Daemin Shin | Computer Science | Academic Luminary Achievement Award

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Dr. Daemin Shin | Computer Science | Academic Luminary Achievement Award

Manager at Financial Security Institute (FSI), South Korea

Daemin Shin is a Manager at the Financial Security Institute, where he has been actively involved in advancing financial security measures since April 2015. With expertise in cloud security, Zero Trust security models, and data security, he has played a significant role in shaping secure financial infrastructures. Before his current role, he was a Senior Researcher at the Financial Security Research Institute from July 2012 to April 2015. His contributions to financial cybersecurity research have been instrumental in addressing security threats and enhancing the resilience of financial institutions. Shin continues to lead innovative research and development in financial security.

Profile

Scopus

Education

Daemin Shin earned his Master of Science in Engineering from the Graduate School of Information Security at Korea University, South Korea, in February 2009. He further pursued his Ph.D. in Engineering at the Department of Information Security, Soonchunhyang University, South Korea, which he successfully completed in February 2020. His academic journey reflects a strong foundation in cybersecurity, particularly focusing on financial security, cloud computing, and data protection. Throughout his education, he has been deeply engaged in research on securing financial transactions and developing security frameworks for modern digital finance ecosystems.

Experience

Shin has over a decade of experience in the field of financial security, with a strong emphasis on cloud security, data protection, and Zero Trust architectures. He started his career as a Senior Researcher at the Financial Security Research Institute, where he contributed to innovative research projects on financial cybersecurity from 2012 to 2015. Since April 2015, he has been serving as a Manager at the Financial Security Institute, where he continues to work on financial security infrastructure, cybersecurity policies, and security compliance strategies. His professional experience has significantly contributed to the development of robust security measures for the financial sector.

Research Interests

Shin’s research interests primarily focus on cloud security, financial security, and Zero Trust security models. He has conducted extensive research on securing cloud-based financial infrastructures, ensuring compliance with regulatory requirements, and mitigating security threats in digital finance. His recent works include studies on security considerations for DevSecOps software supply chains and Zero Trust evaluation frameworks tailored for financial institutions. His expertise in these domains has positioned him as a thought leader in enhancing cybersecurity resilience in the financial industry.

Awards and Recognitions

Shin has been recognized for his outstanding contributions to financial security and cybersecurity research. He has been nominated for the Best Researcher Award in recognition of his groundbreaking research on cloud security and financial security frameworks. His efforts in improving security compliance policies and implementing Zero Trust methodologies in financial institutions have gained widespread recognition. Shin’s work has had a substantial impact on the cybersecurity domain, making financial transactions and data storage more secure against emerging threats.

Publications

D. Shin, V. Sharma, J. Kim, S. Kwon, and I. You (2017). “Secure and Efficient Protocol for Route Optimization in PMIPv6-Based Smart Home IoT Networks,” IEEE Access, vol. 5, pp. 11100-11117, DOI: 10.1109/ACCESS.2017.2710379. Cited by 200+ articles.

D. Shin, K. Yun, J. Kim, P. V. Astillo, J.-N. Kim, and I. You (2019). “A Security Protocol for Route Optimization in DMM-Based Smart Home IoT Networks,” IEEE Access, vol. 7, pp. 142531-142550, DOI: 10.1109/ACCESS.2019.2943929. Cited by 150+ articles.

Shin, Daemin, Kim, Jiyoon, & You, Ilsun (2023). “국내 금융구득 클라우드 전환 동형 및 보안,” REVIEW OF KIISC, 33(5), 57-68. Cited by 50+ articles.

Shin, Daemin, You, Ilsun, and Kim, Jiyoon (2024). “국내 금융구득 클라우드 보안 위험 및 보안 요구사항에 관한 연구,” Journal of Next-Generation Computing, 20(4), 77-96, DOI: 10.23019/kingpc.20.4.202408.007. Cited by 30+ articles.

Daemin Shin, Jiyoon Kim, I Wayan Adi Juliawan Pawana, Ilsun You (2025). “Enhancing Cloud-Native DevSecOps: A Zero Trust Approach for the Financial Sector,” Computer Standards & Interfaces, DOI: 10.1016/j.csi.2025.103975. Cited by 20+ articles.

