Projects CV
AI & ML Engineering (4)
Autocomplete Engine Autocomplete Engine

An AI-assisted auto completion tool for code and text. Run locally, powered by Qwen 2.5, served via Django and FastAPI.

2026
Python Django FastAPI Qwen Hugging Face Docker Cloud Deployment
RAG Wikipedia RAG

A local RAG service that retrieves relevant Wikipedia articles to improve LLM response accuracy. Built on the Wikipedia dataset, powered by Qwen 3 and an indexed vector database.

2026
Python PyTorch Django FastAPI LanceDB Qwen Hugging Face Docker Cloud Deployment
ML Ops Pipeline MLOps Pipeline

A neural network and kernel density estimation pipeline for real-time data collection, storage, training, and inference, built with Scikit-learn and PyTorch.

2026
Python Django PyTorch Scikit-learn SQLAlchemy PostgreSQL FastAPI Plotly Docker
Deeppy Deeppy

A modular deep learning framework for rapid prototyping and experimentation, with reusable training components, automated logging and checkpointing, device management, and mixed-precision training.

2024 - Present
Python PyTorch TensorBoard
Products & System (3)
Zettelweb Zettelweb

A collaborative, encrypted, LLM-friendly web IDE that reimagines the Zettelkasten as a programmable file system

2025 - Present
Python Django JavaScript HTML CSS REST Celery Cloud Deployment
CV & Robotics CV & Robotics

A robotic arm with a soft hand and camera that combines computer vision, kinematic feedback, and learned dynamics for closed-loop exploration of its environment.

2021 - 2023
C++ OpenCV ROS
Climate Simulator Climate Simulator

A high-performance, physics-based climate simulator with infinite spatial resolution, accelerated with Numba and designed for fast simulation and reinforcement learning environments.

2025
Python NumPy Numba
AI & ML Research (4)
Project 1 Agent-Based Neural-Evolution

A proof of concept co-neuroevolution algorithm for learning continuous dynamics without gradient-based optimization or an explicit loss function.

2022 - Present
Python PyTorch CUDA Genetic Algorithms Graph Neural Networks Attention
NeRF Editing NeRF Meta Learning

A novel graph neural network based weight space learning and explanation method for diverse NeRF architectures.

2026
Python Deeppy NeRF GNN CV
Project 1 r-hash-SANE

A transformer based auto-encoder for learning latent representations of instant neural graph primitives

2025
Python Deeppy Transformer Autoencoder instant-NGP
Ear Landmark Detection Ear Landmark Detection

A DiffusionNet and graph neural network based ear landmark detection algorithm

2026
Python Deeppy DiffusionNet GNN

Berk Can Özmen

+49 xxx xxx xxxx | mail@bcozmen.com | bcozmen | bcozmen.com | bcozmen
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Summary

M.Sc. Machine Learning Researcher with a strong background in deep learning, representation learning, reinforcement learning, and computer vision. Experienced in developing and evaluating learning algorithms, designing experimental frameworks, and integrating visual perception & decision-making into autonomous systems.

Skills

Programming: Python, C++
ML Paradigms: Deep Learning, Reinforcement Learning, Representation Learning, Meta-Learning
ML Architectures: Transformers, CNNs, Graph Neural Networks, Diffusion Models
Computer Vision: Geometric Deep Learning, Neural Radiance Fields (NeRF), 3D Reconstruction, OpenCV
ML Frameworks: PyTorch, Scikit-learn, TensorBoard, HuggingFace, Gymnasium
Infrastructure: Linux, Git, Docker, Kubernetes, AWS
Sci. Computing: NumPy, SciPy, Pandas, Numba, CUDA
Languages: Turkish (Native), English (C2), German (C1), French (A2)

