Bibiana Doris Riquelme | Biomedical Physics | Best Researcher Award

Bibiana Doris Riquelme | Biomedical Physics | Best Researcher Award

Principal Research, Consejo Investigaciones Universidad Nacional de Rosario (CIUNR), Argentina.

Bibiana Doris Riquelme is a distinguished physicist based in Rosario, Argentina, with extensive expertise in applied physics, biophotonics, hemorrheology, and biosensors. Her interdisciplinary approach combines principles of physical sciences and biomedical research, leading to significant advancements in understanding biomolecular interactions and their applications in medical technology. Riquelme is affiliated with the Institute of Physics of Rosario (IFIR), where she continues to contribute to innovative research initiatives.

Profile:

 

Education

Riquelme completed her Bachelor’s Degree in Physics from the National University of Rosario (UNR) in December 1989, specializing in applied optics to biology. She further advanced her education by earning a Doctorate in Physics in May 1997, focusing on the complex rheology of biomembranes and its application to human erythrocytes. Additionally, she holds a degree as a Professor of Drawing and Painting from S.S.M. Cosmopolita, demonstrating her diverse academic background and creative skills.

Professional Experiences

Bibiana Riquelme’s professional journey includes a postdoctoral fellowship awarded by FOMEC at the National University of Rosario, where she spent ten months abroad in France, engaged in research at the Laboratory of Mechanics and Cellular and Tissue Engineering. Her work in various esteemed institutions has allowed her to collaborate with leading researchers and contribute to significant advancements in her field. Currently, she is a research scientist at the Institute of Physics of Rosario, where she leads projects focusing on optics and biophysics.

 

Research skills

Riquelme possesses a comprehensive skill set in several critical areas of research, including optics, biophotonics, and hemorrheology. Her proficiency extends to the design and development of biosensors and the study of biomolecular interactions in solution. Her expertise in applied optics to biomedical sciences enables her to contribute significantly to both theoretical and practical advancements in health-related technologies, including medical biomaterials and stem cell technologies.

 

Awards And Recoginition

Throughout her career, Riquelme has received recognition for her contributions to science and technology. Her postdoctoral research experience and collaborations with international research institutions highlight her commitment to advancing knowledge in her field. While specific awards and recognitions are not detailed in the provided information, her standing as a researcher in the academic community reflects her impactful work and dedication to scientific excellence.

Conclusion

Bibiana Doris Riquelme possesses a robust academic background, specialized research expertise, and postdoctoral experience, making her a strong candidate for the Best Researcher Award. Her contributions to applied physics in the biomedical field and her potential for impactful research underscore her suitability for this recognition. Addressing the areas for improvement, particularly in showcasing her publications and community engagement, could further bolster her candidacy and demonstrate her influence within the scientific community.

