Krzysztof Podgórski

ProfessorKnown as name: Krzysztof Podgórski

Research areas and keywords

UKÄ subject classification

  • Probability Theory and Statistics

Keywords

  • stochastic modelling, statistical inference, theory of probability distributions

Research

My research belongs to the area of broadly understood applied probability.
While I consider myself well-trained both in the probability theory and the theory of statistics, I have also extensive experience in modern computationally intense methods of statistics.
Throughout my career I took on a very wide spectrum of problems with both theoretical and applied flavor.
My contributions are in ergodic theory of stochastic processes, bootstrap methods for regression analysis, theory of heavy tailed distributions, non-Gaussian random fields, extreme value theory but also on occasions I have contributed to fields that are more distant to my main interests such as non-commutative probability, information theory, and methodology of science.
I have also a considerable number of publications in areas other than statistics and probability such as ocean and mechanical engineering, environmental, actuarial and medical sciences, where I have presented the developed models in a concrete empirical context.
I enjoy collaborative and interdisciplinary efforts and have a wide spectrum of collaborators both in the theoretical and applied fields.
My current research interests can be grouped into the following three general categories
1. theory and applications of multivariate non-Gaussian stochastic models,
2. statistical analysis of spatial random fields evolving in time with non-trivial dynamics,
3. statistical distributions at random crossing events.

While the focus each of the three areas is clearly distinct, typically and naturally in many of my work there is a strong overlap between two or even all three of them.
I am greatly interested in applications of the developed methodologies and thus proposed them in multidisciplinary research in: mechanical engineering (modeling road profiles and responses to them), ocean engineering (wave models, ship routes, storms), spatial econometrics (multivariate models for linkages between markets), actuarial sciences (non-Gaussian claim models), mathematical finance (detecting common jumps in high-frequency multivariate financial data).

Interests: Stochastic Processes, Random Spatio-temporal Fields, Non-Gaussian Stochastic Models, Applications to Engineering, Mathematical Finance, Environmental Sciences, Modelling of Sea Surfaces, Computationally Intensive Methods of Statistics

 

Teaching

1. Teaching/Course responsibility:

 

- Role: Responsible for the course, lecturer.

- Course name: Data mining and visualization

- Course code: STAN45

- Number of clock hours: 220

 

- Role: Responsible for the course, lecturer (developed for the first time jointly with Sreekar Vadrakar)

- Course name: Functional Data Analysis

- Course code: STAN46

- Number of clock hours: 100

 

2. Supervision of theses (including theses completed by Feb-19):

 

Undergraduate level (number of theses 2018):

*Evaluation of asset pricing models for the Swedish stock market, Ludvig Göransson, Kristoffer Bergram

*Time Series Modelling of OMXS30 Fundamental Valuations, Gustav Furenmo

 

Master level (number of theses 2018):

*Time Series Forecasting using Artificial Neural Networks, Simon Lofwander

* Neural networks to detect automated accounts ﴾bots﴿ on Twitter, Jan Novotny

 

3. Examiner of theses (including theses completed by Feb-19):

 

Undergraduate level (number of theses 2018):

 

Master level (number of theses 2018): 3

 

 

4. Briefly describe development work in education – renewal of curriculum and/or pedagogical development:

 

 

Prepared a project of Data Science and Analytics one-year Master program. The document has obtained a positive evaluation at the School level. Designing and development of new courses including Functional Data Analysis STAN46 (approved and officially offered in Spring 2019) and Deep Learning and Artificial Intelligence Methods STAN** (has passed the Departmental approval and waiting for the Educational Board approval, to be offered in Fall 2019).

 

 

5. Involvement of guest teachers:

- Course name: Advanced high dimensional statistical methods

- Course code: PhD - level

- Name of guest teacher and company/organization: Malgorzata Bogdan, The U

 

 

6. Programme responsibility (name of programme)

 

 

7. Additional work in education (e.g. planning, implementation, and pedagogical leadership)

 

 

 

8. Briefly describe your work with IT-supported teaching and learning

 

Using video recordings for the course on data mining and visualization that are avialable on Live@Lund. They accompany the enhanced interactive webpage for the course: http://krys.neocities.org/Teaching/DataMining/DataMining.html

 

 

9. Production of cases, publications on teaching and learning, teaching materials and textbooks

 

Recent research outputs

Kozubowski, T. J. & Krzysztof Podgórski, 2018 Jun, In : Extremes. 21, 2, p. 315-342

Research output: Contribution to journalArticle

Hossain, M. M., Kozubowski, T. J. & Krzysztof Podgórski, 2018 Feb 7, In : Communications in Statistics: Simulation and Computation. 47, 2, p. 392-412

Research output: Contribution to journalArticle

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