[Conference Award] Lee, D., Cha, Y., Kim, T., Shin, J., Park, T., 2021. Real-time contribution change of environmental variables at the mass algal bloom occurrence.
[New Submit, 17 Oct. 2022] Kang C., Shin, J., Cha, Y., Kim, M., Choi, M., Kim, T., Park, Y., Choi, Y., 2022. Machine learning-guided prediction of potential engineering targets for microbial production of lycopene. Biosource Technology.
[New Submit, 14 Sept. 2022] Kim, T., Shin, J. & Cha, Y., 2022. Incorporation of feature engineering and attention mechanisms into deep learning models to develop an early warning system for harmful algal blooms. Journal of Cleaner Production.
[Affiliation change] PhD Student at University of Michigan - Ann Arbor, Ecohydrology lab (Professor Ivanov).
[New Accept, 11 Mar., 2022] Kim, T., Shin, J., Lee, D., Kim, Y., Na E., Park J., Lim C., & Cha, Y., 2022. Simultaneous feature engineering and interpretation: forecasting harmful algal blooms using a deep learning approach. Water Research, 118289 (https://doi.org/10.1016/j.watres.2022.118289).
[New Accept, 27 Feb., 2022] Kim, Y., Kim, T., Shin, J., Lee, D., Park, Y., Kim, Y., Cha, Y., 2021. Validity evaluation of a machine-learning model for chlorophyll a retrival using Sentinel-2 from inland and coastal water. Ecological Indicators, 108737. (https://doi.org/10.1016/j.ecolind.2022.108737)
[Moderate revision, 16 Feb., 2022] Kim, T., Shin, J., Lee, D., Kim, Y., Na E., Park J., Lim C., & Cha, Y., 2022. Simultaneous feature engineering and interpretation: forecasting harmful algal blooms using a deep learning approach. Water Research.
[Online published, 30 Dec., 2021] Kim, T., Lee, D., Shin, J., Kim, Y., & Cha, Y., 2022. Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams. Science of Total Environment, 152520.(https://doi.org/10.1016/j.scitotenv.2021.152520)
[New Submit, 20 Dec., 2021] Kim, Y., Kim, T., Shin, J., Lee, D., Park, Y., Kim, Y., Cha, Y., 2021. Validity evaluation of a machine-learning model for chlorophyll a retrival using Sentinel-2 from inland and coastal water. Ecological Indicators [Under review].
[New Accept, 14 Dec. 2021] Kim, T., Lee, D., Shin, J., Kim, Y., & Cha, Y. , 2021. Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams. Science of Total Environment [In Press] (https://doi.org/10.1016/j.scitotenv.2021.152520).
[New Submit, 13 Dec. 2021] Kim, T., Shin, J., Lee, D., Kim, Y., Na. E., Park, J., Lim, C., & Cha, Y., 2021. Simultaneous feature engineering and interpretation: forecasting harmful algal blooms using a deep learning approach. Water Research [Under review].
[Patent Application, 7 Dec. 2021] Apparatus and method for providing water quality index using factor analysis. Korea Patent, PS21-1004.