Nhue Do | Graph Analytics | Best Researcher Award

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Dr. Nhue Do | Graph Analytics | Best Researcher Award

Wake Forest University School of Medicine | United States

Author Profile

Scopus

Early Academic Pursuits

Dr. Nhue Do’s academic journey reflects an exceptional blend of medicine, surgery, and leadership. He earned his Doctor of Medicine degree from the University of Southern California, Keck School of Medicine, followed by an MBA from Johns Hopkins University’s Carey Business School, combining medical expertise with management acumen. His early postgraduate training at Harvard Medical School and Beth Israel Deaconess Medical Center exposed him to general surgery, transplantation, and cardiothoracic surgery, setting a strong foundation for a career dedicated to advanced surgical care and innovation.

Professional Endeavors

Dr. Do’s professional career demonstrates an impressive trajectory across leading academic and medical institutions. His appointments span Johns Hopkins University, Vanderbilt University Medical Center, and Advocate Children’s Hospital, where he currently serves as a Congenital Cardiothoracic Surgeon and Surgical Director of the Pediatric Mechanical Circulatory Support Program. His leadership roles, including Associate Vice Chair in Global Surgery at Vanderbilt, showcase his dedication not only to surgical excellence but also to advancing global health initiatives.

Contributions and Research Focus

Throughout his career, Dr. Do has contributed significantly to advancing congenital cardiothoracic surgery and pediatric heart transplantation. He has pioneered clinical protocols such as the use of fresh whole blood, ventricular assist devices, Impella technology, and SherpaPak in pediatric cardiac surgery. His research extends into transplantation, circulatory support devices, and surgical quality improvement. Additionally, his involvement in NIH-funded research and editorial responsibilities highlights his academic commitment to shaping the future of cardiothoracic surgery.

Impact and Influence

Dr. Do’s influence extends beyond the operating room. He has served on advisory boards, national review committees, and editorial boards, ensuring his expertise informs both clinical standards and future research directions. His mentorship in global health programs, leadership in surgical safety councils, and conference organization at national and international levels have amplified his voice in the field of pediatric and congenital heart surgery.

Academic Citations and Recognition

Dr. Do’s scholarly presence is reflected in his active role as a peer reviewer for leading journals such as The Journal of Thoracic and Cardiovascular Surgery and European Journal of Cardio-Thoracic Surgery. His academic honors-including multiple fellowships, scholarships, and leadership programs—underscore his recognition by top medical and surgical bodies worldwide. These achievements reflect his standing as both a clinician and a thought leader in cardiac surgery.

Legacy and Future Contributions

As a board-certified thoracic and congenital heart surgeon with extensive leadership and research experience, Dr. Do is poised to shape the next generation of surgical practice. His ongoing work in pediatric circulatory support and heart transplantation will likely influence future standards of care. Beyond clinical practice, his involvement in mentorship, global health initiatives, and surgical innovation ensures a legacy of advancing both patient outcomes and the broader healthcare landscape.

Conclusion

In summary, Dr. Nhue Do embodies the qualities of an outstanding clinician, educator, and researcher. His career reflects a rare integration of surgical excellence, academic rigor, and global leadership. With his ongoing contributions to congenital cardiothoracic surgery, transplantation, and healthcare innovation, he stands as a role model whose impact will continue to shape the fields of pediatric cardiac surgery and global surgical health for years to come.

Notable Publications

"Forty-eight-hour cold-stored whole blood in paediatric cardiac surgery: Implications for haemostasis and blood donor exposures" 

  • Author: Kiskaddon AL, Andrews J, Josephson CD, Kuntz MT, Tran D, Jones J, Kartha V, Do NL
  • Journal: Vox Sang
  • Year: 2024

 

Yanyan Liu | Topic model | Best Researcher Award

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Ms. Yanyan Liu | Topic model | Best Researcher Award

PHD Candidate at University of Macau, China📖

Yanyan Liu is a dedicated researcher specializing in Data Mining with expertise in neural topic modeling, natural language processing, and recommendation systems. She is currently pursuing her Ph.D. in Computer Science at the University of Macau, focusing on developing innovative machine-learning frameworks to enhance topic modeling and social influence learning. With a strong academic foundation and a passion for advancing knowledge in her field, she has published in esteemed journals and conferences, including Knowledge-Based Systems and ACM CIKM.

Profile

Scopus Profile

Education Background🎓

  • Doctorate in Computer Science
    University of Macau | Aug 2020 – Present
    Major Courses: Natural Language Processing, Web Mining, Computer Vision, and Pattern Recognition.
  • Bachelor of Computer Science and Technology
    Hunan University | Sep 2016 – Jun 2020
    GPA: 85.21/100
    Major Courses: Database (94/100), Computer Network, Advanced Programming, Data Structure, Computer System.

Professional Experience🌱

Yanyan Liu has been involved in cutting-edge research on neural topic modeling, where she proposed:

  • An efficient energy-based neural topic model integrating a learnable topic prior constraint.
  • A novel topic-guided debiased contrastive learning framework to enhance topic discrimination.
    She has also contributed to social influence learning models for recommendation systems, advancing the field of personalized recommendations.
Research Interests🔬

Her research focuses on Data Mining, Natural Language Processing, Web Mining, Computer Vision, and Pattern Recognition, with a particular interest in applying these technologies for real-world challenges.

Author Metrics

Yanyan Liu has established herself as an emerging researcher in the field of data mining and machine learning, with a growing portfolio of impactful publications in reputed venues. Her work has been featured in journals such as Knowledge-Based Systems and conferences like the ACM International Conference on Information and Knowledge Management (CIKM), demonstrating her ability to address complex problems in neural topic modeling and recommendation systems. Through her innovative contributions, she has garnered recognition for proposing efficient frameworks and methodologies that advance understanding in these domains. Her publications reflect her commitment to high-quality research and her potential to make significant strides in the field.

Publications Top Notes 📄

1. Cycling Topic Graph Learning for Neural Topic Modeling

  • Authors: Liu, Y., Gong, Z.
  • Journal: Knowledge-Based Systems
  • Year: 2025
  • Volume: 310
  • DOI/Article ID: 112905
  • Citations: 0 (as of now).
  • Summary:
    This paper introduces a novel approach to neural topic modeling using cycling topic graph learning. The method enhances the interpretability and efficiency of topic models by incorporating graph-based structures to represent relationships among topics dynamically. This energy-efficient framework leverages embeddings to achieve improved coherence and relevance in extracted topics.

2. Social Influence Learning for Recommendation Systems

  • Authors: Chen, X., Lei, P.I., Sheng, Y., Liu, Y., Gong, Z.
  • Conference: 33rd ACM International Conference on Information and Knowledge Management (CIKM)
  • Year: 2024
  • Pages: 312–322
  • Citations: 1 (as of now).
  • Summary:
    This conference paper proposes a social influence learning framework tailored for recommendation systems. It explores the role of social connections in shaping user preferences and integrates social influence modeling with machine learning techniques to enhance recommendation accuracy. The model accounts for dynamic social interactions, improving both predictive power and user satisfaction.

Conclusion

Ms. Yanyan Liu is a highly promising researcher with significant achievements in neural topic modeling and recommendation systems. Her innovative contributions, publications in esteemed venues, and dedication to advancing machine learning and data mining make her a strong candidate for the Best Researcher Award. While her citation metrics and collaborative efforts could benefit from further growth, her potential for impactful research and her current accomplishments position her as an excellent choice for this honor.

Her dedication to tackling complex problems and her innovative approach to addressing them not only align with the criteria for the award but also set a strong foundation for her future contributions to the academic and professional world.