SungKu Heo, Ph.D.

Leader

Associate Professor, Dept. of Environmental Engineering, Kangwon National University

Research topic:
AI, Autonomous systems, Circular economy, Decarbonization, Environmental engineering, Wastewater engineering, Process systems engineering

Professional appointment

2025 Associate professor, Dept. of Environmental Engineering, Kangwon National University, Republic of Korea
2024-2025 Research associate, Dept. of civil and environmental engineering, Imperial College London, United Kingdom
2024 Postdoctoral research associate, Dept. of environmental science and engineering, Kyung Hee University, Republic of Korea
2023 Research scholar, Dept. of material science and chemical engineering, University of Southern California, United States

Degree

2024 Ph.D. in Environmental engineering, Kyung Hee University, Republic of Korea. (Best thesis award, Advisor: Prof. ChangKyoo Yoo)
2018 B.S. in Environmental engineering, Kyung Hee University, Republic of Korea

Honors

Deputy Prime Minister’s commendation (Korean government)

• Outstanding graduate student in Korea (2023)

First scholarship-candidate for Kyung Hee Honor Young Scholar Society (KHYSS)

• Extraordinary scholarship to fund the emerging young researcher who shall academically stand for the Kyung Hee university in future. ($ 15K/year, 2021~2023)

Minister of the Environment’s Award (Korean government)

• A global assessment of public perceptions in social media to evaluate the relationship between climate changes and natural events using big data-based machine learning techniques, Best oral presentation award, 2018 symposium of the Korean Society of Climate Change Research. (2019)

Editor’s Choice (Co-first author)

• Dual-objective optimization for energy-saving and fouling mitigation in MBR plants using influent prediction and an integrated biological-physical model, Journal of Membrane Science (ISSN: 0376-7388, IF=7.183, JCR Top 3 %) (2021)

Editor’s Choice (Co-first author)

• An autonomous operational trajectory searching system for an economic and environmental membrane bioreactor plant using deep reinforcement learning, Water Science and Technology (ISSN:0273-1223, SCIE), International Water Association(IWA) (2020)

Best oral presentation award

• Real-scale demonstration of two-stage AMX® digital twins with AI-driven control policy optimization under enriching nitrogen loads (Korean society of water environment) (2024.03)