Aleyna Nil
Uzunoğlu
Satellite Software Design Engineer · Turkish Aerospace
Ankara, Türkiye
Building for
the final frontier.
I'm a Software Design Engineer with 4.5+ years of experience building mission-critical systems. My work sits at the intersection of aerospace software, artificial intelligence, and reliable backend systems.
At Turkish Aerospace, I architect software for satellite ground segments — telemetry processing, spacecraft command systems — all built under ECSS standards and CCSDS protocols, contributing to multiple satellite programs including microsatellite initiatives.
Currently pursuing an M.Sc. in Computer Engineering at Yıldız Technical University, my thesis focuses on deep learning for exoplanet detection using Kepler/TESS mission data. My research combines signal processing, neural networks, and scientific computing.
Beyond code, I'm fascinated by the cosmos — from analyzing light curves of distant stars to tracking F-16s at airshows. This curiosity drives everything I build.
CEERS Deep Field — JWST | webbtelescope.org
Technical
Expertise
Space Systems
ECSS-compliant development, CCSDS protocols, Telemetry & Telecommand systems, Ground segment automation, Mission operations software
Backend Dev
Java 21, Python, Spring Boot, FastAPI, REST API design, Microservices, PostgreSQL, MongoDB
AI & ML
Deep Learning (CNNs, Vision Transformers), PyTorch, TensorFlow, Time-series analysis, Signal processing, Feature engineering
Tools & Practices
Git, CI/CD, Docker, Linux, OOP, Design patterns, ECSS technical documentation
Core Technologies
Featured
Work
Research and engineering spanning space science, AI, and healthcare.
Deep learning pipeline to detect planetary transits in Kepler/TESS light curves. Combines automated preprocessing, feature extraction (PCA, LDA, SHAP), and CNN-based classification to identify exoplanet candidates from stellar brightness data.
ML models to distinguish technological (artificial) signals from natural astrophysical sources. Exploring advanced signal processing and anomaly detection in the context of Search for Extraterrestrial Intelligence (SETI).
Benchmarked state-of-the-art segmentation architectures (U-Net, DeepLabV3+, TransUNet, Swin-UNet, SAM) for automatic glacier front detection from Sentinel-1 radar imagery. Achieved significant improvements in IoU and F1-score.
Contributed to structures, analysis, and avionics workstreams for a CubeSat-class spacecraft. Supported subsystem integration across mechanical, electronics, and software domains throughout the full development lifecycle.
ML-based decision support system for early sepsis detection, processing 46,520 patient records from the MIMIC dataset. Achieved 86.7% accuracy across 780+ conditions. Awarded 2nd place at "Genç Beyinler Yeni Fikirler" and supported by TÜBİTAK BİDEB 2209-A grant.
Career &
Education
Work
Architecting mission-critical software for satellite ground segments. Developing CCSDS-compliant telemetry/telecommand modules, designing spacecraft-to-ground communication systems, driving end-to-end development under ECSS standards.
Built REST API integrations and backend data processing services for customer feedback platforms using Python and modern web frameworks.
Developed web applications with a security-first approach. Built 3 automation tools for security reporting. Gained hands-on experience in information security practices.
Education
Thesis: Deep learning for exoplanet detection from Kepler/TESS light curves. Focus: machine learning, deep learning, NLP, signal processing.
Graduated with honors. Senior project on sepsis detection awarded 2nd place nationally. TÜBİTAK BİDEB 2209-A grant recipient.
Let's Connect
Interested in collaborating on space systems, AI research, or challenging engineering problems? Always open to meaningful conversations and new opportunities.
aleynaniluzunoglu@gmail.com linkedin.com/in/niluzunoglu github.com/niluzunoglu medium.com/@aleynaniluzunoglu