Titus Theodorus Robroek

Titus Theodorus Robroek is a Postdoctoral Researcher at IT University of Copenhagen, working within the Data, Systems, and Robotics section. His research focuses on resource-aware data systems and data-intensive systems and applications, with affiliations to both the Resource-Aware Data Systems group and the Center for Climate IT.

Dr. Robroek's research interests span multiple areas in machine learning systems, including:

• Resource-aware Machine Learning
• Deep Learning Training Optimization
• GPU Computing and Collocation
• ML Benchmarking Frameworks
• Data Management for Machine Learning
• Scientific Visualization for Training Systems

His recent publications demonstrate a strong focus on optimizing machine learning workflows, particularly in resource-constrained environments. His work addresses critical challenges in data selection, pipeline orchestration, and efficient utilization of hardware resources for deep learning training. The research shows a progression from foundational work on data management to more comprehensive frameworks for resource-aware machine learning.

Dr. Robroek has received research funding through two major projects:

• MOTH: Machine Learning on Tiny Hardware (Novo Nordisk Foundation, 2023-2026)
• RAD: Extremely Parallel and Incredibly Diverse Data Processing on Many Heterogeneous Cores (Independent Research Foundation of Denmark, 2021-2025)

His PhD thesis "Resourceful Learning: Training More Models with Fewer Resources" (2024) represents a significant contribution to the field of efficient machine learning systems.