Activites and Courses

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.

PhD course: Bayesian Modeling and Computations for Inverse Problems

Sometime in 2021 (dates not settled yet)

Inverse problems formalize the process of learning about a system through indirect, noisy, and often incomplete observations. Casting inverse problems in the Bayesian statistical framework provides a natural framework for quantifying uncertainty in parameter values and model predictions, for fusing heterogeneous sources of information, and for optimally selecting experiments or observations. This short course will present fundamentals of the Bayesian approach to inverse problems, covering both modeling issues—e.g., prior distributions, likelihoods, hierarchical models—and computational challenges—e.g., computing posterior expectations via Markov chain Monte Carlo sampling or principled approximations. We will discuss methods that expose low-dimensional structure in inverse problems, that attempt to mitigate the computational cost of repeated forward model evaluations, and that exhibit discretization-invariant performance in large-scale problems.

Time permitting, we will also present Bayesian approaches to optimal experimental design, which attempt to answer ubiquitous questions of what or where to measure, what experimental conditions to employ, and so forth. Here we will introduce a decision theoretic Bayesian design formulation and link it to more classical alphabetic optimality criteria, then focus on computational issues, e.g., how to estimate and maximize expected information gain in various quantities of interest.

Ects: 2.5.
Time: One full week, sometime in 2021.
Teacher: Professor Youssef M. Marzouk, MIT.
Course responsible, contact: Associate Professor Mirza Karamehmedovic, DTU Compute.
More details: Link to DTU's course description, course no. 02969.

Register for the course: DTU students must register as usual via DTU's system; all other students must send an email to Mirza Karamehmedovid or Yiqiu Dong

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


Past Activities


PhD course: Computational Uncertainty Quantification for Inverse Problems

May 2020

We are deeply sorry, but we had to cancel this course due to the COVID-19 corona virus.

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:

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



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