Dr. Imme Ebert-Uphoff       Photo of
                            Imme Ebert-Uphoff

Research Faculty
Department of Electrical and Computer Engineering
Colorado State University
Fort Collins, CO 80523
Email: iebert at engr . colostate . edu
This page: http://www.engr.colostate.edu/~iebert/ 
Bio

Curriculum Vitae (pdf)  

Publications

                          



Building bridges between Data Science and Geosciences:  

I strongly believe in the importance of building a research community that bridges the fields of data science and geoscience, to ensure that all new developments in data science make it into the geosciences as quickly as possible.  Given the rapidly increasing amount of observational and model data in the geosciences, and the fact that many of our society's biggest problems are related to climate change, it is crucial that we employ all suitable data analysis tools to help geoscientists answer as many of their science questions as possible from the data. 

While community building work is not very glamorous, it is important, rewarding, and invigorating to work with researchers from different disciplines.  I learn something new almost every day!

To that end I have been heavily involved in the following activities.

1) CLIMATE INFORMATICS COMMUNITY:

2) IS-GEO COMMUNITY:

3) OTHER:




Research activities:

Selected invited talks (many with video recordings):

Selected research links:
  1. Benchmark data sets for causal discovery:  http://www.engr.colostate.edu/~iebert/DATA_SETS_CAUSAL_DISCOVERY/ .  This page contains several data sets for comparison of different algorithms for causal discovery from spatio-temporal data.  
  1. Climateinformatics.org - Anything related to climate informatics.
  1. www.DataOnStage.com: A page with extensions I developed for existing Bayesian Networks software.
  1. Sept 2014: First 3D results for graphs of information flow: We developed a high efficiency implementation of the PC and PC stable algorithms in C that can handle many more variables than the standard implementations in Java, Matlab and R.  Using that we can now derive graphs of information flow of our planet's climate in 3D, i.e. including several atmospheric height layers at a time.  For first figures, see this tech report:  CSU Tech report, Sept 3, 2014. 



My current Research Interests consist of the following, partially overlapping, topics:
1.  Causal discovery:  finding causal relationships in a system from data

2.  Climate Informatics: discovering new knowledge from climate data

3.  Bayesian networks and other graphical models:  any applications of Bayesian networks in science and engineering (for causal discovery and other purposes)



1.  What is Causal discovery?
Causal discovery seeks to recover causal relationships between variables in a system based on observational data.  Many approaches use Bayesian Networks or Markov Networks (my preferred methods).  Other methods exist, e.g. based on Granger causality.  Currently I use these methods primarily in the context of climate data, because there is still so much about climate that we do not yet understand while there is an increasing amount of climate data available - making this an ideal application of causal discovery. 

The process of causal discovery can never prove any causal relationships with certainty, primarily because there can always be unknown common causes.  However, we can come up with a set of most likely causal hypotheses based on data that we can then turn back over to the domain expert for consideration.  Thus causal discovery is always a process that involves both domain experts and AI experts, working together.

Causal Discovery in recent news:
The ACM A.M. Turing Award is an annual prize given by the Association for Computing Machinery (ACM) to "an individual selected for contributions of a technical nature made to the computing community". It is stipulated that "The contributions should be of lasting and major technical importance to the computer field". The Turing Award is recognized as the "highest distinction in Computer science" and "Nobel Prize of computing". (cited from wikipedia)

Judea Pearl was awarded the 2011 ACM Touring Award for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning. 
Press release:  Pearl - Touring Award 2011
The Nobel Prize in Economics in 2011 was awarded to Thomas J. Sargent and Christopher A. Sims for their work on Cause and effect in the macroeconomy.
Press release:  Sargent and Sims - Nobel Economics 2011



2.  What is Climate Informatics?
Climate informatics seeks to apply techniques from machine learning and artificial intelligence (AI) to discover new knowledge from climate data.  There are many other names for this discipline, such as knowledge discovery in climate.  The term Climate Informatics emphasizes the fact we can view the climate as a system that produces information and then analyze that information to learn about the system.  This viewpoint allows us to use observational climate data and apply algorithms from information science that have not yet been widely applied in climate science.  Of course, viewing climate as an information system is not a completely new approach.  Statistical methods - and to a smaller extend some machine learning methods - have been used to analyze climate for quite some time.  What is new is the explicit call for more members from the computer science/mathematics/economics community to collaborate with climate scientists and to apply a much greater variety of machine learning algorithms to climate science.

Some helpful pages on this new initiative:

My personal interest within climate informatics is currently to apply causal discovery to find new causal hypotheses from climate data.  For example, we can define climate networks (a connected grid around the globe) based on graphical models to track information flow around the globe.  We can also try to find causal relationships between different modes (such as WPO, EPO, PNA and NAO).  Most of my current work deals with temporal models, i.e. we also try to identify the time frames in which variables may "cause" each other.  (See my publications.)

For example, the figure below shows likely causal hypotheses we found for the interaction between modes WPO, EPO, PNA and NAO.  Arrows indicate the direction of information flow and the numbers next to the arrows indicate time delays in days (Ebert-Uphoff and Deng, 2012a).

Graph showing causal hypotheses
        between WPO, EPO, NAO and PNA



The figure below shows plots of climate networks we generated using geopotential height data around the globe, using a graphical model approach to calculate the links (Ebert-Uphoff and Deng, 2012b).  The individual plots below show the links that connect geographical location over a time span of 0, 1, 2 and 3 days.

Network plots for delays of 0, 1, 2
        and 3 days.




3.  What are Bayesian Networks?

Bayesian Networks (BNs) are a tool for modeling systems containing uncertainty.  They have gained much popularity in recent years.  BNs use tools from probability theory (primarily Bayes' theorem, which gave them their name) to solve various tasks in the areas of data mining and artificial intelligence. 
They have been used for applications ranging from the natural sciences (e.g. meteorology, volcano eruption, water management) to medicine (e.g. cancer diagnosis), computer science (spam filter, image processing, text mining), engineering (e.g. reliability analysis, printer diagnosis, electric load forecasting) to computational biology (e.g. gene/protein interactions, cellular biology).

For some very accessible introductions to Bayesian networks, see 

Bayesian networks can be used for a number of purposes, namely
(1) to generate predictions,
(2) for diagnosis,
(3) as policy and decision making tool and
(4) for causal discovery.

My goal is to extend the use of Bayesian networks for applications in science and engineering. 



My past research interests

I used to work in the area of robotics (from 1993 to 2006), primarily in the area of theoretical kinematics
Past topics include (see publications):




My Goal: 


To take Causal Discovery
where no Causal Discovery algorithm has gone before.



Last updated: April 2017.