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< Professor Sungyeol Choi >
Professor Sungyeol Choi from the Department of Nuclear and Quantum Engineering received the Science and ICT Ministerial Commendation on the 9th Annual Nuclear Safety and Promotion Day last month, in recognition of his contributions to the promotion of nuclear energy through the safe management of spent nuclear fuel and radioactive waste.
Professor Choi developed high-precision, multi-physics codes that can predict and prevent abnormal power fluctuations caused by boron hideout within nuclear fuel in a pressurized water reactor, solving the problem that has caused economic losses of tens of billions of won every year from industrial sites.
He is now developing a new technology that can reduce high-level waste by recycling spent nuclear fuel, while preventing nuclear material from being used for nuclear weapons, which is one of the biggest challenges faced by the nuclear industry.
In 2017, his first year in office as a KAIST professor, Professor Choi was selected as the youngest and the only member under 50 of the Standing Scientific Advisory Committee at the Information Exchange Meeting on Partitioning and Transmutation (IEMPT), an authoritative association on the disposal of high-level nuclear waste.
The following year, he became the first Korean to receive the Early Career Award, which is given to one person every two years by the International Youth Nuclear Congress.
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research Simultaneous Analysis of 21 Chemical Reactions... AI to Transform New Drug Development
< Photo 1. (From left) Professor Hyunwoo Kim and students Donghun Kim and Gyeongseon Choi in the Integrated M.S./Ph.D. program of the Department of Chemistry > Thalidomide, a drug once used to alleviate morning sickness in pregnant women, exhibits distinct properties due to its optical isomers* in the body: one isomer has a sedative effect, while the other causes severe side effects like birth defects. As this example illustrates, precise organic synthesis techniques, which selectivel
2025-06-16 -
research KAIST Turns an Unprecedented Idea into Reality: Quantum Computing with Magnets
What started as an idea under KAIST’s Global Singularity Research Project—"Can we build a quantum computer using magnets?"—has now become a scientific reality. A KAIST-led international research team has successfully demonstrated a core quantum computing technology using magnetic materials (ferromagnets) for the first time in the world. KAIST (represented by President Kwang-Hyung Lee) announced on the 6th of May that a team led by Professor Kab-Jin Kim from the Department of P
2025-06-12 -
research KAIST and Mainz Researchers Unveil 3D Magnon Control, Charting a New Course for Neuromorphic and Quantum Technologies
< Professor Se Kwon Kim of the Department of Physics (left), Dr. Zarzuela of the University of Mainz, Germany (right) > What if the magnon Hall effect, which processes information using magnons (spin waves) capable of current-free information transfer with magnets, could overcome its current limitation of being possible only on a 2D plane? If magnons could be utilized in 3D space, they would enable flexible design, including 3D circuits, and be applicable in various fields such as nex
2025-05-22 -
research KAIST achieves quantum entanglement essential for quantum error correction
Quantum computing is a technology capable of solving complex problems that classical computers struggle with. To perform accurate computations, quantum computers must correct errors that arise during operations. However, generating the quantum entanglement necessary for quantum error correction has long been considered a major challenge. < Photo 1. (From left) Students Young-Do Yoon and Chan Roh of the Master's and Doctoral Integrated Program of the Department of Physics poses with Profe
2025-02-25 -
research KAIST Proposes AI Training Method that will Drastically Shorten Time for Complex Quantum Mechanical Calculations
- Professor Yong-Hoon Kim's team from the School of Electrical Engineering succeeded for the first time in accelerating quantum mechanical electronic structure calculations using a convolutional neural network (CNN) model - Presenting an AI learning principle of quantum mechanical 3D chemical bonding information, the work is expected to accelerate the computer-assisted designing of next-generation materials and devices The close relationship between AI and high-performance scientific computi
2024-10-30