Conclusion

Daemin Shin’s dedication to advancing financial security and cybersecurity has been instrumental in shaping modern security frameworks for financial institutions. His research on cloud security, Zero Trust models, and DevSecOps methodologies continues to drive innovation in securing financial infrastructures. With a strong academic and professional background, he remains committed to developing secure financial ecosystems and mitigating cybersecurity risks in an ever-evolving digital landscape. His contributions have earned him significant recognition, making him a leading figure in financial security research.

Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

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Assist. Prof. Dr. Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

Assistant Professor at Department of Electronic Engineering, Tsinghua University, China

Quanming Yao is a world-class researcher in the field of machine learning, holding the position of Assistant Professor in the Department of Electronic Engineering at Tsinghua University. With a strong academic background and extensive experience in deep learning, Yao’s research focuses on creating efficient and parsimonious solutions in machine learning, particularly in deep networks and graph learning. His work aims to enhance interpretability in AI models and has led to groundbreaking advancements, such as the development of EmerGNN, the first deep learning model that interprets drug-drug interaction predictions for new drugs. His contributions have significantly impacted both academia and industry, leading to the commercialization of his methods in the AI unicorn 4Paradigm.

Profile

Orcid

Education

Yao earned his Ph.D. in Computer Science and Engineering from the Hong Kong University of Science and Technology (HKUST) between 2013 and 2018. Prior to this, he completed his undergraduate studies at Huazhong University of Science and Technology, where he obtained a degree in Electronic and Information Engineering in 2013.

Experience

Before becoming an assistant professor at Tsinghua University in 2021, Yao worked as a researcher and senior scientist at 4Paradigm Inc. in Hong Kong, from June 2018 to May 2021. In his current academic role, he serves as a Ph.D. advisor, leading research in machine learning and AI, with a specific focus on making deep learning models more efficient and interpretable.

Research Interests

Yao’s research interests revolve around the concept of “parsimonious deep learning,” wherein he explores how simple solutions can lead to substantial improvements in machine learning models. His work is especially notable for its emphasis on automated graph learning methods, which has earned him first place in the Open Graph Benchmark, an equivalent to ImageNet in graph learning. He is also dedicated to the development of deep learning methods that provide interpretable results, particularly in domains like drug discovery, where his innovations have had a direct impact on creating a synthetic biology startup, Kongfoo Technology.

Awards

Yao’s exceptional contributions to the field of machine learning have earned him numerous prestigious awards. These include the Inaugural Intech Prize in 2024, the Aharon Katzir Young Investigator Award in 2023, Forbes 30 Under 30 in the Science & Healthcare Category (China) in 2020, and the Google Ph.D. Fellowship in 2016. He was also recognized as one of the World’s Top 2% Scientists in 2023, highlighting his influence in the global research community.

Publications

Yao has published over 100 papers in top-tier international journals and conferences, with a significant citation record (around 12,000 citations and an h-index of 36). His work includes several landmark papers, such as:

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network, Nature Computational Science, 2023.

AutoBLM: Bilinear Scoring Function Search for Knowledge Graph Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.

Efficient Low-rank Tensor Learning with Nonconvex Regularization, Journal of Machine Learning Research (JMLR), 2022.

Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels, Advance in Neural Information Processing Systems (NeurIPS), 2018.

These papers showcase his innovative work in the areas of drug interaction prediction, knowledge graph learning, and robust training of deep neural networks, significantly impacting both theoretical and practical aspects of AI.

Conclusion

Quanming Yao stands out as a leader in machine learning, particularly in deep learning, graph learning, and AI applications in drug discovery. His exceptional academic journey, impactful research, and numerous awards reflect his profound influence in the field. Yao’s contributions to AI are reshaping industries, and his future work promises to continue pushing the boundaries of what is possible with machine learning.

Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

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Dr. Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

Researcher | Central People’s Hospital of Zhanjiang | China

Dr. Cheng-Mao Zhou is a prominent researcher at the Central People’s Hospital of Zhanjian, specializing in the application of artificial intelligence (AI) in perioperative medicine. His work primarily focuses on the development and implementation of machine learning and deep learning algorithms aimed at enhancing postoperative complication prediction and prevention. Dr. Zhou has made significant contributions to medical AI, particularly in the areas of postoperative complications such as delirium and renal impairment. His work has been widely recognized in the field, with multiple publications in high-impact journals and a citation index reflecting his impactful research.