Publication Top Notes

  • Preliminary Study of the Gamma-Radiation Effect on the Plasma Ions Concentration in Transfusion Units
    Authors: Alet, A.I., Porini, S., Detarsio, G., Galassi, M.E., Riquelme, B.D.
    Year: 2024
    Citation: Anales de la Asociacion Fisica Argentina, 35(1), pp. 21–24.
    🧪📊
  • Effect of Aqueous Extracts of Phyllanthus sellowianus on the Viscoelastic Properties of Human Red Blood Cells: In Vitro Antidiabetic Activity
    Authors: Mascaro Grosso, H., Buszniez, P., Castellini, H.V., Riquelme, B.D.
    Year: 2023
    Citation: Anales de la Asociacion Fisica Argentina, 34(2), pp. 42–45.
    🍃💉
  • Biospeckle Laser as a Tool to Analyze Erythrocyte Aggregation
    Authors: Toderi, M.A., Riquelme, B.D., Galizzi, G.E.
    Year: 2022
    Citation: Optical Engineering, 61(12), 124101.
    🔬✨
  • Methods: A New Protocol for In Vitro Red Blood Cell Glycation
    Authors: Batista da Silva, M.V., Alet, A.I., Castellini, H.V., Riquelme, B.D.
    Year: 2022
    Citation: Comparative Biochemistry and Physiology – Part A: Molecular and Integrative Physiology, 264, 111109.
    📚🧬
  • New Insights into the Mechanics of Erythrocytes: Effects of Radiation and Several Drugs of Biomedical Interest
    Authors: Riquelme, B.D., Toderi, M., Batista, M., Estrada, E., Alet, A.I.
    Year: 2022
    Citation: Series on Biomechanics, 36(1), pp. 61–69.
    🩸🔍
  • In Vitro Alteration on Erythrocytes Mechanical Properties by Propofol, Remifentanil, and Vecuronium
    Authors: Alet, A.I., Batista da Silva, M.V., Castellini, H.V., Alet, N.A., Riquelme, B.D.
    Year: 2021
    Citation: Microvascular Research, 135, 104132.
    💊🩹
  • New Insights into the Analysis of Red Blood Cells from Leukemia and Anemia Patients: Nonlinear Quantifiers, Fractal Mathematics, and Wavelet Transform
    Authors: Bortolato, S.A., Mancilla Canales, M.A., Riquelme, B.D., Ponce de León, P., Korol, A.M.
    Year: 2021
    Citation: Physica A: Statistical Mechanics and its Applications, 567, 125645.
    📈💔
  • Simultaneous Determination of Human Erythrocyte Deformability and Adhesion Energy: A Novel Approach Using a Microfluidic Chamber and the “Glass Effect”
    Authors: Londero, C.M., Riquelme, B.D.
    Year: 2021
    Citation: Cell Biochemistry and Biophysics, 79(1), pp. 49–55.
    🔬💧
  • Preliminary Study of the Effects of Gamma Radiations on Human Red Blood Cells
    Authors: Estrada, E., Castellini, H., Acosta, A., Riquelme, B.D., Galassi, M.E.
    Year: 2020
    Citation: Anales de la Asociacion Fisica Argentina, 31(2), pp. 51–54.
    ☢️🔴
  • Extensive Clinical, Serologic and Molecular Studies Lead to the First Reported Rhmod Phenotype in Argentina
    Authors: Mufarrege, N., Franco, N., Trucco Boggione, C., Castilho, L., Cotorruelo, C.
    Year: 2020
    Citation: Transfusion, 60(7), pp. 1373–1377.
    🩸🇦🇷

Hyun-Soo Choi | Medical Domain | Best Researcher Award

Prof Dr. Hyun-Soo Choi | Medical Domain | Best Researcher Award

Assistant Professor at Seoul National University of Science and Technology, South Korea

Dr. Hyun-Soo Choi is an Assistant Professor in the Department of Computer Science and Engineering at Seoul National University of Science and Technology. He holds a Ph.D. in Electrical and Computer Engineering from Seoul National University. His research focuses on artificial intelligence, machine learning, deep learning, biomedical data learning, and causal inference. With significant contributions to the field, Dr. Choi has published extensively in renowned journals, including IEEE Transactions on Neural Networks and Learning Systems and the Journal of Clinical Medicine. His work on deep learning-based medical diagnostics and imbalanced data classification has garnered attention for its innovative approaches and practical applications. In addition to his academic role, he serves as the Chief Technical Officer at Ziovision and has previously worked as a researcher at SK Telecom. Dr. Choi has received accolades such as the Annual Excellence Award from Seoul National University, recognizing his impactful research and academic achievements.

Profile

Dr. Hyun-Soo Choi’s educational journey is marked by distinguished achievements and a solid foundation in engineering and computer science. He earned his Bachelor of Science degree in Computer and Communication Engineering from Korea University in February 2013, where he developed a strong base in computing principles. He then pursued advanced studies at Seoul National University, completing his Ph.D. in Electrical and Computer Engineering in February 2020. During his doctoral studies, he was supervised by Prof. Sungroh Yoon and focused on “Imbalanced Data Learning: Advances in Techniques and Applications,” contributing significant advancements to the field of artificial intelligence and machine learning. This academic background not only reflects his expertise in data analysis and bio-medical data learning but also highlights his commitment to advancing knowledge through innovative research.

Professional Experience

Dr. Hyun-Soo Choi has accumulated a diverse range of professional experiences across academia and industry. Currently, he serves as an Assistant Professor in the Department of Computer Science and Engineering at Seoul National University of Science and Technology since February 2023. In this role, he teaches advanced courses in programming, natural language processing, and deep learning. Concurrently, Dr. Choi is the Chief Technical Officer at Ziovision, overseeing technological innovations and developments since October 2021. Previously, he was an Assistant Professor at Kangwon National University from March 2021 to February 2023, where he contributed to various courses on data analysis and machine learning. Dr. Choi also has experience as a researcher at SK Telecom’s T-brain, focusing on advanced artificial intelligence applications from February 2020 to February 2021. His academic background includes a Ph.D. from Seoul National University, where he was mentored by Prof. Sungroh Yoon.

