Guillaume Sagnol

Guillaume Sagnol

Senior Optimization Consultant



Before joining FICO, I worked as a researcher at TU Berlin and Zuse Institute Berlin (Germany), where I led several projects in the fields of transportation and healthcare logistics. I have also worked in the field of Operations Research for telecommuniactions during my PhD studies (INRIA & École Polytechnique, France). This page holds a record of my academic journey.

  • Optimization under Uncertainty
  • Convex & Integer Optimization
  • Modelling
  • Machine Learning
  • Experimental Design
  • PhD in Systems & Controls, 2010

    INRIA & École Polytechnique, France

  • MSc in Engineering, 2007

    École des Mines de Paris, France


A list of all my publications can be found here.
(2023). Competitive Kill-and-Restart and Preemptive Strategies for Non-Clairvoyant Scheduling. Lecture Notes in Computer Science (LNCS) 13904, Proceedings of the 24th International Conference on Integer Programming and Combinatorial Optimization (IPCO 2023), pp. 246–260.


(2023). Fast Screening Rules for Optimal Design via Quadratic Lasso Reformulation. Journal of Machine Learning Research, 24(307), pp. 1–32.

PDF Cite

(2023). Spatiotemporal reconstruction of ancient road networks through sequential cost–benefit analysis. PNAS Nexus, 2(2), pgac313.


(2022). Competitive Algorithms for Symmetric Rendezvous on the Line. Proceedings of the 33rd ACM-SIAM Symposium on Discrete Algorithms (SODA 2022), pp. 329–347.


(2021). Restricted Adaptivity in Stochastic Scheduling. LIPIcs 204, 29th Annual European Symposium on Algorithms (ESA 2021), pp. 79:1–79:14.



Introduction to Linear and Discrete Optimization (ADM1)
This course introduces the topics of linear and discrete optimization, which are fundamental tools to model a variety of real-world problems.
Approximation Algorithms
This course provides an introduction to different techniques to design and analyze approximation algorithms for NP-hard problems.
Convex Optimization
This course presents the basics of convex optimization and conic programming, and reviews a large range of applications.
Optimal Design of Experiments
This course presents the computational aspects of Optimal Experimental Design.
Game Theory & Transportation
This course presents the basics of game theory and how it can be applied to study transportation networks.


Supervised PhD Theses:



I am the original author of PICOS, a Python Interface for Conic Optimisation Solvers. Maximilian Stahlberg did a fantastic job to refactor the code and to include robust optimization features.

PICOS is a user friendly interface to several conic and integer programming solvers, very much like YALMIP under MATLAB. The main motivation for PICOS is to have the possibility to enter an optimization problem as a high level model, and to be able to solve it with several different solvers. Multidimensional and matrix variables are handled in a natural fashion, which makes it painless to formulate a SDP or a SOCP in python. This is very useful for educational purposes, and to quickly implement some models and test their validity on simple examples. Furthermore, with PICOS you can take advantage of the python programming language to read and write data, construct a list of constraints by using python list comprehensions, take slices of multidimensional variables, etc.


  • guillaumesagnol <at> fico <dot> com
  • Fair Isaac Germany GmbH c/o Zuse Institute Berlin, Takustr. 7, 14195 Berlin,
  • FICO