CURRENT STUDENTS
STUDENT RESOURCES
CURRENT STUDENTS FAQs
For M.S. students, your advisor when you are admitted to KAUST is the Program Chair. For Ph.D. students, your advisor is your PI (supervisor) whose lab you have been accepted in to.
Yes, you can change your advisor. M.S. students are advised to do so if/when they begin their thesis or directed research. Ph.D. students do have the ability to change advisors, but the overall impact to the Ph.D. project, as well as the time left to finish the Ph.D., could be significant. This will have to be taken into account before approval.
M.S. students need 36 credits (combination of courses and research is specific to your program).
Ph.D. students need 6 credits of 300-level coursework and will earn dissertation research credit each semester until they defend (no minimum credits established, although there is a minimum residency requirement of 2.5 years).
M.S. students get all university holidays (Eid Al-Fitr, Eid Al-Adha, Spring break).
Ph.D. students get university holidays and three weeks of annual/vacation leave per calendar year to be taken in agreement with your PI.
Yes. Drop and Add deadlines are on the academic calendar.
Your GPC can help you request these from the Registrar’s Office, or you can contact them directly at RegistrarHelpDesk@KAUST.EDU.SA
Latest Events
Abstract:
Raman spectroscopy and non-linear Raman spectroscopy techniques are increasingly being used in various disciplines, including chemical analysis, life sciences and medicine. Applications in these fields rely on artificial intelligence (AI)-based methods to transform measured data into high-level information and knowledge within the application domain. The high-level information depends on the specific task and sample characteristics, such as disease types, tissue types and other properties such as constituent concentrations. To achieve this translation, specialised data pipelines must be constructed for each measurement modality, including experimental design, sample size planning, data pre-treatment, data pre-processing, chemometric and machine learning based data modelling, model transfer methods and transfer learning. Almost every step in the data pipeline can be optimised using AI-based methods, including machine learning and deep learning. This talk will highlight common pitfalls encountered when building data pipelines for linear and non-linear Raman spectroscopy measurement techniques and discuss strategies to avoid them.
Bio:
Thomas Bocklitz studied Physics at the University of Jena and received his PhD in Physical Chemistry/Chemometrics there in 2011. In 2013 he became head of a junior research group "Statistical Modelling and Image Analysis" at the University of Jena. Since 2019 he is head of the research group "Photonic Data Science" at the Leibniz IPHT. In 2023 he was appointed as full professor for "Artificial Intelligence in Spectroscopy and Microscopy" at the University of Bayreuth and in 2024 as professor for "Photonic Data Science" at the University of Jena and the Leibniz-IPHT. His main research area is closely related to the photonic data lifecycle, which includes machine learning and chemometrics based modelling of photonic data. He has published more than 140 papers in peer-reviewed journals and has given more than 50 invited talks at conferences. Thomas Bocklitz's work has been honoured with prestigious awards, such as the Kaiser-Friedrich Research Prize in 2018 and the Bruce Kowalski Award in 2015. He also received an ERC Consolidator Grant in 2023 for the STAIN-IT project.
LIFE AT KAUST