simulation

High-dimensional Q-learning for Dynamic Treatment Regimes

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B3 L5 R5220

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients’ responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high.

Going to extremes to predict natural disasters

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A systematic approach to selecting and configuring statistical models improves predictions of extreme events.

KAUST Ph.D. student Lukas Larisch wins the PACE 2017 Challenge

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Lukas Larisch, a KAUST Ph.D. student in Computer Science working under the supervision of Professor Gabriel Wittum, won the PACE 2017 Parameterized Algorithms and Computational Experiments Challenge. The award ceremony that took place in Vienna, Austria, at the beginning of September this year has been held during the ALGO Congress at the International Symposium on Parameterized and Exact Computation (IPEC 2017).

Ph.D. student Michał Mańkowski wins poster award at the 18th Annual American Society of Transplant Surgeons Symposium

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Michał Mańkowski, a Ph.D. student in the University's Computer, Electrical and Mathematical Science and Engineering (CEMSE) division, won the Poster of Distinction Award at the American Society of Transplant Surgeons (ASTS) 18th Annual State of the Art Winter Symposium. The event, which was held in Miami Beach, Florida, U.S., from January 11 to 14, also voted Mańkowski's soapbox presentation as the symposium's best solution on how more lives can be saved through transplantation. Mańkowski presented on behalf of Professor Sommer Gentry, his supervisor at the Johns Hopkins School of Medicine.

Otavio Bertozzi

Research Interests Control systems and power systems engineering. Modeling, simulation, analysis and design of solutions applied to power system stability and control. Optimal and data-driven approaches to the integration of renewable energy resources. Education Profile M.Sc., Electrical and Computer Engineering, KAUST, Thuwal, Saudi Arabia, 2020 ​B.Sc., Electrical Engineering, Unesp, Bauru, Brazil, 2019 Professional Profile 2017-2018: xDSL Interoperability Testing Intern, Intel Corporation, Munich, Germany 2015: Summer Intern, Rohde & Schwarz Reference Laboratory (BME HVT), Budapest, Hungary

Lukas Larisch

Ph.D. Student, Computer Science
Lukas Larisch is a Phd candidate in Computer Science. He is studying under the supervision of Professor Gabriel Wittum. Lukas has a bachelor degree in Computer Science from the Goethe University of Frankfurt, Germany. Education Profile B.Sc. in Computer Science from the Goethe University of Frankfurt, Germany. Awards and Distinctions Winner of the PACE 2017 Challenge

Libo Huang

Ph.D. Student, Computer Science
Libo Huang is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Dominik Michels in Computational Sciences Group (CSG). Education and Early Career Libo Huang obtained his bachelor degree in Geophysics from Peking University in China in 2015. After that, he joined KAUST and received his master degree in Earth Science and Engineering in 2016. Education Profile M. Sc., Earth Science and Engineering , King Abdullah University of Science and Technology (KAUST), Saudi Arabia, 2016 B. Sc