Jakob Zscheischler

Alumnus

Alumni

Empirical Inference

Scientific Interests

I'm working at the interface between machine learning and environmental science. My research currently focuses on the detection of extreme events in Earth observation data (such as fAPAR, NDVI, carbon fluxes). The question of how climate extremes affect ecosystems is of high importance for understanding climate-carbon feedbacks and hence future climate predictions. I try to identify large-scale extremes in impact variables and trace them back to climatic drivers and external disturbances.

I'm also part of the Department for Biogeochemical Integration at the MPI for Biogeochemistry in Jena.

Education

2010-	PhD Student both at MPI for Intelligent Systems in Tuebingen and MPI for Biogeochemistry in Jena
2010	Diploma in Mathematics at Humboldt University of Berlin
2009-2010	Diploma Thesis at MPI for Biological Cybernetics in Tuebingen
2003	Abitur at Kreuzschule Dresden

Professional Experience

2013	Visiting Researcher at the group of Anna Michalak at Carnegie Institution for Science, Department for Global Ecology, Stanford, CA, USA
2009	Internship at Potsdam Institute for Climate Impact Research (PIK)
2007	Internship at Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), Manaus, Brazil
2003-2004	Civilian Service (old people's home)

Scholarships and Awards

2006-2010	Scholar of the Evangelisches Studienwerk Villigst
2010	Coauthor of "Inferring Deterministic Causal Relations", winner of the Best Student Paper Award at UAI, 2010

Personal Interests

Classical Music, Singing, Football, Climate Change, Hiking

16 results (View BibTeX file of all listed publications)

2016

Distinguishing cause from effect using observational data: methods and benchmarks

Mooij, J., Peters, J., Janzing, D., Zscheischler, J., Schölkopf, B.

Journal of Machine Learning Research, 17(32):1-102, 2016 (article)

link (url) [BibTex]

2016

Mooij, J., Peters, J., Janzing, D., Zscheischler, J., Schölkopf, B. Distinguishing cause from effect using observational data: methods and benchmarks Journal of Machine Learning Research, 17(32):1-102, 2016 (article)

link (url) [BibTex]

2014

Impact of Large-Scale Climate Extremes on Biospheric Carbon Fluxes: An Intercomparison Based on MsTMIP Data

Global Biogeochemical Cycles, 2014 (article)

Web DOI [BibTex]

2014

Zscheischler, J., Michalak, A., Schwalm, M., Mahecha, M., Huntzinger, D., Reichstein, M., Berthier, G., Ciais, P., Cook, R., El-Masri, B., Huang, M., Ito, A., Jain, A., King, A., Lei, H., Lu, C., Mao, J., Peng, S., Poulter, B., Ricciuto, D., Shi, X., Tao, B., Tian, H., Viovy, N., Wang, W., Wei, Y., Yang, J., Zeng, N. Impact of Large-Scale Climate Extremes on Biospheric Carbon Fluxes: An Intercomparison Based on MsTMIP Data Global Biogeochemical Cycles, 2014 (article)

Web DOI [BibTex]

Extreme events in gross primary production: a characterization across continents

Zscheischler, J., Reichstein, M., Harmeling, S., Rammig, A., Tomelleri, E., Mahecha, M.

Biogeosciences, 11, pages: 2909-2924, 2014 (article)

PDF Web DOI [BibTex]

Zscheischler, J., Reichstein, M., Harmeling, S., Rammig, A., Tomelleri, E., Mahecha, M. Extreme events in gross primary production: a characterization across continents Biogeosciences, 11, pages: 2909-2924, 2014 (article)

PDF Web DOI [BibTex]

An extended approach for spatiotemporal gapfilling: dealing with large and systematic gaps in geoscientific datasets

v Buttlar, J., Zscheischler, J., Mahecha, M.

