Gwangsu Kim | Mathematics | Best Researcher Award

Assistant Professor, Jeonbuk National University, South Korea.

Gwangsu Kim, an accomplished academic, currently serves as an Assistant Professor in the Department of Statistics at Jeonbuk National University. With a robust foundation in statistical methodologies and artificial intelligence, he is dedicated to advancing research in these fields. His leadership roles and participation in key projects highlight his commitment to academic innovation and knowledge creation.

Profile

Scopus

Education📝

Gwangsu Kim earned his Ph.D. in Statistics from Seoul National University in 2012, where he focused on Bayesian nonparametrics. His thesis, titled “Posterior Contraction Rate of the Proportional Hazards Model Having a Nonparametric Link and Its Applications,” laid the groundwork for his future research endeavors. He also holds a Bachelor of Science in Statistics from the same university, further solidifying his expertise in the field.

Experience👨‍🏫

Kim’s professional journey includes significant roles as a Research Associate Professor at KAIST and a Research Assistant Professor at Seoul National University. He has been involved in various projects, including the Human Plus Project and AI research initiatives funded by the National Research Foundation of Korea. His extensive teaching experience includes part-time lecturer positions at several prominent universities.

Research Interest🔬

Prof. Kim’s research interests lie in Bayesian statistics, artificial intelligence, and machine learning. He is particularly focused on developing innovative methodologies for model selection, hyperparameter optimization, and causal inference. His ongoing projects explore the intersection of data science and climate change, reflecting a commitment to addressing pressing global challenges through statistical analysis.

Awards and Honors🏆

Gwangsu Kim has received several prestigious awards, including the Distinguished Leadership Award and the Academic Innovation Award from the Korean Artificial Intelligence Association. These accolades recognize his contributions to the field of AI and his impactful leadership within the academic community. His involvement in organizing conferences and workshops further exemplifies his commitment to fostering collaboration and innovation in research.

Skills🛠️

Prof. Kim possesses strong analytical and problem-solving skills, enhanced by his expertise in statistical software and programming languages. He is proficient in various statistical modeling techniques and has a solid understanding of machine learning algorithms. His effective communication skills enable him to convey complex concepts clearly, making him an effective educator and researcher. Additionally, his leadership experience in professional organizations showcases his ability to engage and inspire others in the academic community.

Conclusion 🔍

Gwangsu Kim, Prof., is a highly qualified candidate for the Research for Best Researcher Award. His academic achievements, research contributions, and leadership in the field of statistics and artificial intelligence reflect a commitment to advancing knowledge and innovation. With a focus on enhancing publication metrics and broadening collaborative efforts, he can further solidify his standing as a leading researcher in his field.

Publication Top Notes

Title: Deep Neural Network-Based Accelerated Failure Time Models Using Rank Loss
Authors: Kim, G., Park, J., Kang, S.
Year: 2024
Citation: Statistics in Medicine, Article in Press.

Title: Bayesian Analysis of the Generalized Additive Proportional Hazards Model: Asymptotic Studies
Authors: Kim, G., Yoo, C.D., Kim, Y.
Year: 2024
Citation: Bayesian Analysis, 2024, 19(4), pp. 1225–1243.

Title: Hypothesis Perturbation for Active Learning
Authors: Cho, S.J., Kim, G., Yoo, C.D.
Year: 2024
Citation: IEEE Journal on Selected Topics in Signal Processing, Article in Press, Open access.

Title: QUERYING EASILY FLIP-FLOPPED SAMPLES FOR DEEP ACTIVE LEARNING
Authors: Cho, S.J., Kim, G., Lee, J., Shin, J., Yoo, C.D.
Year: 2024
Citation: 12th International Conference on Learning Representations, ICLR 2024.

Title: Atomically mixed catalysts on a 3D thin-shell TiO2 for dual-modal chemical detection and neutralization
Authors: Shin, J., Lee, G., Choi, M., Cho, D., Jang, J.-S.
Year: 2023
Citation: Journal of Materials Chemistry A, 2023, 11(34), pp. 18195–18206.

Title: Breathable MOFs Layer on Atomically Grown 2D SnS2 for Stable and Selective Surface Activation
Authors: Kim, G.S., Lim, Y., Shin, J., Kang, C.-Y., Jang, J.-S.
Year: 2023
Citation: Advanced Science, 2023, 10(17), 2301002, Open access.

Title: ESD: EXPECTED SQUARED DIFFERENCE AS A TUNING-FREE TRAINABLE CALIBRATION MEASURE
Authors: Yoon, H.S., Tee, J.T.J., Yoon, E., Li, Y., Yoo, C.D.
Year: 2023
Citation: 11th International Conference on Learning Representations, ICLR 2023.

Title: Deep learning-based noise robust flexible piezoelectric acoustic sensors for speech processing
Authors: Jung, Y.H., Pham, T.X., Issa, D., Yoo, C.D., Lee, K.J.
Year: 2022
Citation: Nano Energy, 2022, 101, 107610.

Title: Fair Facial Attribute Classification via Causal Graph-Based Attribute Translation
Authors: Kang, S., Kim, G., Yoo, C.D.
Year: 2022
Citation: Sensors, 2022, 22(14), 5271, Open access.

Title: Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold
Authors: Lee, J., Kim, G., Olfat, M., Hasegawa-Johnson, M., Yoo, C.D.
Year: 2022
Citation: Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, 36, pp. 7363–7371.