Mihai A. Diaconeasa
Assistant Professor of Nuclear Engineering
- Burlington Laboratory 1110D
Dr. Mihai Diaconeasa obtained his B.S. degree from University College Utrecht, the international undergraduate honors college of Utrecht University, the Netherlands, his M.S. in Nuclear Science and Engineering from Massachusetts Institute of Technology (MIT), and Ph.D. in Mechanical Engineering from University of California, Los Angeles (UCLA). After his graduation, Dr. Diaconeasa held the postdoctoral research scholar position at the B. John Garrick Institute for the Risk Sciences from the School of Engineering at UCLA.
Over the past years, Dr. Diaconeasa has developed the methodologies needed to design and implement a suite of computer codes in the probabilistic risk, reliability, and resilience assessment fields for nuclear, aerospace, and maritime industries. He was also the associate general chair for the International Conference on Probabilistic Safety Assessment and Management (PSAM-14) hosted by UCLA in 2018. He is a member of the American Nuclear Society (ANS) Standards Committee ANS-30.1 Working Group under the Research and Advanced Reactors Consensus Committee and a member of the American Society of Mechanical Engineers (ASME) Safety Engineering and Risk Analysis Division (SERAD) Executive Committee.
Dr. Diaconeasa leads the development of ADS-IDAC, a dynamic probabilistic risk assessment methodology and software platform for nuclear power plants, the Hybrid Causal Logic Analyzer system risk and reliability software used to enhance the design process and assess the commercial off-the-shelf (COTS) parts usage in space systems for extended deep space missions at NASA’s Jet Propulsion Laboratory (JPL), and the Phoenix human reliability analysis methodology and software for Japan’s Nuclear Regulation Authority (JNRA).
University of California, Los Angeles
Nuclear Science and Engineering
Massachusetts Institute of Technology
University College Utrecht, Utrecht University
Dr. Diaconeasa's research focus includes theories, applications, and simulation-based techniques in risk sciences such as traditional and dynamic probabilistic risk assessment, reliability analysis, resilient systems design, probabilistic physics of failure modeling, and Bayesian inference.