Enrique Lopez Droguett

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Professor in the Civil and Environmental Engineering Department and Director of the Center for Reliability Science and Engineering in the Garrick Institute for the Risk Sciences at the University of California, Los Angeles (UCLA). He is also Associate Editor for the Journal of Risk and Reliability and serves in the Board of Directors of the International Association for Probabilistic Safety Assessment and Management (IAPSAM). Prof. López Droguett conducts research on Bayesian inference and artificial intelligence supported digital twins and prognostics and health management based on physics informed deep learning for reliability, risk, and safety assessment of structural and mechanical systems. His most recent focus has been on quantum computing for developing predictive solutions for risk and reliability assessment of complex engineering systems. He has led many major studies on these topics for a broad range of industries, including oil and gas, nuclear energy, defense, civil aviation, mining, renewable and hydro energy production, and distribution networks. Prof. López Droguett has authored more than 300 papers in archival journals and conference proceedings.

Ania Khodabakhshian

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Ania Khodabakhshian is a Ph.D. candidate at the Department of Architecture, Built Environment, and Construction Engineering of Politecnico di Milano. Her research interests include Machine Learning, Construction Management, Risk Management, Building Energy Retrofit, Ethics-aware technology implementation, and Academia-Industry partnerships. During her six-month visit at UCLA, she will research Machine Learning-based Risk Management models in Construction Projects under the supervision of Professor Droguett, which is a part of her ongoing industrial research in collaboration with Jacobs Solutions Inc. She has previously collaborated with MIT Schwarzman College of Computing, the University of Salford, and the International Council for Research and Innovation in Building and Construction (CIB) in the scope of grant-won projects.

Auguste Hirth

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Some of the projects I've worked on have included: explaining the decisions black-box machine learning classifiers make with Boolean circuits, programmatically optimizing the number of gates in a quantum circuit, and writing educational materials for quantum computing software and its use cases. Currently I'm interested in developing the relationship between Bayesian networks and quantum circuits, and determining if NISQ computing can be useful in solving problems represented with Bayesian networks and similar probabilistic graphical models. Quantum computing may enable us in the near term to evaluate large risk-analysis graphical models in ways that would otherwise be impossible with classical computing.

Mohammad Pishahang

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I am a PhD candidate in Transportation Engineering, with a research focus on wildfire evacuation planning and management. In this project, I am utilizing a comprehensive set of tools, including data science, artificial intelligence, stochastic simulation, and probabilistic modeling, to conduct evacuation planning for Wildland-Urban Interface (WUI) communities in the event of wildfires. Apart from my research, I dedicate my time to the development of web and desktop applications.

Gabriel San Martin

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I am a third year Ph.D. Candidate in Civil Engineering working on Quantum Computing applications for Civil Infrastructure management. In particular, I am very interested in the capabilities of quantum computers to solve challenging tasks in the protection, optimization, and improvement of critical infrastructure. For this, my research focus span techniques to perform efficient probabilistic inference and combinatorial optimization. Outside of work, I enjoy biking to the beach and cooking interesting recipes.

Mariana Guimarães Matias Pereira

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Civil Engineer in Bahia Water and Sanitation Company (EMBASA) and Master’s Student in the Master's Degree Program in Environment, Water and Sanitation (MAASA) of Federal University of Bahia (UFBA). Member of the GAMMA Research Group (Growing with Applied Metrics and Mindful Analysis), which involves statistical and computational learning and data science in cooperative research projects with industries and in education. My experience regards in ​​Civil and Sanitary Engineering with an emphasis on controlling and reducing water losses; water distribution systems; hydraulic modeling and geoprocessing. Furthermore, I have experience in statistical and machine learning, through Python programming language. Main research interests: statistical and machine learning applied to improving operational efficiency in water distribution systems.