 Research Interest

Dr. Hyun-Soo Choi’s research focuses on advanced artificial intelligence (AI) and machine learning techniques, with a particular emphasis on deep learning, universal learning machines, and causal inference. His work addresses the challenges of bio-medical data learning, aiming to enhance the accuracy and efficiency of data-driven healthcare solutions. Dr. Choi’s research contributions include developing innovative algorithms for imbalanced data learning and applying deep learning methods to various medical fields, such as organ localization and ECG analysis. His recent projects explore novel approaches in facial fracture detection, drug monitoring, and real-time health monitoring, underscoring his commitment to translating AI advancements into practical medical applications. By integrating interdisciplinary methodologies, Dr. Choi strives to push the boundaries of AI research, improving diagnostic and predictive capabilities in healthcare and beyond.

 Research Skills

Dr. Hyun-Soo Choi exhibits a broad and advanced skill set in artificial intelligence and machine learning, particularly in deep learning, causal inference, and bio-medical data analysis. His expertise spans a range of research techniques, including the development of advanced algorithms for imbalanced data learning and generative adversarial networks (GANs). Dr. Choi is proficient in employing deep learning models for diverse applications, such as facial fracture detection, organ localization in wireless capsule endoscopy, and electrocardiography analysis. His ability to integrate machine learning methods with real-world medical data demonstrates a strong aptitude for translating complex theoretical models into practical solutions. Additionally, Dr. Choi’s experience with causal inference methods and universal learning machines highlights his skill in handling intricate data structures and extracting meaningful insights. His comprehensive understanding of these areas is evident through his numerous high-impact publications and contributions to cutting-edge research in his field.

Conclusion

Dr. Hyun-Soo Choi is a strong candidate for the “Research for Best Researcher Award” due to his prolific research output, interdisciplinary expertise, and leadership roles in both academia and industry. However, to further strengthen his application, it would be beneficial to highlight his success in securing research funding, previous awards or recognitions, mentorship activities, and the broader societal impact of his research. With these additions, Dr. Choi would present a well-rounded profile that exemplifies excellence in research and its application to solving real-world challenges.

 

Publications Top Notes

  • A Comprehensive Study on a Deep-Learning-Based Electrocardiography Analysis for Estimating the Apnea-Hypopnea Index
    • Authors: Kim, S., Choi, H.-S., Kim, D., … Kim, Y., Lee, W.H.
    • Year: 2024
  • A Deep Learning–Based Approach for Prediction of Vancomycin Treatment Monitoring: Retrospective Study Among Patients With Critical Illness
    • Authors: Kim, D., Choi, H.-S., Lee, D., … Park, J.-H., Park, J.
    • Year: 2024
    • Citations: 1
  • Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study
    • Authors: Park, S.W., Yeo, N.Y., Kang, S., … Lim, C.-M., Heo, J.
    • Year: 2024
    • Citations: 2
  • Explicit Feature Interaction-Aware Graph Neural Network
    • Authors: Kim, M., Choi, H.-S., Kim, J.
    • Year: 2024
    • Citations: 1
  • In-Advance Prediction of Pressure Ulcers via Deep-Learning-Based Robust Missing Value Imputation on Real-Time Intensive Care Variables
    • Authors: Kim, M., Kim, T.-H., Kim, D., … Han, S.-S., Choi, H.-S.
    • Year: 2024
  • Improving Generalization Performance of Electrocardiogram Classification Models
    • Authors: Han, H., Park, S., Min, S., … Choi, H.-S., Yoon, S.
    • Year: 2023
    • Citations: 3
  • On the Impact of Knowledge Distillation for Model Interpretability
    • Authors: Han, H., Siwon, K., Choi, H.-S., Yoon, S.
    • Year: 2023
    • Citations: 1
  • Multi-View Computed Tomography Network for Osteoporosis Classification
    • Authors: Hwang, D.H., Bak, S.H., Ha, T.-J., … Kim, W.J., Choi, H.-S.
    • Year: 2023
    • Citations: 1
  • PixelSteganalysis: Pixel-Wise Hidden Information Removal With Low Visual Degradation
    • Authors: Jung, D., Bae, H., Choi, H.-S., Yoon, S.
    • Year: 2023
    • Citations: 3
  • ConBERT: A Concatenation of Bidirectional Transformers for Standardization of Operative Reports from Electronic Medical Records
    • Authors: Park, S., Bong, J.-W., Park, I., … Choi, H.-S., Kang, S.
    • Year: 2022
    • Citations: 1