Philipp Geiger

Address Upon request
Contact E-mail: upon request; web: geiger.onl; phone: upon request

Summary

Degrees Doctorate in computer science, diplom (~ MSc) in mathematics; graded "very good"
Research Machine learning, causal inference, time series, multi-agent/economic decisions
Application, teamwork Implemented congestion forecasting app, data-driven debugging in cloud computing; using Python, R, MySQL; in collaboration with researchers and engineers

Experience

04/2017 – present
Postdoc researcher
Max Planck Institute for Intelligent Systems, Tübingen, Germany
  • Leading research project on machine learning for efficient multi-agent facility usage
  • Implemented congestion forecasting web app for campus cafeteria in Python, MySQL
  • Applying game theory (Bayesian games, best-response dyn.), time series analysis (Kalman filtering, exponential smoothing, ridge regression, RNNs), data preprocessing
  • Collaborating with researchers, software engineers, work councils, privacy officers
07/2015 – 10/2015
Research intern
Microsoft Research Ltd., Cambridge, United Kingdom
  • Worked on AI simulation research project under Katja Hofmann

Education

06/2013 – 03/2017
Doctorate in computer science (equivalent to PhD)
Max Planck Institute for Intelligent Systems, Tübingen, and University of Stuttgart, Germany
  • Thesis title: "Causal models for decision making via integrative inference"
  • Grade: magna cum laude/"very good"
  • Supervisors: Bernhard Schölkopf, Dominik Janzing and Marc Toussaint
  • Focused on time series, quasi-experiments, counterfactuals and decision making
  • Applied Gaussian process regression to debugging problems in cloud computing and vector autoregressive processes to economic data (using Python, Matlab and R)
10/2006 – 12/2012
Diplom in mathematics (equivalent to MSc)
Heidelberg University and Humboldt University of Berlin, Germany
  • Thesis title: "Mutual Information and Gödel Incompleteness"
  • Grade: 1.4 (best score 1.0 of 5.0)/"very good"
  • Specialization: mathematical logic, theoretical computer science; minor: philosophy

Selected publications

Skills

Program-ming
  • Machine learning implementation (Gaussian process regression, ridge regression, Kalman filtering, exponential smoothing, vector autoregression and neural networks) with Python (working knowledge), TensorFlow, R, Matlab and MySQL (basic)
  • Object-oriented programming with Python (working knowledge), C++ (basic)
Communi-cating
  • Presenting and explaining data, insights and results using PowerPoint, LaTeX, HTML
  • Coordinating with diverse stakeholders from customers and manufacturers over researchers and software engineers to work councils and privacy officers
  • Languages: German (native), English (fluent), French (beginner)

Supervision, teaching and reviewing

10/2016 – 03/2017
Supervisor
  • Student: Claudius Proissl (University of Stuttgart); research project during MSc
10/2013 – 02/2014
Teaching assistant
University of Tübingen, Germany
  • Lecture "Intelligent Systems I": a first course in machine learning
10/2011 – 04/2012
Teaching assistant
Heidelberg University, Germany
  • Lecture "Computability and Computational Complexity Theory I"
10/2014 – present
Reviewer
  • Conferences: NIPS ('14, '17), ICML ('16, '17), UAI ('16, '17)
  • Journals: ACM TIST, IEEE PAMI, IEEE TKDE, IJDSA

Memberships and awards

09/2015 – 06/2017 Associate Doctoral Fellow of Max Planck ETH Center for Learning Systems
07/2005 Award for outstanding results in physics by German Physical Society (DPG)

References

Prof. Bernhard Schölkopf Max Planck Institute for Intelligent Systems, Tübingen, Germany
Dr. Katja Hofmann Microsoft Research Ltd., Cambridge, United Kingdom
Dr. Wolfgang Merkle Heidelberg University, Germany