Recent Publications

This paper demonstrates how different machine learning techniques performed on a recent, partially labeled dataset (based on the Locked …


April 2018 – July 2019

Supervisor of Master Students

Vrije Universiteit Amsterdam

I supervised several students who were doing an internship in the field of anomaly detection to obtain their master’s degree. Companies such as Royal Dutch Airlines (KLM), Takeaway and the Dutch Ministry of the Interior and Kingdom Relations were involved in these graduation projects. My task was to give advise to the student about their research and to provide feedback on their master’s thesis.
April 2017 – December 2018

Teacher’s Assistant

Vrije Universiteit Amsterdam

I was teacher’s assistant for the courses Experimental Design and Data Analysis (2017) and Statistical Models (2017 and 2018) at the Vrije Universiteit. The first course is intended for master students Artificial Intelligence and Computer Science, while the second course is aimed at master students Business Analytics and Mathematics. My duties consisted of supervising exercise classes, helping students with their assignments and grading their homework and exams.


Course: Kaleidoscope Data Science

Netherlands Research School for Information and Knowledge Systems

Data Science is one of the most penetrating research trends of the last few years, and is finding more and more applications. Data Science techniques are also getting to be used as an instrument for other research purposes. The objective of this tutorial is to provide a kaleidoscopic overview of current trends, for both students in data science and students whose research is not primarily data science. Some presentations will take an application or industry perspective, some present ongoing research. The first day features several presentations on the data aspect (data quality, data cleansing), the second day is focused on analytics (such as an introduction into deep learning).
Feb 2018 – May 2018

Course: Stochastic Programming

Dutch Network on the Mathematics of Operations Research

Stochastic programming is a framework for modelling optimization problems that involve uncertainty. Whereas deterministic optimization problems are formulated with known parameters, real world problems almost invariably include some unknown parameters. When the parameters are known only within certain bounds, one approach to tackling such problems is called robust optimization. Here the goal is to find a solution which is feasible for all such data and optimal in some sense. Stochastic programming models are similar in style but take advantage of the fact that probability distributions governing the data are known or can be estimated. The goal here is to find some policy that is feasible for all (or almost all) the possible data instances and maximizes the expectation of some function of the decisions and the random variables. More generally, such models are formulated, solved analytically or numerically, and analyzed in order to provide useful information to a decision-maker. The most widely applied and studied stochastic programming models are two-stage linear programs. Here the decision maker takes some action in the first stage, after which a random event occurs affecting the outcome of the first-stage decision. A recourse decision can then be made in the second stage that compensates for any bad effects that might have been experienced as a result of the first-stage decision. The optimal policy from such a model is a single first-stage policy and a collection of recourse decisions (a decision rule) defining which second-stage action should be taken in response to each random outcome.

Course: Taking Charge of your PhD

ElroyCOM Training

This course intends to help PhD-students to manage their project effecitvely by optimising the success factors (technical conditions, supervision, planning, social and expert network) and practising personal communication skills. These personal communication techniques, involving feedback, conflicts, negotiations, and other subjects are practised with an actor.

Course: Foundations of Data Science

Netherlands Research School for Information and Knowledge Systems

Course: Research Methods and Methodology for IKS

Netherlands Research School for Information and Knowledge Systems

The primary goal of this hands-on course is to enable Ph.D. students to make a good research design for their own research project. To this end, it provides an interactive training in various elements of research design, such as the conceptual design and the research planning. But the course also contains a general introduction to the philosophy of science (and particularly to the philosophy of mathematics, computer science and AI). And, it addresses such divergent topics as ‘the case-study method’, ‘elementary research methodology for the empirical sciences’ and ‘empirical methods for computer science’.
Sep 2017 – Nov 2017

Course: Markov Decision Processes

Dutch Network on the Mathematics of Operations Research

The theory of Markov decision processes (MDPs) - also known under the names sequential decision theory, stochastic control or stochastic dynamic programming - studies sequential optimization of stochastic systems by controlling their transition mechanism over time. Each control policy defines a stochastic process and values of objective functions associated with this process. The goal is to select a control policy that optimizes a function of the values generated by the utility functions. In real life, decisions that are made usually have two types of impact. Firstly, they cost or save resources, such as money or time. Secondly, by influencing the dynamics of the system they have an impact on the future as well. Therefore, the decision with the largest immediate profit may not be good in view of future rewards in many situations. MDPs model this paradigm and can be used to model many important applications in practice. In this course we provide results on the structure and existence of good policies, on methods for the computation of optimal policies, and illustrate them by applications.

Course: Awareness of Scientific Integrity

Centrum Wiskunde & Informatica

This course deals with several topics on research integrity and ethics in academia. Several people starting with our director will present on different topics. After the talks we will divide into groups and discuss some ethical dilemmas that can occur in the academic environment.
Feb 2017 – May 2017

Course: Randomized Algorithms

Dutch Network on the Mathematics of Operations Research

Randomness has proven itself to be a useful resource for developing provably efficient algorithms and protocols. This course will explore examples from a variety of settings and problem areas such as graph algorithms, algorithms in algebra, approximate counting, probabilistically checkable proofs, algorithms for big data, and matrix algorithms. Topics also include an introduction to tools from probability theory, including matringales, Chernoff bounds and Lovasz Local lemma.
Nov 2016 – Feb 2017

Course: Networks and Semidefinite Programming

Dutch Network on the Mathematics of Operations Research

Combinatorial optimization problems are concerned with the efficient allocation of limited resources to meet desired objectives when the values of the variables are restricted to be integral. Such problems arise in various applications, e.g., airline crew scheduling, manufacturing, network design, cellular telephone frequency design, and they can often be modeled as optimization problems on graphs. The course deals with several basic combinatorial optimization problems. While these problems are intrinsically hard to solve in general, we will present polynomial-time solvable instances. Algorithms use combinatorial tools, linear and semidefinite programming.
Nov 2016 – Feb 2017

Course: Cooperative Games

Dutch Network on the Mathematics of Operations Research

Game theory studies interactive decision situations involving conflict and/or cooperation. In cooperative games binding agreements are allowed and the players may form coalitions. The focus is on the question how to reallocate the resulting joint coalitional payoff among the players in a fair way.