The paradigm of wastewater treatment is shifting towards the energy-neutral status and net-zero production of carbon dioxide. To achieve the energy-neutral wastewater treatment (ENWT), my research interests are on four topics: energy-neutral process, genomic indicator, database establishment and artificial intelligence. My main research goals have been focused the ENWT and optimize the wastewater treatment processes as an adaptation method to climate change. As the first step of adsorption (A-stage), emerging carbon recovery processes such as chemically enhanced primary treatment (CEPT) and high-rate activated sludge (HRAS) are urgently required to recover suspended and soluble organic carbon contents from wastewater. The captured organic carbon is redirected to anaerobic digestion (AD) to produce biogas. By this carbon redirection, the treatment cost including aeration energy in the activated sludge process is largely saved due to the simple and easy removal of organic carbon instead of intensive aeration. Regarding HRAS, I received a fund from National Research Foundation of Korea (NRF) entitled “Molecular indicator and machine-trained model for core functions of feast-famine reaction utilized in sewage energy recovery” (2021-2025). For the high-quality analysis of DNA and RNA to develop the molecular indicator, advanced systems of micro-electrophoresis, DNA/RNA integrity analyzer, real-time qPCR, digital PCR and next generation sequencing are in operation. My research has been more focused on the advances of information and communication technology (ICT). For example, the integrated database (DB) system can be established including all process operation factors, physicochemical information and microorganism information of ENWT. The integrated DB system can be shared with stakeholders of citizens, city authority and operational organization. In addition, descriptive statistics is a core function of the designated DB to visualize the operational status and time-series performance of ENWT. In addition, my laboratory is developing the world-class environmental genomics research. As the current 3rd generation environmental genomics (2016-present), it is possible to construct complete genomes of microorganisms through long-read sequencing technology and advanced deep learning algorithms. High-performance computing facilities are essential for the integrity and error tolerance of in-silico-organized genomes, called metagenome-assembled genome (MAG). This technology overcomes the limitation of the 16s rRNA gene, which can only be used for phylogenetic analysis of bacteria, and allows to fully explain the function of microorganisms. To utilize the integrated genomic information, machine-learning codes are optimized to effectively summarize the genomic input data and remove the noise signals as an original technology of artificial intelligence.