01 / ABOUT
ABOUT
SYSTEM PROFILE
- 2027
- EXPECTED GRAD
- 07+
- ROLES & RESEARCH PROJECTS
- ML
- FOR HEALTHCARE
PROFILE_NARRATIVE.txt
I'm a Computer Science student at Sabancı University, class of 2027, and I build machine learning models for medicine.
Most of my work so far is clinical risk prediction: take a large electronic health record dataset, engineer features a clinician would recognise, train a calibrated gradient-boosted model, and prove it beats the scores hospitals already use. I care about calibration and explainability as much as AUROC, because a model nobody trusts doesn't get used.
Around that: undergraduate research on explainable deep learning for healthcare and on machine learning for biomedical alloys, a teaching assistantship in data science, and a couple of full-stack products built in teams. Right now I'm moving toward LLM and RAG systems, and building one to learn it properly.
I also paint. It isn't a metaphor for anything. I just like it.
02 /
WORK
/07WHAT I ACTUALLY DO
Machine learning for healthcare: calibrated risk models on real clinical data, explainability that a physician can read, and the data engineering underneath.
Concretely: I turn raw hospital and genomic data into features, train and validate models with leakage-safe cross-validation, calibrate them, explain them with SHAP, and report them to clinical standards. I've also shipped full-stack products with a team, and I'm now building retrieval-augmented LLM systems.
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Clinical risk model
0.898AUROC
Calibrated 30-day post-discharge mortality model for patients over 70, trained on 172k hospital admissions. Beat the standard clinical scores by +0.15 and +0.07 AUROC under grouped cross-validation.
XGBoost · GroupKFold · SHAP · TRIPOD+AI
-
EHR feature pipeline
77clinical features
Engineered from a 28 GB raw electronic-health-record release. Every feature has a clinical justification, and the whole pipeline runs on a laptop.
DuckDB · polars · pandas
-
Genetic variant classifier
98.8% peak accuracy
Diagnosis support for a rare inherited bone disorder from 3,000+ sequences. Four algorithms compared; a novel feature representation based on collagen amino-acid structure did the heavy lifting.
scikit-learn · Biopython · cross-validation
-
Legal-tech hackathon
2ndplace · ₺20k seed
B2B product that scores litigation outcomes and cites relevant precedent for uploaded case documents. Built in a weekend with a team of five.
Python · NLP · product pitch
FULL HISTORY ON REQUEST
I keep the detailed list of roles, projects and metrics off the public site on purpose. If you're hiring or supervising, ask and I'll send the full page plus a CV.
03 /
SKILLS
/07ML & data
- XGBoost
- scikit-learn
- SHAP
- pandas
- polars
- NumPy
- DuckDB
- SQL
- Matplotlib
- Biopython
- Jupyter
Languages
- Python
- C++
- JavaScript
- SQL
Web & infra
- React
- Vite
- Django REST Framework
- PostgreSQL
- Docker
- Firebase / Supabase
- Astro
- Git / GitHub
- Jira
- Ubuntu / Linux
Concepts
- Machine learning
- Deep learning
- Explainable AI
- Model calibration
- Full-stack web
- REST APIs
- Statistics
- Data visualisation
Learning now
Deliberately moving from classical ML into LLM engineering. Building a RAG project to prove it.
- LLM systems
- Retrieval-augmented generation
- Agents
ALSO
Creative
- Oil painting
- Watercolour
- Drawing
- Digital illustration
- Photoshop
- Premiere Pro
Spoken
- English (C1, fluent)
- Turkish (native)