Nonlinear Processes in Geophysics, 21(1):203-215, 2014 (article)

PDF DOI [BibTex]

v Buttlar, J., Zscheischler, J., Mahecha, M. An extended approach for spatiotemporal gapfilling: dealing with large and systematic gaps in geoscientific datasets Nonlinear Processes in Geophysics, 21(1):203-215, 2014 (article)

PDF DOI [BibTex]

Factors controlling decomposition rates of fine root litter in temperate forests and grasslands

Solly, E., Schöning, I., Boch, S., Kandeler, E., Marhan, S., Michalzik, B., Müller, J., Zscheischler, J., Trumbore, S., Schrumpf, M.

Plant and Soil, 2014 (article)

PDF DOI [BibTex]

Solly, E., Schöning, I., Boch, S., Kandeler, E., Marhan, S., Michalzik, B., Müller, J., Zscheischler, J., Trumbore, S., Schrumpf, M. Factors controlling decomposition rates of fine root litter in temperate forests and grasslands Plant and Soil, 2014 (article)

PDF DOI [BibTex]

A few extreme events dominate global interannual variability in gross primary production

Zscheischler, J., Mahecha, M., v Buttlar, J., Harmeling, S., Jung, M., Rammig, A., Randerson, J., Schölkopf, B., Seneviratne, S., Tomelleri, E., Zaehle, S., Reichstein, M.

Environmental Research Letters, 9(3):035001, 2014 (article)

PDF Web DOI [BibTex]

Zscheischler, J., Mahecha, M., v Buttlar, J., Harmeling, S., Jung, M., Rammig, A., Randerson, J., Schölkopf, B., Seneviratne, S., Tomelleri, E., Zaehle, S., Reichstein, M. A few extreme events dominate global interannual variability in gross primary production Environmental Research Letters, 9(3):035001, 2014 (article)

PDF Web DOI [BibTex]

A global analysis of extreme events and consequences for the terrestrial carbon cycle

Zscheischler, J.

Diss. No. 22043, ETH Zurich, Switzerland, ETH Zurich, Switzerland, 2014 (phdthesis)

[BibTex]

Zscheischler, J. A global analysis of extreme events and consequences for the terrestrial carbon cycle Diss. No. 22043, ETH Zurich, Switzerland, ETH Zurich, Switzerland, 2014 (phdthesis)

[BibTex]

2013

Climate Extremes and the Carbon Cycle

Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M., Seneviratne, S., Zscheischler, J., Beer, C., Buchmann, N., Frank, D., Papale, D., Rammig, A., Smith, P., Thonicke, K., van der Velde, M., Vicca, S., Walz, A., Wattenbach, M.

Nature, 500, pages: 287-295, 2013 (article)

DOI [BibTex]

2013

DOI [BibTex]

Detection and attribution of large spatiotemporal extreme events in Earth observation data

Zscheischler, J., Mahecha, M., Harmeling, S., Reichstein, M.

Ecological Informatics, 15, pages: 66-73, 2013 (article)

Abstract

Latest climate projections suggest that both frequency and intensity of climate extremes will be substantially modified over the course of the coming decades. As a consequence, we need to understand to what extent and via which pathways climate extremes affect the state and functionality of terrestrial ecosystems and the associated biogeochemical cycles on a global scale. So far the impacts of climate extremes on the terrestrial biosphere were mainly investigated on the basis of case studies, while global assessments are widely lacking. In order to facilitate global analysis of this kind, we present a methodological framework that firstly detects spatiotemporally contiguous extremes in Earth observations, and secondly infers the likely pathway of the preceding climate anomaly. The approach does not require long time series, is computationally fast, and easily applicable to a variety of data sets with different spatial and temporal resolutions. The key element of our analysis strategy is to directly search in the relevant observations for spatiotemporally connected components exceeding a certain percentile threshold. We also put an emphasis on characterization of extreme event distribution, and scrutinize the attribution issue. We exemplify the analysis strategy by exploring the fraction of absorbed photosynthetically active radiation (fAPAR) from 1982 to 2011. Our results suggest that the hot spots of extremes in fAPAR lie in Northeastern Brazil, Southeastern Australia, Kenya and Tanzania. Moreover, we demonstrate that the size distribution of extremes follow a distinct power law. The attribution framework reveals that extremes in fAPAR are primarily driven by phases of water scarcity.

