Internships and research positions

Student Research Assistant
University of Amsterdam (AIRLab), Ahold Delhaize
Jul 2019 – Dec 2019 Amsterdam, Zaandam (The Netherlands)
Research Assistant in the AIRLab (ILPS) at the University of Amsterdam in cooperation with Ahold Delhaize (full-time till August, part-time till December). My research, supervised by Pengjie Ren, focused on advancing task-oriented dialogue systems to conduct human-like conversations with diverse responses. The results are summarized in our paper Diversifying Dialogue Response Generation with Prototype Guided Paraphrasing.
Student Research Assistant
Vrije Universiteit Amsterdam
Nov 2018 – Jun 2019 Amsterdam (The Netherlands)
Research Assistant (part-time) in the field of high-order automated theorem proving developing a hammer for Lean. A hammer reduces high-order formulas into first-order clauses to efficiently check for possible proofs and makes manual formalizations much easier. The research is part of the Lean Forward project supervised by Jasmin Christian Blanchette and Robert Y. Lewis.
Student Intern
May 2017 – Aug 2017 Sunnyvale (USA, California)
Student intern in the team Trajectory Planning (Research and Development - Autonomous Driving) working on predicting agent intentions and future traffic scenarios. The research focused on using Generative Adversarial Networks (GANs) in the context of video generation. I have been supervised by Yan Meng.
Cooperative Student / Student Intern
Daimler AG, Mercedes-Benz
Dec 2016 – Sep 2018 Stuttgart (Germany)
Student intern in the team Image Understanding (Research and Development - Autonomous Driving) working on hierarchical multi-label classification with a special focus on rare classes. The research was conducted both on bounding-box detection and semantic segmentation for complex, urban traffic scences. I have been supervised by Björn Fröhlich (Dec 2016 - Mar 2017) and Jonas Uhrig (Apr 2018 - Sep 2018). During the last internship period, I wrote my bachelor thesis on Hierarchical multi-label object detection of rare classes for autonomous driving (PDF).


Courses and student supervision


I have been a teaching assistant for a couple of courses of the Master program “Artificial Intelligence” at the University of Amsterdam. A short description of each can be found below.

Deep Learning (19/20, 20/21)

This course teaches the fundamentals of deep learning including backpropagation, initialization and regularization techniques, and optimizing deep neural networks. Furthermore, we discuss common neural network architectures (CNNs, RNNs, GNNs), and generative models (Energy-based, VAE, GANs, Normalizing Flows). I have been responsible for creating and teaching a series of Jupyter notebooks to more than 150 students showing the implementation of the most important models, including their benefits and drawbacks. The notebooks are uploaded on this website, and more details on the course can be found here.

Advanced Topics in Computational Semantics (19/20, 20/21)

In this research-focused course, we discuss recent advances in the field of Natural Language Processing and Computational Semantics. The topics include multi-task learning and meta-learning, as well as transfer learning from large language models (BERT-family) and multi-lingual tasks. The students work on research projects in groups, of which some can become published as conference papers.

Natural Language Processing 1 (19/20)

This course teaches the fundamentals of Natural Language Processing, with the second half of the course focusing on RNNs, Attention and applications including machine translation and summarization.

Fairness, Accountability, Confidentiality and Transparency in AI (19/20)

This course focuses on four, often underrated topics in Artificial Intelligence: Fairness, Accountability, Confidentiality and Transparency. The goal of the course is to gain a general understanding of those four topics, and reproduce a published paper/method in one of those four fields.

Information Retrieval 1 (19/20)

The course discusses state-of-the-art techniques that constitute the core of information retrieval systems, such as search engines, recommender systems, and conversational agents. It focuses on evaluation, document representation and matching, learning to rank, and user interaction.

Student supervision

  • Kaleigh Douglas (MSc AI thesis, Jan 2021 - present)
  • Nadja Rutsch (MSc QUVA Intern, Jun 2021 - present)

If you are a student looking for a thesis supervisor and working on a similar topic to my research interests, feel free to send me a mail.


Reviewing and presentation

I have been involved in the following conferences:

    - Reviewer for: ECCV-2020, ICCV-2021, CausalUAI-2021 (workshop)

    - Volunteer for: ICLR-2020, ICML-2020

    - Presented work at: NeurIPS-2020, ICLR-2021