2022–Present
Senior Machine Learning Scientist · DeepHealth
Working on deep learning for lung disease detection, including model development, data selection, and reliability- and regulation-oriented workflows.
Machine Learning Scientist · Mathematician · Researcher
Marios Koulakis
Geometry For Machine Learning
ABOUT ME
I work at the intersection of machine learning, mathematics, and applied AI. My background spans deep learning, computer vision, industrial ML, and mathematical research, with a growing focus on manifold learning and geometric views of representation learning and deep networks.
I am particularly interested in research directions that connect geometry, manifold structure, data analysis, and deep learning theory. At the moment, I am especially interested in collaborations and academic opportunities related to the theory of deep learning.
Manifold Learning
Geometry For ML
Computer Vision
Dimensionality Reduction
Reliable AI
Teaching
CAREER
2021–2023
Postdoctoral Researcher · Karlsruhe Institute of Technology
Research in computer vision and machine learning, including h-NNE, supervision, and construction-related vision projects.
2019–2021
Data Scientist / Senior Data Scientist · Royal Schiphol Group
Built computer vision and activity-recognition solutions for aircraft handling processes and operational analytics.
2016–2018
Software Engineer / Data Scientist & Engineer · Nulogy
Worked on predictive modeling, optimization, data infrastructure, and AWS-based systems in manufacturing and supply-chain settings.
2015–2016
Software Engineer · ASD Personalinformationssysteme
Full-stack development for scheduling and time-tracking software in an agile environment.
2011–2015
PhD Researcher · University of Münster
Research in mathematical logic and set theory as a Marie Skłodowska-Curie fellow.
RESEARCH
Geometry, dimensionality reduction, and structure in data
Applied AI, anomaly detection, and industrial computer vision
Bildbasierte Baufortschrittsüberwachung
Book chapter 2024
Image-based construction progress monitoring in a practical engineering context.
Reliable perception and representation learning
Is My Driver Observation Model Overconfident? Input-Guided Calibration Networks for Reliable and Interpretable Confidence Estimates
IEEE T-ITS 2022
Work on calibration and confidence estimation for driver observation models under real-world deployment shifts.
PROJECTS
Manifold Microscope
A benchmarking and experimentation toolkit for studying geometric properties of synthetic and real-life-like data manifolds.
- Benchmark datasets for manifold analysis
- Geometry-oriented evaluation tools
- Public codebase for reproducing the framework and experiments
CVPR 2025 tutorial
Co-organized tutorial on dimensionality reduction, clustering, and structure in data for computer vision and machine learning.
- Conceptual overview and practical materials
- Bridges theory, visualization, and applications
- Part of a broader interest in structure-aware ML
Awesome Dimensionality reduction and Clustering
Curated reading and implementation lists for two closely related toolboxes: dimensionality reduction and clustering.
- Surveys and landmark methods with paper and code links
- Frameworks, metrics, and datasets for practical comparison
- Clustering resources also include theory and cluster-number estimation
h-NNE
A hierarchical dimensionality reduction method related in spirit to t-SNE and UMAP, but designed for speed, simplicity, and structured coarse-to-fine exploration.
- Built around hierarchical structure in the data
- Supports interpretable zoom-like exploration
- Useful for visualization and data analysis