Web DOI [BibTex]

Zscheischler, J., Mahecha, M., Harmeling, S., Reichstein, M. Detection and attribution of large spatiotemporal extreme events in Earth observation data Ecological Informatics, 15, pages: 66-73, 2013 (article)

Web DOI [BibTex]

A Guided Hybrid Genetic Algorithm for Feature Selection with Expensive Cost Functions

Jung, M., Zscheischler, J.

In Proceedings of the International Conference on Computational Science, 18, pages: 2337 - 2346, Procedia Computer Science, (Editors: Alexandrov, V and Lees, M and Krzhizhanovskaya, V and Dongarra, J and Sloot, PMA), Elsevier, Amsterdam, Netherlands, ICCS, 2013 (inproceedings)

Web DOI [BibTex]

Jung, M., Zscheischler, J. A Guided Hybrid Genetic Algorithm for Feature Selection with Expensive Cost Functions In Proceedings of the International Conference on Computational Science, 18, pages: 2337 - 2346, Procedia Computer Science, (Editors: Alexandrov, V and Lees, M and Krzhizhanovskaya, V and Dongarra, J and Sloot, PMA), Elsevier, Amsterdam, Netherlands, ICCS, 2013 (inproceedings)

Web DOI [BibTex]

2012

Climate classifications: the value of unsupervised clustering

Zscheischler, J., Mahecha, M., Harmeling, S.

In Proceedings of the International Conference on Computational Science , 9, pages: 897-906, Procedia Computer Science, (Editors: H. Ali, Y. Shi, D. Khazanchi, M. Lees, G.D. van Albada, J. Dongarra, P.M.A. Sloot, J. Dongarra), Elsevier, Amsterdam, Netherlands, ICCS, June 2012 (inproceedings)

Abstract

Classifying the land surface according to dierent climate zones is often a prerequisite for global diagnostic or predictive modelling studies. Classical classifications such as the prominent K¨oppen–Geiger (KG) approach rely on heuristic decision rules. Although these heuristics may transport some process understanding, such a discretization may appear “arbitrary” from a data oriented perspective. In this contribution we compare the precision of a KG classification to an unsupervised classification (k-means clustering). Generally speaking, we revisit the problem of “climate classification” by investigating the inherent patterns in multiple data streams in a purely data driven way. One question is whether we can reproduce the KG boundaries by exploring dierent combinations of climate and remotely sensed vegetation variables. In this context we also investigate whether climate and vegetation variables build similar clusters. In terms of statistical performances, k-means clearly outperforms classical climate classifications. However, a subsequent stability analysis only reveals a meaningful number of clusters if both climate and vegetation data are considered in the analysis. This is a setback for the hope to explain vegetation by means of climate alone. Clearly, classification schemes like K¨oppen-Geiger will play an important role in the future. However, future developments in this area need to be assessed based on data driven approaches.

Web DOI [BibTex]

2012

Zscheischler, J., Mahecha, M., Harmeling, S. Climate classifications: the value of unsupervised clustering In Proceedings of the International Conference on Computational Science , 9, pages: 897-906, Procedia Computer Science, (Editors: H. Ali, Y. Shi, D. Khazanchi, M. Lees, G.D. van Albada, J. Dongarra, P.M.A. Sloot, J. Dongarra), Elsevier, Amsterdam, Netherlands, ICCS, June 2012 (inproceedings)

Web DOI [BibTex]

Information-geometric approach to inferring causal directions

Janzing, D., Mooij, J., Zhang, K., Lemeire, J., Zscheischler, J., Daniušis, P., Steudel, B., Schölkopf, B.

