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Dr. Matthias Haeni - Personal Homepage
As a technical expert and data scientist with the main interest in collecting, analyzing and publishing great data sets, I believe in an increasing importance of "full stack data science", in essence taking control of the whole process beginning with sensor technology and going all the way to publishing high quality data.
In 2014, I completed my PhD (at the ETH with Nina Buchmann, Werner Eugster and Roman Zweifel) in the field of eddy covariance, highly resolved dendrometers and meterology in European forests. During this time, I also completed the Science and Policy graduate school at the Zurich-Basel Plant Science Center and spent several Short Term Scientific Missions in labs abroad.
Now at the WSL, I am involved in several "data heavy" projects (LWF Meteo, ICOS, TreeNet, Pfynwald, MODOEK). I am furthermore a Technical Consultant to the United Nations Convention to Combat Desertification (UNCCD) and I am a co-founder of a startup company from the University of Zurich, dealing with data science. Teaching is a passion of mine and since 2016 I am also a certified Software Carpentry and Data Carpentry instructor.
WSL contact for/member of:
My heart beats for working with all kinds of technical equipment and with all kinds of data sets, small or big, simple or complex, clean or chaotic, structured or unstructured. Data munging as well as conducting advanced statistics, predictive analyses, and visualizing data on paper, in webpages or on mobile devices are my specialty. Some of my favorite tools include R statistical software, python, R shiny, and more. Here are some "visible" examples of my work:
LWF meteo download tool: https://www.wsl.ch/apps/lwfmeteo/
TreeNet online processing tool: https://treenet.info/onlineproc
Fieldbook (online fieldbook for meteo stations): https://fieldbook.ch
WSL ozone risk assessment application: https://www.wsl.ch/apps/ozone/
MODOEK online visualisation data: https://www.wsl.ch/apps/MODOEK/
Ask me about technical stuff...
Processing, manipulating, visualizing all kinds of data (chaotic or not, quality checked or not, small or huge, etc.) with R, incl. R shiny, R markdown, R notebook; python; shell; relational and NoSQL databases, among others MySQL, Oracle, MS SQL Server, SQlite, InfluxDB, DynamoDB, MongoDB; managing AWS EC2 instances, azure virtual servers and on-premise services; big data processing with Hadoop, Spark on MapR, Hortonworks, AWS; LabVIEW Embedded; Alteryx, QlikView, Tableau; automatisation with cron, Airflow, UNIX services; planning and implementing data streams from sensor to vizualisation; machine learning (supervised/unsupervised); statistical modelling (clustering, classification, segmentation); anomaly detection/pattern recognition; data publications systems (CKAN and dataverse); documentation with git/github/bitbucket, sphinx, dokuwiki, Confluence.
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