Sun Jong Kim | In Vitro Diagnostics | Science and Technology Pioneer Award

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Dr. Sun Jong Kim | In Vitro Diagnostics | Science and Technology Pioneer Award 

Researcher at BioFront | South Korea

Dr. Sun Jong Kim is a highly accomplished biotechnology and life sciences professional whose career reflects sustained contributions to in vitro diagnostics, stem cell science, biomolecular research, and translational biomedical innovation. Equipped with a strong academic foundation including bachelor’s, master’s, and doctoral studies in life sciences, Dr. Sun Jong Kim has developed deep expertise in cellular biology, developmental biology, and molecular mechanisms of pluripotency. His professional journey includes senior research and leadership roles across multiple biomedical organizations, where he has guided R&D programs involving the establishment and maintenance of pluripotent stem cell lines, large-scale microalgae production systems, medical device development, and regulatory preparation for diagnostic technologies. He has contributed to the development of human embryonic stem cell resources, in vitro differentiation platforms, and advanced diagnostic systems applied to areas such as oncology, COVID-19 rapid testing, and point-of-care technologies. His industrial leadership also includes oversight of product development pipelines, quality assurance systems, and multidisciplinary research operations, demonstrating an ability to translate scientific results into usable biomedical solutions that directly support clinical and research infrastructures. His research outputs include numerous scientific publications, national and international presentations, and patents in areas such as stem cell culturing techniques, photobioreactors, and biomaterial-based innovations. The measurable impact of his scholarly contribution is represented by 24 citations by 24 documents, 2 indexed documents, and an h-index of 2, reflecting ongoing engagement and recognition within the scientific community. Dr. Sun Jong Kim’s research interests encompass stem cell biology, human cell culture systems, the regulation of self-renewal and differentiation pathways, bioprocess design, clinical diagnostics, and biotechnology commercialization. His work has been supported by significant research grants and has contributed to advancements in both academic discovery and applied biomedical engineering. In addition to laboratory research, he has played a crucial role in the development, certification, and evaluation of diagnostic medical devices aligned with international regulatory standards and real-world clinical needs. Through his ongoing work at Biofront and other institutions, Dr. Sun Jong Kim continues to drive innovation in science and technology, contributing to advancements that support more accurate diagnostics, greater understanding of biological systems, and the modernization of biomedical tools used in healthcare and life science research.

Profile: Scopus

Featured Publications:

Kim, S. J.,(1999). Isolation of developmentally regulated novel genes based on sequence identity and gene expression patterns. Molecules and Cells, 9, 207–218.

Kim, S. J.,(2001). Ovarian expression of melatonin Mel(1) receptor mRNA during mouse development. Molecular Reproduction and Development, 59, 126–132.

Kim, S. J.,(2003). Isolation and culture of human embryonic stem-like cells from abnormal blastocysts. Korean Journal of Fertility and Sterility, 30, 293–301.

Kim, S. J.,(2003). Establishment and maintenance of human embryonic stem cells on STO, a permanently growing cell line. Biology of Reproduction, 69, 2007–2014.

Kim, S. J.,(2004). Establishment of embryonic germ cells from cultured human primordial germ cells and their differentiation into neuronal cells. Molecules and Cells, 17, 309–315.

Kim, S. J.,(2004). Recent advances in human embryonic stem cell research. Journal of the Korean Medical Association, 918–925.

Kim, S. J.,(2004). Effects of type IV collagen and laminin on the cryopreservation of human embryonic stem cells. Stem Cells, 22, 950–961.

Kim, S. J.,(2004). Available human feeder cells for the maintenance of human embryonic stem cells. Reproduction, 128, 727–735.

Pengfei Wei | Computer Science | Best Researcher Award

Posted on by Phenomenological Research Awards

Dr. Pengfei Wei | Computer Science | Best Researcher Award 

Senior Engineer at Guangdong University of Technology | China

Dr. Pengfei Wei is a Senior Engineer at Guangdong University of Technology, recognized for his pioneering contributions to the field of computer science, particularly in multimodal learning, knowledge tracing, edge artificial intelligence, and task-oriented dialogue systems. He holds a Ph.D. in Computer Science, where his research focused on integrating deep learning models with practical applications in intelligent education and human–machine interaction. Combining academic rigor with industrial innovation, he brings substantial experience from both enterprise research and academic development, bridging the gap between theory and real-world technology deployment. His work encompasses advanced methods such as visual-enhanced transformers for multimodal named entity recognition, genetic-inspired relation extraction, and the introduction of Kolmogorov–Arnold representations in knowledge tracing, which have improved model interpretability and performance in AI-based learning systems. In addition to his theoretical advancements, he has successfully led projects on real-time lab-safety analytics and large-scale AI deployment using Huawei Ascend, Nvidia, and TPU platforms, contributing to the broader industrial adoption of edge AI technologies. Dr. Pengfei Wei has authored numerous peer-reviewed papers in top-tier international journals and conferences, including Neural Networks, ICMR, and IJCAI, and serves as a reviewer for several prestigious publications such as Neural Networks, Pattern Recognition Letters, AAAI, and IJCNN. His collaborative initiatives with research teams and institutions have fostered multidisciplinary innovation, emphasizing the integration of AI with blockchain, big data, and education systems. A dedicated mentor and research leader, he actively supports student-led research and fosters the development of next-generation AI scholars. His professional memberships with the China Computer Federation (CCF) and the Association for Computing Machinery (ACM) reflect his strong engagement in the global computing community. Dr. Pengfei Wei’s research continues to push the boundaries of multimodal understanding and intelligent systems, driving transformative progress in computational learning and applied artificial intelligence. Through his sustained contributions, he remains committed to advancing the capabilities of intelligent technologies that enhance human productivity, knowledge discovery, and digital transformation.

Profile: Orcid | Google Scholar

Featured Publications:

  • Liao, W., B. Zeng, Yin, X., & Wei, P. (2021). An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence, 51(6), 3522–3533.

  • Liao, W., Zeng, B., Liu, J., Wei, P., Cheng, X., & Zhang, W. (2021). Multi-level graph neural network for text sentiment analysis. Computers & Electrical Engineering, 92, 107096.

  • Liao, W., Zeng, B., Liu, J., Wei, P., & Fang, J. (2022). Image-text interaction graph neural network for image-text sentiment analysis. Applied Intelligence, 52(10), 11184–11198.

  • Liao, W., Zeng, B., Liu, J., Wei, P., & Cheng, X. (2022). Taxi demand forecasting based on the temporal multimodal information fusion graph neural network. Applied Intelligence, 52(10), 12077–12090.

  • Wei, P., Zeng, B., & Liao, W. (2022). Joint intent detection and slot filling with wheel-graph attention networks. Journal of Intelligent & Fuzzy Systems, 42(3), 2409–2420.

  • Wei, P., Ouyang, H., Hu, Q., Zeng, B., Feng, G., & Wen, Q. (2024). VEC-MNER: Hybrid transformer with visual-enhanced cross-modal multi-level interaction for multimodal NER. Proceedings of the International Conference on Multimedia Retrieval (ICMR 2024).

  • Wen, S., Zeng, B., Liao, W., Wei, P., & Pan, Z. (2021). Research and design of credit risk assessment system based on big data and machine learning. Proceedings of the IEEE 6th International Conference on Big Data Analytics (ICBDA 2021), 9–13.