Welcome!
My name is Mario Teixeira Parente. This website contains some contents of my academic life.
From 10/201609/2020, I was a PhD student at the Chair for Numerical Mathematics (M2) in the Department of Mathematics of the Technical University Munich (TUM). My research focus lied on Uncertainty Quantification (UQ), a field from applied mathematics using tools from statistics, probability theory, and numerics.
Currently, I am a postdoc in the DataDriven Discovery (DDD) Group of the Jülich Centre for Neutron Science (JCNS) outstation within the Heinz MaierLeibnitz Zentrum (MLZ) at the neutron source FRM II in Garching near Munich. The focus of my work is the investigation of machine learning methods for neutron scattering data.
At the moment, I am also a lecturer at the Department of Computer Science and Mathematics of the Munich University of Applied Sciences (HM). I offer a course on the fundamentals of UQ for advanced bachelor students and a freshmen course on linear algebra.
Feel free to rummage around or subscribe! You can find recent posts right below this text.
Posts

Mentor for the Max Weber Program

Linear algebra course at HM

CAMERA Workshop

Bayes' theorem and medical screening tests

Scientific Computing: attempting a definition

UQ course at HM

LENS ISIS Machine Learning School

Start at JCNS4 with AINX

PhD defense: Passed

Submission of my PhD thesis

Sensitivities in SEIR models: a (very) quick investigation

Published: Generalized bounds for active subspaces

Revised preprint: Generalized bounds for active subspaces

Accepted: Identifying relevant hydrological and catchment properties in active subspaces

Preprint: Generalized bounds for active subspaces

Another research stay at Lund University

Accepted: Bayesian calibration and sensitivity analysis for a karst aquifer model using active subspaces

Research week at Lund University

Research stay at UT Austin

Preprint (update): On the relationship between parameters and discharge data for a lumped karst aquifer model

Preprint: Bayesian calibration and sensitivity analysis for a karst aquifer model using active subspaces

Ferienakademie 2018

Preprint: A probabilistic framework for approximating functions in active subspaces

Preprint: On the relationship between parameters and discharge data for a lumped karst aquifer model

Accepted: Efficient parameter estimation for a methane hydrate model with active subspaces

ProLehre compact course, part IV

ProLehre teaching consulting

Revised preprint: Efficient parameter estimation for a methane hydrate model with active subspaces

Kickoff for ExaQUte

ProLehre compact course, part III

Feedback for teaching

ProLehre compact course, part II

IGSSE Forum 2018

ProLehre compact course, part I

Start of lectures in summer term 2018

Symbol for UNMIX

Kickoff for ProLehre compact course at MA

Kickoff for UNMIX

Talk in M2 Oberseminar

End of lectures in winter term 2017/18

Preprint: Efficient parameter estimation for a methane hydrate model with active subspaces