Profile

Scopus

Education

Dr. Zhou’s academic background is rooted in both the medical and computational sciences, where he pursued studies that bridged the gap between artificial intelligence and perioperative care. His educational foundation has been instrumental in fostering his expertise in AI algorithms and their practical applications in clinical settings. Although specific degrees and institutions are not listed, his professional trajectory highlights advanced academic training that combines medicine and technology, driving his innovations in the field.

Experience

Dr. Zhou’s career is marked by his focus on applied basic research within the domains of artificial intelligence and perioperative medicine. With years of experience, he has developed sophisticated machine learning models to predict postoperative complications, an area that significantly impacts patient outcomes. His work involves designing algorithms that enhance the accuracy of predictions related to complications such as delirium and renal issues. Dr. Zhou has also led multiple ongoing research projects that contribute to both theoretical and practical advancements in medical AI, particularly within anesthesiology and critical care.

Research Interests

Dr. Zhou’s primary research interests revolve around the integration of artificial intelligence, specifically machine learning and deep learning algorithms, into perioperative medicine. His work aims to leverage AI to predict and prevent postoperative complications, improving the accuracy of clinical predictions and optimizing patient care. In particular, he focuses on predictive methodologies for conditions such as delirium and renal impairment following surgery. His research bridges the gap between technology and clinical application, working toward a future where AI plays a central role in personalized medicine and post-surgical care.

Awards

Dr. Zhou is a candidate for the Best Researcher Award, a recognition acknowledging his groundbreaking work in the field of artificial intelligence and perioperative medicine. His research contributions have been pivotal in advancing the understanding and application of AI for postoperative care, improving outcomes for patients and offering a significant contribution to the field of medical AI. Though details of other awards are not specified, his nomination for this prestigious award highlights his considerable influence and recognition within the medical research community.

Publications

Dr. Zhou has authored over 20 AI research articles, with a particular focus on predictive methodologies for postoperative complications. His most notable publications include work on the prediction of delirium and renal impairment, demonstrating the effectiveness of machine learning models in clinical settings. Below is a selection of his key publications:

“A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm”

    • Authors: Zhou, C.-M., Xue, Q., Li, H., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 0

“Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery”

    • Authors: Zhou, C.-M., Li, H., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 3

“An AI-based prognostic model for postoperative outcomes in non-cardiac surgical patients utilizing TEE: A conceptual study”

    • Authors: Zhu, Y., Liang, R., Zhou, C.-M.
    • Year: 2024
    • Citations: 0

“Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 6

“Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology”

    • Authors: Zhou, C.-M., Wang, Y., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 10

“A long duration of intraoperative hypotension is associated with postoperative delirium occurrence following thoracic and orthopedic surgery in elderly”

    • Authors: Duan, W., Zhou, C.-M., Yang, J.-J., Ma, D.-Q., Yang, J.-J.
    • Year: 2023
    • Citations: 19

“Prognostic value of postoperative lymphocyte-to-monocyte ratio in lung cancer patients with hypertension”

    • Authors: Yuan, M., Wang, P., Meng, R., Zhou, C., Liu, G.
    • Year: 2023
    • Citations: 0

“Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Zhu, Y.
    • Year: 2023
    • Citations: 5

“Non-linear relationship of gamma-glutamyl transpeptidase to lymphocyte count ratio with the recurrence of hepatocellular carcinoma with staging I–II: a retrospective cohort study”

    • Authors: Li, Z., Liang, L., Duan, W., Zhou, C., Yang, J.-J.
    • Year: 2022
    • Citations: 2

“Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2022
    • Citations: 16

Conclusion:
Dr. Cheng-Mao Zhou stands as a leader in the fusion of artificial intelligence and perioperative medicine. His pioneering research on postoperative complication prediction using AI algorithms not only enhances clinical outcomes but also sets the stage for future innovations in patient care. As a member of prestigious professional societies, his work has garnered widespread recognition, including his nomination for the Best Researcher Award. Dr. Zhou’s dedication to advancing the integration of AI into medical practice continues to influence both academic and clinical spheres, driving significant improvements in patient outcomes. His contributions are critical to the ongoing transformation of the medical landscape, positioning him as a key figure in the future of AI-driven healthcare.