Workshops, Courses and other Activities

The forthcoming activities of the CUAI research activity – summer schools, workshops, training sessions, etc. – are announced here.  All these activities are headed and coordinated by Associate Professor Yiqiu Dong and Professor Per Christian Hansen.


 

Workshop: Imaging with Uncertainty Quantification (IUQ)

September 27-29, 2022

Imaging is everywhere in science and technology, and often there is a need for assessing the uncertainty of the reconstructions due to measurement noise, model errors, etc. We see an increasing interest in performing uncertainty quantification (UQ) for imaging applications, and for making such methods readily useful in applications.

This workshop aims at bringing together specialists in UQ for imaging, and we invite talks that cover various aspects related to the development of theory, methodology and software. We also welcome talks about interesting applications of UQ in imaging. The goal is to stimulate networking and collaboration between researchers and students in these areas.

Before the workshop, we arrange a 1-day short course devoted to the Python software CUQIpy that we are currently developing for modeling and computations related to UQ for imaging.

For more details about the workshop, and to register, go to the IUQ Workshop homepage.

The workshop and training course are part of the activities in the research project CUQI, Computational Uncertainty Quantification for Inverse problems, funded by The Villum Foundation.

 

Felipe Uribe - Bayesian Inverse Problems

Postdoc Felipe Uribe is invited to talk about Bayesian inverse problems at the Summer School on Recent Advancements in Computational and Learning Methods for INVERSE PROBLEMS (CLIP22), July 11–15: https://bugs.unica.it/cana/clip22/

 

Jakob Sauer Jørgensen - CUQIpy

Senior researcher Jakob Sauer Jørgensen is invited to talk about the software package CUQIpy at the CIMPA Summer School 2022 MATHEMATICAL METHODS IN DATA ANALYSIS, July 18–29: https://sites.google.com/view/mathschoolinalbania/

 


Past Activities

 

PhD course: Introduction to Uncertainty Quantification for Inverse Problems

January 3-22, 2022 (3 week period at DTU)

Uncertainty quantification (UQ) is the science of characterization and management of randomness in computational models of real world applications. UQ blends theories and methods crossing stochastic analysis, statistical modeling and scientific computing.

This course introduces state-of-the-art numerical methods for quantification and reduction of uncertainties in computational models. UQ is paramount to enhance analysis and prediction tasks in multiple applications such as tomography, material science, spatial statistics, reliability, etc. Therefore, the course can be of interest to students from any discipline in applied mathematics and engineering. The course provides the mathematical background for theory and methods of UQ, which are illustrated via Python exercises. Examples covered in the course include elemental models of deconvolution, diffusion and structural engineering.

Ects: 5.
Time: January 3-22, 2022 (3 week period at DTU).
Teachers: Postdoc Babak M. Afkham and Postdoc Felipe Uribe Castillo, DTU Compute.
Course responsible, contact: Associate Professor Yiqiu Dong, DTU Compute.
More details: Link to DTU's course description, course no. 02975.

 

PhD course: Bayesian Scientific Computing

December 2019

Bayesian statistics is concerned with inference on variables that are not directly observable, the unknowns of primary interest, based a priori information about them plus observation of other quantities that depend indirectly on the variables of interest. The connection between Bayesian inference and inverse problems, the science of estimating variables from noisy indirect measurements is clear, and presently Bayesian methods in inverse problems are widely used. The interplay between ideas from scientific computing for inverse problems and Bayesian methods for inference gives rise to Bayesian scientific computing, which the topic of this course.

The lectured will focus on basic techniques in Bayesian methods, including probability distributions, Bayes' formula, conditioning, hierarchical models, estimation problems arising in this context, as well as certain numerical techniques for inverse problems, including regularization and iterative methods for solving large systems. In the lectures the connections between computational inverse problems and Bayesian inference will be highlighted. The Bayesian methods developed in the course will be used to solve inverse problems with sparsity constraints and dynamically update estimates with classical filtering techniques such as Kalman filtering.

The course consists of lectures and MATLAB based exercises, and is based on the book: D.  Calvetti and E. Somersalo, Introduction to Bayesian Scientific Computing, Springer, 2007, as well as a preliminary new edition of it.  A basic knowledge of any recent version of MATLAB is required; no additional toolboxed will be used.

Ects: 2.5.
Time: December 9-13, 2019 (one full week).
Teacher: Professor Daniela Calvetti and Professor Erkki Somersalo, both from Case Western Reserve University, Cleveland, Ohio.
Course responsible, contact: Professor Per Christian Hansen, DTU.
More details: link to course description (DTU course 02962).

Sign up for the course by sending an email to Per Christian Hansen: pcha@dtu.dk.

Please be advised that all participants are responsible themselves for finding accommodation (we do not have the resources to help with this).

 

VILLUM Investigator Grant Inauguration

The inauguration of the CUQI research initiative took place Nov. 4, 2019 with the program

  • 15:00 Welcome and presentation of the PI - Rasmus Larsen, Provost, DTU
  • 15:15 WILLUM FONDEN and its role in Danish research - Jens Kann-Rasmussen, chairman, Villum Fonden
  • 15:25 The Villum Investogator Programme - Thomas Bjørnholm, Director of Science, Villum Fonden
  • 15:35 CUQI at DTU Compute - Per Christian Hansen, Professor and Villum Investigator, DTU Compute
  • 15:45 Classifying Stroke Using Electrical Impedance Tomography - Samuli Siltanen, Professor, Univ. of Helsinki & Honarary Professor at DTU Compute
  • 16:20 Closing Remarks - Per B. Brockhoff, Head of Department, DTU Compute

     

Contact

Yiqiu Dong
Associate Professor
DTU Compute
+45 45 25 31 08

Contact

Per Christian Hansen
Professor
DTU Compute
+45 45 25 30 97