CV Dr. Peter Gmeiner
Senior Machine Learning Engineer @GfK, Mathematician @AlgoBalanceGithub, LinkedIn, Xing, Google Scholar, Twitter: @PeterGmeiner4,
EMail: peter.gmeiner(at)algobalance.com
last update: 01/2023
Summary
A mathematician who is currently working in ML/AI, applying causal inference in practice (e.g. implementing a causal inference engine). I have years of experience in developing software as a fullstack software developer (Python, R, Java, C++, C#, SQL, Jupyter, PHP, HTML, Docker, Kubernetes, Spark, UML, ...). This technical knowledge is built on top of a foundational understanding of Machine Learning and AI techniques supplemented with years of research experience at university. My research interest is in causal inference, causal discovery, and in the connection between ML and causality.Positions
since 02/2019  Senior Machine Learning Engineer at GfK SE, Nürnberg, Germany Tasks:
 
11/201612/2018  Software engineer at Orpheus GmbH, Nürnberg, Germany Tasks:
 
since 04/2016  Managing director, software, and algorithm developer at AlgoBalance UG (haftungsbeschränkt), Germany Tasks:
 
04/2016  Cofounder of AlgoBalance UG (haftungsbeschränkt), Germany  
10/201509/2016  EXIST scholarship holder, idea provider and knowhow provider for the 'FoodOptimizer' project.  
08/201509/2015  Analysis algorithmic specialist at codemanufaktur GmbH, Erlangen, Germany Tasks:
 
11/201003/2015  Research assistant at the Department Mathematics of the University of ErlangenNürnberg, Germany Tasks:

Education
03/2015  PhD in Mathematics, University of ErlangenNürnberg, Thesis: 'Spectral Hypergraph Partitioning and Relative Entropy'  
10/2010  Diploma in Mathematics, University of ErlangenNürnberg, Thesis: 'Komplexitätsmaße und Emergenz'  
07/2005  intermediate diploma in computer science, University of Applied Sciences Nürnberg  
07/2003  subjectrelated advanced technical college entrance qualification (Fachabitur) 
Grants
10/201509/2016  government grant (EXIST) for the software project 'FoodOptimizer' (now called Nutrimizer) 
Publications
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[4]  P. Gmeiner, A. Mohapatra:  
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Talks
12/2022  Machine Learning for Causal Analysis, GfK's Causal Inference Engine, Talk together with Michael Grottke at Data Science Summit 2022.  
12/2022  Statistical Transportability between Markets, Causal Inference Meeting.  
12/2022  SemiMarkovian Causal Models, Talk at GfK Nürnberg.  
11/2022  Markovian Causal Models, Talk at GfK Nürnberg.  
06/2022  On (un)observed confounders in causal models, Talk at GfK Nürnberg.  
05/2022  On (un)observed confounders in causal models, Causal Inference Meeting.  
01/2022  Causal Effect Estimation with Double Machine Learning, Causal Inference Meeting.  
01/2022  Causal Effect Estimation with Double Machine Learning, we.create 2022, GfK Nürnberg.  
05/2021  Causal Inference Engine, Talk at GfK Nürnberg.  
01/2021  Statistical Transportability between Markets, we.create 2021, GfK Nürnberg.  
07/2020  Causal discovery with Point of Sales data, 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020), 2020.  
01/2020  Causal discovery with POS data and business knowledge, we.create 2020, GfK Nürnberg.  
03/2019  Causality: Applications and Connections to Machine Learning, Talk at GfK Nürnberg.  
03/2015  InformationTheoretic Cheeger Inequalities, Special Seminar, MPI Leipzig.  
04/2013  Vertex and edge coloring models, 'Arbeitsgemeinschaft' with topic 'Limits of Structures', Oberwolfach.  
09/2012  Some Properties of Persistent Mutual Information, European Conference on Complex Systems 2012 in Brussels.  
05/2012  Spektren von Laplaceoperatoren auf Hypergraphen, Forschungsseminar Analysis, Stochastik und Mathematische Physik, TU Chemnitz.  
04/2012  The classical KAM Theory, 'Arbeitsgemeinschaft' with topic 'Quasiperiodic Schrödinger Operators', Oberwolfach.  
03/2012  Überraschende Geometrie und Fraktale, Tag der Mathematik, Erlangen.  
10/2011  Quantum ergodicity for quantum graphs, 'Arbeitsgemeinschaft' with topic 'Quantum Ergodicity', Oberwolfach. 
Skills
Mathematical skills  graph theory, information theory, statistical inference, causal inference, statistics and probability theory, exponential families, Bayesian networks, calculus, algorithm development and analysis, optimization  
Methodological skills  (log)linear models, regression models, random forest, SVM, neural networks (especially deep learning), LDA, reinforcement learning, clustering methods, causal models, causal inference and discovery methods, GAM, hidden Markov models, natural language processing (Word2Vec, Fasttext), (non)linear optimization, particle swarm optimization  
Programming languages  C, C++, C#, SQL, Java, Visual Basic, HTML, PHP, Python, Javascript, jQuery, Latex, Matlab, Mathematica, Maple, R  
Application software  Office (MS Office, OpenOffice), Git, Dia, Apache  
Development environments  Eclipse, Visual Studio, Aptana Studio, RStudio, Xamarin Studio, Intellij IDEA, PyCharm, Jupyter, Hive  
Software design  UML, design patterns, object orientation, microservice architecture  
Operating systems  MS Windows, Linux (Debian), MacOS, Android, iOS  
Project management  Scrum, Agile Software Development  
Language skills  German (native), English (fluent)  
Other interests and hobbies  running (active), hiking, artificial intelligence, nutritional sciences, philosophy 