Artificial Intelligence, 182-183, pages: 1-31, May 2012 (article)

Abstract

While conventional approaches to causal inference are mainly based on conditional (in)dependences, recent methods also account for the shape of (conditional) distributions. The idea is that the causal hypothesis “X causes Y” imposes that the marginal distribution PX and the conditional distribution PY|X represent independent mechanisms of nature. Recently it has been postulated that the shortest description of the joint distribution PX,Y should therefore be given by separate descriptions of PX and PY|X. Since description length in the sense of Kolmogorov complexity is uncomputable, practical implementations rely on other notions of independence. Here we define independence via orthogonality in information space. This way, we can explicitly describe the kind of dependence that occurs between PY and PX|Y making the causal hypothesis “Y causes X” implausible. Remarkably, this asymmetry between cause and effect becomes particularly simple if X and Y are deterministically related. We present an inference method that works in this case. We also discuss some theoretical results for the non-deterministic case although it is not clear how to employ them for a more general inference method.

Web DOI [BibTex]

Janzing, D., Mooij, J., Zhang, K., Lemeire, J., Zscheischler, J., Daniušis, P., Steudel, B., Schölkopf, B. Information-geometric approach to inferring causal directions Artificial Intelligence, 182-183, pages: 1-31, May 2012 (article)

Web DOI [BibTex]

Significant global reduction of carbon uptake by water-cycle driven extreme vegetation anomalies

Zscheischler, J., Mahecha, M., von Buttlar, J., Harmeling, S., Jung, M., Randerson, J., Reichstein, M.

Nature Geoscience, 2012 (article) In revision

[BibTex]

Zscheischler, J., Mahecha, M., von Buttlar, J., Harmeling, S., Jung, M., Randerson, J., Reichstein, M. Significant global reduction of carbon uptake by water-cycle driven extreme vegetation anomalies Nature Geoscience, 2012 (article) In revision

[BibTex]

2011

Testing whether linear equations are causal: A free probability theory approach

Zscheischler, J., Janzing, D., Zhang, K.

In pages: 839-847, (Editors: Cozman, F.G. , A. Pfeffer), AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI), July 2011 (inproceedings)

Abstract

We propose a method that infers whether linear relations between two high-dimensional variables X and Y are due to a causal influence from X to Y or from Y to X. The earlier proposed so-called Trace Method is extended to the regime where the dimension of the observed variables exceeds the sample size. Based on previous work, we postulate conditions that characterize a causal relation between X and Y . Moreover, we describe a statistical test and argue that both causal directions are typically rejected if there is a common cause. A full theoretical analysis is presented for the deterministic case but our approach seems to be valid for the noisy case, too, for which we additionally present an approach based on a sparsity constraint. The discussed method yields promising results for both simulated and real world data.

PDF Web [BibTex]

2011

Zscheischler, J., Janzing, D., Zhang, K. Testing whether linear equations are causal: A free probability theory approach In pages: 839-847, (Editors: Cozman, F.G. , A. Pfeffer), AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI), July 2011 (inproceedings)

PDF Web [BibTex]

2010

Inferring High-Dimensional Causal Relations using Free Probability Theory

Zscheischler, J.

Humboldt Universität Berlin, Germany, August 2010 (diplomathesis)

PDF [BibTex]

2010

Zscheischler, J. Inferring High-Dimensional Causal Relations using Free Probability Theory Humboldt Universität Berlin, Germany, August 2010 (diplomathesis)

PDF [BibTex]

Inferring deterministic causal relations

Daniusis, P., Janzing, D., Mooij, J., Zscheischler, J., Steudel, B., Zhang, K., Schölkopf, B.

In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pages: 143-150, (Editors: P Grünwald and P Spirtes), AUAI Press, Corvallis, OR, USA, UAI, July 2010 (inproceedings)

Abstract

We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.

PDF Web [BibTex]

Daniusis, P., Janzing, D., Mooij, J., Zscheischler, J., Steudel, B., Zhang, K., Schölkopf, B. Inferring deterministic causal relations In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pages: 143-150, (Editors: P Grünwald and P Spirtes), AUAI Press, Corvallis, OR, USA, UAI, July 2010 (inproceedings)

PDF Web [BibTex]