Hi! My name is TaeHo Kim. I'm an environment researcher interpreting and diagnosing the Earth's mysterious patterns. Using environmental modeling, I aim to 1) discover meaningful spatiotemporal patterns, 2) provide a scientific basis for which factors are important and sensitively affect the patterns, and 3) present insights for effective environmental management policy making. Let's make the big-wave together!
Environmental Engineering PhD Student,
Ecohydrology Lab,
University of Michigan
E-mail: theokim@umich.edu
Phone: +1 734-730-8148
Discovery of unknown spatiotemporal patterns in the Earth.
Development of generalized and interpretable model frameworks to manage big data.
Assimilation of incomplete data with generative models.
Development of integrated index to summarize environmental states and phenomena.
(Updated 4 Jan. 2023)
[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.