In January 2020, Dr. Bedartha Goswami joined the Cluster of Excellence 'Machine Learning' at the University of Tübingen as head of the Independent Research Group "Machine Learning in Climate Science" (MLCS).
The “MLCS" research group aims to understand the interactions between different components of the climate system based on present-day and paleoclimatic data sets used along with the output of climate models. Climatic phenomena of interest include the El Niño Southern Oscillation (ENSO), the Global Monsoon (GM), and the Inter-Tropical Convergence Zone (ITCZ). The changes in the state of these climatic systems are consequential to people all around the planet, and they inform socio-economic decisions at all levels of societal organization. The ideas we use to infer hidden structure in climate data fall broadly under the category of Machine Learning (ML) approaches. ML concepts are inherently suited to the task as they are designed to identify, classify, and predict complex patterns.
Bedartha Goswami has worked at the Potsdam Institute for Climate Impact Research (PIK) with Dr. Norbert Marwan on a DFG funded project (2017-2020) that focuses on the development of a new framework to work with uncertainties in climate data. Prior to that, he has worked with Prof. Dr. Bodo Bookhagen at the Institute of Geosciences, University of Potsdam, on characterising spatial patterns of hydrology in the greater Himalayas and also on estimating flow accumulation from lidar point clouds (2016-2017). Bedartha did his PhD (2015) under the supervision of Prof. Dr. Dr. h.c. Jürgen Kurths at PIK, on developing new ways to deal with uncertainties in sedimentary proxy records obtained from paleoclimate archives such as stalagmites, lake sediments, and ice cores.
Dr. Bedartha Goswami
Machine Learning in Climate Science
Cluster of Excellence "Machine Learning"
Maria-von-Linden-Str. 6, 4th floor
Room No. 40-5/A10
+49 7071 2970894