researcher Award

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Mr. Daojun Liang | Time Series Analysis | Best Researcher Award

PhD student | Shandong University | China

Mr. Daojun Liang is a dedicated PhD student at Shandong University with a solid academic background in computer science. He earned his BS from Taishan University in 2016 and his MS from Shandong Normal University in 2019. Currently pursuing his doctoral studies, Daojun has established himself as a researcher with expertise in uncertainty quantification, time series analysis, and large language models (LLM). Recognized for his independent research skills, Daojun has published several high-level papers in prestigious journals and serves as a reviewer for reputable organizations like IEEE, ACM, Elsevier, and Springer.

Profile

Scholar

Education

Daojun Liang began his academic journey with a Bachelor’s degree in Computer Science from Taishan University in 2016. Driven by a passion for innovation, he pursued a Master’s degree in Information Science and Engineering at Shandong Normal University, which he completed in 2019. His commitment to academic excellence led him to Shandong University, where he is currently advancing his research as a PhD candidate. His educational foundation has equipped him with the skills necessary for cutting-edge research and practical problem-solving in the fields of artificial intelligence and computational sciences.

Experience

Daojun’s research and professional experience demonstrate his versatility and expertise. He has contributed to several impactful projects, such as the development of intelligent vehicle networking technologies and the creation of advanced forecasting methods for 6G communication systems. His work with data-driven analysis and artificial intelligence for industrial applications highlights his ability to address complex challenges. Additionally, his role as an SCI reviewer for leading journals and collaborations with esteemed institutions like Fortiss GmbH and Shanghai Jiao Tong University reflect his strong academic and professional network.

Research Interests

Daojun’s research interests encompass long-term time series forecasting, uncertainty quantification, and the development of probabilistic inference methods. He focuses on analyzing intrinsic patterns in data to propose efficient and lightweight solutions. His work has implications for a variety of industries, including energy, manufacturing, and telecommunications. Daojun is also exploring the intersection of deep learning, natural language processing, and computer vision, ensuring his research remains at the forefront of innovation.

Awards and Recognitions

Daojun has been nominated for the Best Researcher Award in recognition of his outstanding contributions to academia and industry. His innovative methods for time series analysis and uncertainty quantification have not only been published in high-impact journals but have also been widely adopted in industrial applications. He has been honored as a reviewer for leading journals and conferences, which underscores his influence in the research community.

Publications

Liang, D., Zhang, H., Yuan, D., Zhang, M. (2024). Progressive Supervision via Label Decomposition: A Long-Term and Large-Scale Wireless Traffic Forecasting Method. Knowledge-Based Systems, 305, p.112622. (SCI Q1, IF = 7.2). Cited by 10.

Liang, D., Zhang, H., Yuan, D., Zhang, M. (2024). Periodformer: An Efficient Long-Term Time Series Forecasting Method Based on Periodic Attention. Knowledge-Based Systems, 304, p.112556. (SCI Q1, IF = 7.2). Cited by 8.

D. Liang, H. Zhang, D. Yuan, M. Zhang. (2024). Multi-Head Encoding for Extreme Label Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. (SCI Q1, IF = 20.8). Cited by 15.

Liang, D., Yang, F., Wang, X., et al. (2019). Multi-Sample Inference Network. IET Computer Vision, 13(6), 605-613. (SCI Q1, IF = 1.7). Cited by 12.

Liang, D., Zhang, H., Yuan, D., et al. (2025). DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting. ACM SigKDD 2025. Cited by 5.

Conclusion

Daojun Liang exemplifies the qualities of a modern researcher: innovative, dedicated, and collaborative. His contributions to uncertainty quantification, time series analysis, and large language models are reshaping academic and industrial practices. With numerous publications, collaborative projects, and a commitment to advancing knowledge, Daojun stands as a promising figure in his field.