About Me

I see myself as a bioinformatics researcher passionate about building scalable, interpretable, and ethically aligned machine learning systems that bridge health, computation, and social impact.

I’m currently pursuing an M.Sc. in Bioinformatics at Saarland University,
where I combine machine learning, data engineering, and computational biology to explore how intelligent systems can advance both research and society.

Research & Engineering

At the Interdisciplinary Institute for Societal Computing (I2SC),
I work as a Research Assistant, contributing to the development of a scalable geo-intelligence platform that integrates Google Maps data, spatial analytics, and distributed pipelines.

The system processes shapefiles, traffic data, user reviews, and live busyness trends to generate dynamic urban insights for crisis monitoring and regional planning.

Previous Experience

I’ve contributed to interdisciplinary research and product development across institutions such as ETH Zürich, Harvard Medical School, USC, Brown, and Emory University.

• Emory University – built an interactive global map of medical AI ethics guidelines using React and Python
• Harvard Medical School – designed CNN models for parallel medical image reconstruction
• ETH Zürich – developed reproducible Snakemake genomic workflows for DNA alignment benchmarking
• USC – applied ensemble ML models for sepsis mortality prediction
• Thread In Motion – integrated analytics pipelines with Hotjar, HubSpot, and GA to optimize marketing
• Samsung Innovation Campus – top 2.3% nationwide with an AI tool for crop yield optimization
• Sabancı University – identified brain-specific endothelial proteins via transcriptomic analysis

Leadership & Outreach

I lead Marketing and Outreach at AI Safety Saarland (AISS) —
a student-driven initiative promoting responsible AI development through fellowships, incubators, and interdisciplinary events.

My role combines branding, strategy, and university collaboration to grow one of Germany’s most active AI-safety communities.

Technical Focus

• Languages: Python · R · C++ · SQL · JavaScript
• Frameworks: PyTorch · TensorFlow · FastAPI · Celery · Kafka · PostGIS · TimescaleDB · React
• Core Areas: Deep Learning · Computational Genomics · Geospatial Analytics · HPC Workflows · System Design
• Philosophy: Engineering for interpretability, reproducibility, and social responsibility

Vision & Aspirations

My long-term goal is to contribute to frontier research environments and advance AI for Science through generalizable, safe, and transparent models.

I’m particularly interested in:

• AI for Science & Health — learning representations that reveal biological structure
• Multimodal Learning & Bias Mitigation — ensuring models reason equitably across data types
• Safe & Transparent AI Systems — building architectures aligned with human values

Beyond Research

Outside academia, I explore creative technology, storytelling, and photography.
I believe the next generation of AI leaders must think like scientists, design like engineers, and communicate like artists.