Sooyoun Lee l Data Analysis Software | Women Researcher Award

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Dr. Sooyoun Lee l Data Analysis Software | Women Researcher Award

National Institute of Health | South Korea

Dr. Sooyoun Lee is a Research Assistant Professor at Ajou University’s Bio-AI Center, specializing in bioinformatics, biomedical science, and computational biology. She holds a PhD in Bioinformatics from Seoul National University and an M.S. in Biomedical Science from Ajou University. Her research focuses on multi-omics data analysis, cancer genomics, precision medicine, and AI-driven drug repositioning. Dr. Lee has extensive experience in bioinformatics tool development, transcriptome analysis, and proteogenomics, with numerous high-impact publications and patents. Her work bridges computational methods and medical applications, advancing personalized healthcare solutions and integrative approaches in biomedical research.

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Featured Publications

Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

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Prof. Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

SUNY Upstate Medical University | United States

Prof. Bardia Rodd is an Associate Professor at SUNY Upstate Medical University and Associate Director of AI Innovation at the AI for Health Equity, Analytics, and Diagnostics (AHEAD) Center. He completed dual PhDs in Electrical & Computer Engineering from Université Laval and in Computer Science from the University of Malaya, alongside a postdoctoral fellowship at the University of Pennsylvania Perelman School of Medicine, focusing on precision medicine, artificial intelligence, and image-guided systems. His academic career spans faculty positions at SUNY Upstate, the University of Maryland, and Laval University, with extensive experience in biocomputational engineering, machine learning, medical image analysis, and data-driven healthcare solutions. He has led numerous grants, including initiatives in AI in education, biomedical research, and curriculum development. Prof. Rodd has supervised multiple graduate and postgraduate students and contributed to open educational resources, advancing accessible teaching in machine learning and bioengineering. He is an active member of professional societies including IEEE, SPIE, and the Association of Pathology Informatics, serving on multiple technical committees and leadership roles. His research interests include AI-driven biomedical imaging, predictive modeling, and health equity applications. Prof. Rodd has authored a textbook on machine learning for data analysis and continues to integrate research, teaching, and innovation to advance computational medicine and AI education.

Profile : Scopus

Featured Publications

Siezen, H., Awasthi, N., Rad, M. S., Ma, L., & Rodd, B. (2025). “Low-rank distribution embedding of dynamic thermographic data for breast cancer detection.” Journal of Thermal Biology, 104303.

Usamentiaga, R., Fidanza, A., Yousefi, B., Iacutone, G., Logroscino, G., & Sfarra, S. (2025). “Advancing knee injury prevention and anomaly detection in rugby players through automated processing of infrared thermography: A novel biothermodynamics approach.” Thermal Science and Engineering Progress, 103782.

Rad, M. S., Huang, J. V., Hosseini, M. M., Choudhary, R., Siezen, H., Akabari, R., et al. (2025). “Deep learning for digital pathology: A critical overview of methodological framework.” Journal of Pathology Informatics, 100514.

Yousefi, B., Khansari, M., Trask, R., Tallon, P., Carino, C., Afrasiyabi, A., Kundra, V., Ma, L., Ren, L., Farahani, K., & Hershman, M. (2025). “Measuring subtle HD data representation and multimodal imaging phenotype embedding for precision medicine.” IEEE Transactions on Instrumentation and Measurement, TIM-24-01821.

Cao, Y., Sutera, P., Mendes, W. S., Yousefi, B., Hrinivich, T., Deek, M., et al. (2024). “Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.” Radiotherapy and Oncology, 199, 110443.

Ch Rajyalakshmi | Deep learning using medical image data | Best Researcher Award

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Mrs Ch Rajyalakshmi - B V Raju Institute of Technology - Deep learning using medical image data

Mrs Ch Rajyalakshmi : Deep learning using medical image data

Professional profile:

Early Academic Pursuits:

Mrs. Ch. Rajyalakshmi's academic journey commenced with a Bachelor's degree in Computer Science and Information Technology from NEC, Narsaraopeta, in 2004. This laid the foundation for her pursuit of a Master's degree in Computer Science and Engineering at AVIET, Narsipatnam, in 2011. Building on her academic prowess, she is currently pursuing a Ph.D. at JNTU Kakinada, with her thesis focusing on "HIGH SPECTRAL SATELLITE IMAGE SEGMENTATION WITH DEEP LEARNING UNET BASED TECHNIQUES FOR GENERATING FLOOD INUNDATION."

Professional Endeavors:

With a teaching experience spanning 18 years, Dr. Rajyalakshmi has played pivotal roles in various academic institutions. She has been an Associate Professor at BVRIT, Narsapur, since 2011, following roles as an Assistant Professor at CMRIT Engg College, Hyderabad, and a Lecturer at Narsaraopeta Engineering College, Narsaraopeta, earlier in her career.

Contributions and Teaching Experience:

Mrs. Rajyalakshmi's expertise in computer science is evident through the diverse range of courses she has taught. From Artificial Intelligence and Machine Learning to Database Design and Operating Systems, she has contributed significantly to the education of students. Her teaching extends to areas such as Computer Networks, Software Engineering, Multimedia and Design, and Human-Computer Interaction.

Research Focus and Publications:

Mrs. Rajyalakshmi's research interests primarily revolve around the application of Deep Learning architectures for spatial data, particularly in processing high spectral satellite images for water body identification. Her publications in renowned journals and conferences reflect the depth of her contributions to the field. Some notable publications include works on satellite image data extraction using classification techniques and the detection of active attacks in Mobile Ad Hoc Networks (MANETs).

Accolades and Recognition:

Mrs. Rajyalakshmi's academic achievements include being FET Qualified in 2011 and GATE Qualified in 2006. Her commitment to excellence is also evident in her completion of various NPTEL courses and Coursera certifications, covering topics such as Machine Learning, Database Management Systems, Deep Learning, and Artificial Intelligence.

Impact and Influence:

Through her research and teaching endeavors, Mrs. Rajyalakshmi has made a significant impact on the academic community. Her publications and patents attest to her dedication to advancing knowledge in computer science and related domains.

Legacy and Future Contributions:

As a seasoned educator and researcher, Mrs. Rajyalakshmi continues to shape the academic landscape. Her administrative responsibilities, including course coordination, involvement in accreditation processes, and leadership roles in various committees, showcase her commitment to the holistic development of educational institutions. With an ongoing Ph.D. pursuit and an impressive list of publications and patents, she is poised to leave a lasting legacy in the realms of computer science, education, and research. Her multifaceted contributions highlight a career marked by continuous growth, learning, and impactful contributions to academia.

Notable Publications:

Citations:

A total of  8 citations for his publications, demonstrating the impact and recognition of his research within the academic community.

  • Citations     8
  • h-index         2