Introduction

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Hello, my name is Daan Rutten. I am currently a Senior Applied Scientist working in inventory placement at Amazon. My Ph.D. research focused on the performance of large-scale systems and the optimization thereof by incorporating machine learning algorithms and making smart design decisions. My previous work has studied how to structure cloud networks in the presence of task-server constraints, how to implement machine learning predictions while maintaining robustness and how to learn optimal decision policies in dynamic environments.

Research areas

Large-scale online optimization and reinforcement learning Distributed and stochastic algorithms with applications to supply chains Probabilistic modeling and algorithmic analysis on complex networks

Programming languages

Java Python SQL C++ MATLAB R

Programming tools

AWS (EC2, S3, Redshift) PyTorch, NumPy, SciPy FICO Xpress Git

Education

Ph.D. in Operations Research

Georgia Institute of Technology | 2020 - 2024

M.Sc. in Computer Science and Applied Mathematics Summa Cum Laude

Eindhoven University of Technology | 2018 - 2020

B.Sc. in Applied Physics and Applied Mathematics Summa Cum Laude

Eindhoven University of Technology | 2016 - 2018

Employment

Senior Applied Scientist

Amazon | Oct. 2025 - present
  • Lead scientist for product placement system processing over 10B units annually.
  • Built simulation environments and reinforcement learning-based optimization for sortation in crossdocks.

Applied Scientist

Amazon | Feb. 2024 - Sept. 2025
  • Developed and deployed two new online learning algorithms world-wide.
  • Improved legacy placement models, achieving a 1,000× speedup in solve time.

Visiting Scholar

Harvard University | Aug. 2023 - Dec. 2024
  • Invited by Prof. Michael Mitzenmacher to collaborate on stochastic optimization research.

Applied Scientist Intern

Amazon | May. 2022/2023 - Aug. 2022/2023
  • Created unsupervised clustering algorithms for delivery to rural destinations.
  • Developed robust optimization models resilient to high forecast uncertainty.

Machine Learning Intern

Applied Materials | May. 2021 - Aug. 2021
  • Implemented a complex, numerical simulation to optimize an AR device.
  • Applied advanced techniques to optimize the manufacturing workflow of AR devices.

Full Stack Engineer

Games for Health Europe | Mar. 2016 - July 2022
  • Creator and lead software engineer for Whappbot, a web-based chatbot.

Regional Department Lead

TopTutors | Aug. 2016 - May.2019
  • Responsible for managing the tutoring of more than 200 high-school students by over 40 tutors.

Manuscripts

Distributed Rate Scaling in Large-Scale Service Systems

Rutten, D., Zubeldia, M., & Mukherjee D. Operations Research | 2025

Mean-field Analysis for Load Balancing on Spatial Graphs

Rutten, D., & Mukherjee D. The Annals of Applied Probability 34.6 | 2024

Distributed Rate Scaling in Large-Scale Service Systems

Rutten, D., Zubeldia, M., & Mukherjee D. ACM SIGMETRICS Performance Evaluation Review | 2023

Mean-field Analysis for Load Balancing on Spatial Graphs

Rutten, D., & Mukherjee D. In Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (pp. 27-28) | 2023

Smoothed Online Optimization with Untrusted Predictions

Rutten, D., Christianson N., Mukherjee D., & Wierman A. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 7(1), 1-36 | 2023

A New Approach to Capacity Scaling Augmented With Unreliable Machine Learning Predictions

Rutten, D., & Mukherjee, D. Mathematics of Operations Research | 2023

Load Balancing Under Strict Compatibility Constraints

Rutten, D., & Mukherjee, D. Mathematics of Operations Research | 2022

Capacity Scaling Augmented With Unreliable Machine Learning Predictions

Rutten, D., & Mukherjee, D. ACM SIGMETRICS Performance Evaluation Review, 49(2), 24-26 | 2022

Load Balancing Under Strict Compatibility Constraints

Rutten, D., & Mukherjee, D. In Abstract Proceedings of the 2021 ACM SIGMETRICS/International Conference on Measurement and Modeling of Computer Systems (pp. 51-52) | 2021

Modeling Rydberg Gases Using Random Sequential Adsorption on Random Graphs

Rutten, D., & Sanders, J. Physical Review A, 103(3), 033302 | 2021

Awards

Ed Iacobucci Research Fellowship in Applied Probability and Simulation

Georgia Institute of Technology | Spring 2024

George Nicholson Student Paper Competition Finalist

INFORMS conference | Fall 2023

ACM SIGMETRICS Best Paper Award

ACM SIGMETRICS conference | Summer 2023

ACM SIGMETRICS Student Grant $1000

ACM SIGMETRICS conference | Summer 2023

INFORMS Junior Faculty Paper Award Finalist

INFORMS conference | Fall 2022

Stochastic Networks Student Grant

Stochastic Networks conference | Summer 2022

Alice and John Jarvis Ph.D. Student Research Award $500

Georgia Institute of Technology | Fall 2021

ACM SIGMETRICS Student Grant

ACM SIGMETRICS conference | Summer 2021

ARC-TRIAD Fellowship $5,000

Georgia Institute of Technology | Spring 2021

ASML Young Talent Award €5,000

Royal Dutch Society of Sciences (KHWM) | Fall 2020

Stewart Fellowship $10,000

Georgia Institute of Technology | Fall 2020

Holland Scholarship €1,250

Dutch Ministry of Education, Culture and Science | Fall 2019

Young Talent Incentive Prize €500

Royal Dutch Society of Sciences (KHWM) | Fall 2016

Teaching

Graduate Teaching Assistant for Stochastic Processes II

Georgia Institute of Technology | Spring 2022

Graduate Teaching Assistant for Stochastic Processes 2

Eindhoven University of Technology | Spring 2020

Mentorship

Improving Multi-armed Bandits with Confidence Estimates

Xie, Y., & Rutten, D. Georgia Institute of Technology | 2022

Online Knapsack Problem

KandageDon, U., & Rutten D. SURE REU | 2021

Get in touch

Please feel free to reach out if you have any questions. I am always happy to know who is reading my website.