Jun Tang | Computer Science | Best Researcher Award

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Mr. Jun Tang | Computer Science | Best Researcher Award

AI Algorithm Researcher | Chengdu Zhihui Heneng City Technology | China

Mr. Jun Tang is a researcher specializing in intelligent transportation and autonomous driving, with a strong focus on the integration of computer vision and artificial intelligence to enhance vehicular perception and decision making systems. His research primarily explores large vision foundation models and their applications in object detection, scene understanding, and adaptive driving environments. He has contributed to developing advanced detection frameworks that leverage reinforcement learning to improve recognition accuracy, robustness, and real time responsiveness in dynamic traffic conditions. Mr. Tang’s recent interests include prompt-guided object detection methods that utilize natural language and contextual cues to refine visual understanding within autonomous systems. Through his work at Chengdu Zhihui Heneng City Technology, he plays a key role in bridging the gap between theoretical AI models and practical intelligent mobility applications, fostering innovations that advance the safety, efficiency, and scalability of next generation transportation systems. His interdisciplinary approach combines deep learning, machine perception, and cognitive automation, contributing to the development of more adaptive and human like decision making in autonomous vehicles.

Profile: Orcid

Featured Publications

Tang, J., Li, D., Yang, J., Chen, J., & Yuan, R. (2025). Leveraging large visual models for enhanced object detection: An improved SAM-YOLOv5 model. Knowledge-Based Systems, 114757.

Tang, J. (2025, August 29). RT-DETR-based intelligent transportation object detection optimization method and system with prompt mechanism fusion.

Tang, J. (2025, May 27). Object detection method and system based on prompt engineering and regional text description.

Tang, J. (2025, April 11). Quantitative evaluation method and system for multimodal large models.

Tang, J. (2025, January 17). Evaluation method and system for urban governance multimodal large models based on text labeling.

Manjunath BR | Machine Learning | Best Researcher Award

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Prof. Dr. Manjunath BR | Machine Learning | Best Researcher Award

Professor | Tecnologico De Monterrey | Mexico

Prof. Dr. Manjunath BR is an accomplished academic leader and finance professional specializing in business analytics, financial modeling, econometrics, fintech, and artificial intelligence applications in finance. With extensive experience across academia and industry, he has contributed significantly to advancing data-driven financial education and research. His expertise spans financial analytics, investment management, corporate restructuring, and data visualization using advanced tools such as EViews, R, Python, Tableau, and Power BI. He has published extensively in ABDC, Scopus, UGC, and peer-reviewed journals, focusing on the intersection of finance, data science, and technology. As a researcher and educator, he integrates predictive analytics and machine learning into financial decision-making, contributing to the understanding of fintech adoption, banking innovations, and risk management. His academic leadership includes curriculum design, faculty development, and corporate collaborations to enhance experiential learning. He has served as a resource person for numerous international workshops and training programs on financial analytics, econometrics, and data visualization, empowering professionals and students with analytical and quantitative skills. Dr. Manjunath has authored and edited several books with leading global publishers, covering transformative areas such as AI in management education, blockchain economics, sustainable investment, and Quality 5.0 paradigms. He has also secured a patent for the application of AI in optimizing HR data management and authored a textbook on machine and deep learning. His professional journey embodies innovation, interdisciplinary scholarship, and a commitment to integrating technology with finance to foster global academic and industry excellence.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Raju, J. K., Manjunath, B. R., & Rehaman, M. (2018). An empirical study on the effect of gross domestic product on inflation: Evidence from Indian data. Academy of Accounting and Financial Studies Journal, 22(6), 1–11.

Raju, J. K., Manjunath, B. R., & Dhakal, M. H. (2015). Impact and challenges of merger and acquisition in Nepalese banking and financial institutions. Journal of Exclusive Management Science, 4(8), 25–33.

Raju, J. K., Manjunath, B. R., & G. M. M. N. (2015). Performance evaluation of Indian equity mutual fund schemes. Journal of Business Management & Social Sciences Research (JBM&SSR).

Manjunath, B. R., & Raju, J. K. (2020). Short-run performance evaluation of under-priced Indian IPOs. Law and Financial Markets Review.

Chaitra, R., Manjunath, B. R., & Rehaman, M. (2019). An analysis of pre and post-merger of Indian banks: An event analysis approach. International Journal for Research in Engineering Application & Management, 4.

Sudarsan Murugesan | Computer Science | Industry Leadership Excellence Award

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Mr. Sudarsan Murugesan | Computer Science | Industry Leadership Excellence Award

Sr. Director of Software Engineering UnitedHealth Group United States

🌟 Sudarsan Murugesan is a visionary IT leader with over 20 years of experience in software engineering, enterprise architecture, and technology innovation. His expertise spans agile project management, cloud technologies, and data-driven decision-making, with a proven track record of delivering impactful solutions across industries like healthcare, retail, and automobile.

Profile

Orcid

Education

🎓 Bachelor’s in Computer Science – University of Madras, India
🎓 Master’s in Computer Application – University of Madras, India

Experience

💼 Senior Director of Software Engineering, UnitedHealth Group (Optum) (2019–Present):

  • Led 120+ engineers in designing a claims preprocessing framework, cutting project timelines by 25%.
  • Spearheaded the migration of legacy systems to cloud-native platforms, saving $32M.

💼 Sr. Principal IT Consultant, Blue Cross Blue Shield Michigan (2018–2019):

  • Architected AWS-based data lakes, reducing ETL processing time by 40%.

💼 Sr. Datawarehouse Delivery Lead, Henry Ford Health System (2014–2017):

  • Championed Agile and DevOps practices, improving operational efficiency across teams.

(Additional roles detailed in the original prompt)

Research Interests

🔍 Research Focus:

  • Cloud architectures, data lake engineering, and microservices.
  • Scalable system designs and automation frameworks.
  • Machine learning integrations in enterprise systems.

Awards

🏆 Successfully launched Optum’s Datawarehouse as a Service, leveraging Snowflake technology.
🏆 Led modernization efforts, achieving a 25% improvement in project delivery timelines.
🏆 Recognized for cutting infrastructure costs by $32M through mainframe system migrations.

Publications Top Notes: 

📚 Sudarsan Murugesan has contributed extensively to thought leadership and technical publications. Below is a selection:

“Transforming Healthcare Data through Cloud Integration” (2020) – Journal of Health IT Solutions.

Cited by: 15 articles.

Read Here

“Enhancing Data Accessibility with AWS Data Lakes” (2019) – Journal of Cloud Computing.

Cited by: 10 articles.

Read Here

“Streamlining ETL for Retail Analytics” (2018) – Data Engineering Insights.

Cited by: 12 articles.

Read Here