Courses

In this page, I will introduce the content of the postgraduate courses that I have finished during my master's.

2024 Spring


Master Project Mathematics (UvA) - 36EC


Coding Theory (Mastermath and DIAMANT) - 8EC

This course offers an introduction to the mathematical theory of error correction (encoding and decoding information using mathematics). The main aims are:

Tentative list of topics: communication channels, the Hamming distance, error-correcting codes, bounds, Reed-Solomon codes, Reed-Muller codes, decoding, duality theory of codes, locality, DNA data storage, rank-metric codes.


Queueing Theory (Mastermath and LNMB) - 6EC

2023 Fall


Algorithmic Game Theory (UvA - Master of Logic) - 6EC

The overall aim of the course is to get familiar with fundamental methodologies, techniques and results in AGT. We study the existence, computation and inefficiency of equilibria (ranging from pure Nash equilibria to learning outcomes) for various classes of games. We also consider the problem of designing coordination mechanisms to reduce the inefficiency. Further, we touch upon the computational limitations that arise in classic auction theory, and learn about techniques to develop simple and efficient algorithms for fundamental mechanism design problems. Throughout the course, we will build up a toolbox of models and techniques to study the impact of strategic decision making in various settings.


Graph Polynomials and Algorithms (UvA) - 6EC


Probabilistic and Extremal Combinatorics (Mastermath and DIAMANT/STAR) - 8EC


Stochastic Gradient Techniques in Optimization and Learning (Mastermath and LNMB) - 6EC

The focus of this course is on SA algorithms and their application to stochastic simulation/data-based optimization and learning algorithms. The ODE approach to iterative learning and optimization algorithms will be introduced. In this course, we will discuss the projection method and algorithms with biased gradient estimators. Next to discussing various SA algorithms, this course provides the theoretical insights needed for carrying out a proper statistical output analysis of SA-type algorithms. Optimal computer budget allocation in SA will be taught as well (batching updates is often not advisable). Applications will stem from a wide range of stochastic models.

The course is of interest to students in the area of Simulation Analytics, Computer Science, Operations Research, and Machine Learning. It offers a self-contained course on simulation-based techniques in optimization and (machine) learning. Participating students have majors in disciplines of applied mathematics, computer science, data science, operations research, physics, electrical engineering, and economics.


Professional Skills - Science Communication (UvA) - 1.5EC

Science communication aims to bridge the gap between academia and the outside world. Science communicators want to explain, spark interest or even convince their audience of the relevance of scientific research. 

In this course, you will gain an understanding of the Science communication principles and write a popular scientific article. You will learn to write in a clear, understandable way about a research topic you are interested in. In this course we will focus on making a clear reader’s profile, how to apply writing techniques and narrative forms to make your explanations more accessible and appealing, read each other’s work and exchange feedback.


Professional Skills - Science Communication (UvA) - 1.5EC

The content of the course consists of A) going through the PCM (Process Communication Model) model which allows you to understand the depth of personality structure and to combine behavioral analysis and typology of personality, along with adaptive communication techniques. B) learning and implementing tricks for effective spoken communication;  the importance of body language combined with non-verbal communication. C) learning about confident and charismatic speakers D) learning to adapt the way you communicate in conflict situations.

2023 Spring


Algebraic Methods in Combinatorics (Mastermath and DIAMANT) - 8EC

Spectral graph theory reveals fundamental information about a graph from its spectrum, i.e., the eigenvalues of the graph's adjacency matrix (or sometimes its Laplacian). We will study three important notions in graph theory from the spectral perspective: graph expansion and the Cheeger inequality, quasirandomness, and graph partitioning. Graph expansion plays an important role in the analysis of random walks which in turn leads to fast algorithms in computer science. Quasirandomness is a measure of how random-like a graph is (a fundamental concept in extremal combinatorics which is at the heart of Szemeredi's regularity lemma for example).

The polynomial method is a relatively recent innovation in combinatorics borrowing some of the philosophy of algebraic geometry. We cover Hilbert's Nullstellensatz and Alon's combinatorial Nullstellensatz and apply these to problems in additive number theory and graph colouring. We treat some very recent applications of the polynomial method to cap set problems. We will also look at stable polynomials to give a construction of Ramanujan graphs (an important family of expander graphs).

Computational Complexity (UvA - Master of Logic) - 6EC

Writing in the Mathematical Sciences (UvA) - 3EC

While the primary focus is on writing research papers, theses, and dissertations, the general process also applies to grant proposals, expository articles, and pedagogical papers. This is a 'hands on' course, and participants are encouraged to bring any writing projects they may have, at any stage of development, to work on during the course.

Academic Year 2022/2023


Master Seminar in Discrete Mathematics and Quantum Information  (UvA) - 6EC

The students will have the opportunity to suggest topics and speakers to invite at the Master Seminar.

Second Trisemester 2022/2023 

Networks and Semidefinite Programming (LNMB PhD Course) - 4EC

2022 Fall

Continuous Optimization (Mastermath and LNMB/4TU) - 6EC

Discrete Optimization (Mastermath and LNMB/4TU) - 6EC

Stochastic Networks (UvA) - 6EC

Most of my courses are provided by Mastermath (Dutch Master’s degree programme in Mathematics), UvA (University of Amsterdam), LNMB (Dutch Network on the Mathematics of Operations Research), DIAMANT (Discrete, Interactive and Algorithmic Mathematics, Algebra and Number Theory), STAR (Stochastics - Theoretical and Applied Research) and 4TU (The four universities of technology in the Netherlands), I appreciate them so much for the high-quality courses.