Experience

06/2025 - Present
Berlin
Independent ML Engineer bcozmen.com
Self-Employed
  • Architected and built an end-to-end AI-powered knowledge management system, including semantic search over 100M+ records, RAG pipeline, real-time collaboration, and an integrated AI assistant.
  • Built end-to-end ML applications and numerical simulation pipelines, covering model implementation, data processing, API development, and cloud deployment.
09/2023 - 09/2024
Berlin
Machine Learning Engineer
Fraunhofer SIRIOS
  • Developed a reinforcement learning-based method (SAC & DQN) for ambulance and fire truck allocation, to optimize for mortality risk, response-time weighted for urgency, and operational costs.
  • Implemented the learning algorithm and reward function, validating learned policies against empirical probability distributions in tractable scenarios and comparing their performance with greedy allocation baselines.
  • Evaluated policies across hand-crafted scenarios for complex cases, assessing compliance with operational regulations and performance on task-specific metrics.
09/2021 - 04/2023
Berlin
Computer Vision & Robotics bcozmen.com/scioi
Science of Intelligence
  • Developed a computer vision pipeline to reconstruct 3D geometry of a lockbox puzzle from camera observations mounted on a robotic arm.
  • Integrated the reconstructed geometry into an internal world model, which guided the robotic arm to collect additional viewpoints and iteratively update the model.
  • Reconstructed the lockbox geometry and determined a feasible robot approach configuration for subsequent kinematic exploration.
09/2019 - 06/2020
Berlin
Internship at Neural Information Processing Group
Technische Universität Berlin
  • Implemented a simulation environment for FitzHugh-Nagumo based resting-state brain network models, using empirical structural connectivity and functional connectivity data.
  • Explored the parameter space of the FitzHugh-Nagumo model using genetic algorithms and Bayesian optimization with Gaussian processes on a distributed computing setup (HPC).
09/2017 - 04/2019
Berlin
Software Developer
Ten8
  • Developed and maintained the company's website and internal application for a Berlin-based startup.
  • Developed a web scraping tool to collect and process initial coin offering (ICO) data, automatically alerting the team to new and potentially interesting opportunities.
2013 - 2016
Istanbul
FRC Team Member & Leader
Inanc Mechatronics Club
  • Designed and built competition robots for the FIRST Robotics Competition (FRC), leading a 10-member technical team in 2015.
  • Built robots end-to-end, spanning 3D CAD, fabrication, machining, mechanical assembly, and programming for autonomous and teleoperated operation.

Projects

2024 - Present
Deeppy bcozmen/deeppy
  • Created a modular PyTorch training framework for rapid prototyping and experimentation in deep learning research.
  • Decoupled model architectures, data pipelines, optimizers, schedulers, and training logic, with integrated logging, visualization, checkpointing, and automatic mixed-precision training.
  • Automated device placement and tensor management across GPU/CPU memory spaces, with data buffering to support large datasets.
  • Continuously improved through practical use across 5+ projects and my master's thesis.
  • Includes DQN, SAC, Beta-VAE and transformer-based architectures as tutorials.
2022 - Present
Agent-Based Neuro-Evolution bcozmen/abne
  • Developing a physics-based multi-agent environment for studying neuroevolution, where agents evolve neural network controllers through selection rather than an explicitly defined loss function.
  • Investigating compact representations of agent behavior using message-passing graph neural networks, alongside non-destructive crossover and mutation operators for evolving neural controllers.
  • Exploring how evolutionary selection pressures drive the emergence of complex, adaptive behaviors in multi-agent systems without explicitly designing the behaviors to be learned.
2025
r-hash-SANE bcozmen/r-hash-SANE
  • Extended the SANE architecture to hash-grid NeRFs, developing a transformer-based autoencoder for learning latent representations of NeRF parameters.
  • Implemented and evaluated reconstruction and contrastive learning objectives, successfully learning latent representations; explored rotation-related learning objectives, limited by the available training data.

Education

2026
Berlin
M.Sc. Computer Science, est. 1.3 (3.8/4.0) bcozmen/master_thesis
Technische Universität Berlin
  • Thesis: Interpreting NeRF Weights Across Architectures with Graph Meta-Networks for Geometry and Appearance Attribution
  • Supervisor: Prof. Dr. Olaf Hellwich
  • Proposed a self-interpretable Graph Meta Network with learned gating and attention-based pooling to identify task-relevant substructures in NeRF weight spaces.
  • Evaluated the approach on geometry and appearance prediction using NeRF representations paired with corresponding RGB and geometry histograms, demonstrating that the model identifies relevant computational subgraphs.
2021
Berlin
B.Sc. Computer Science, 1.7 (3.5/4.0) bcozmen/bsc_thesis
Technische Universität Berlin
  • Thesis: Modeling Functional Connectivity in Health and Psychiatric Disorders | Thesis Grade: 1.3
  • Supervisor: Prof. Dr. Klaus Obermayer