Arda Duman
Software Engineer • M.Sc. Informatics (TUM)I hold an M.Sc. in Informatics from Technical University of Munich, where I specialized in Engineering Software-Intensive Systems. My academic foundation was established at Koc University, focusing on computer systems, networks, and data analytics.
With professional experience at companies like RideBee and Enmacc, I specialize in Mobile Development (Kotlin/Jetpack Compose) and Frontend Engineering. I am dedicated to architecting scalable software solutions and bridging complex technical requirements with high-quality user experiences.
News
Education
Engineering Software-intensive Systems Major. Focused on Computer Architecture, Distributed Systems, and Algorithms.
Graduated with a GPA of 3.03/4.00, earning a place on the High Honor List.
Track Programs:- Computer Systems, Networks and Software
- Cryptography, Security, and Theory
- Data Analytics
Experience
- Optimizing a B2B car-sharing platform to enhance commute efficiency and user engagement.
- Developing robust mobile features using Kotlin and Java.
- Modernizing application architecture by migrating legacy XML layouts to Jetpack Compose.
- Developed high-performance internal web applications using Angular.
- Built scalable frontend components with a focus on modular design.
- Developing the user interface of Setur mobile application using React Native and TypeScript.
- Involved in project language migration from JavaScript to TypeScript and Scrum methodology.
- Worked on a high-impact project utilizing Machine Learning models to process and read Identification (ID) cards automatically.
Selected Projects
A comprehensive SaaS solution for intercity bus companies in Turkey, successfully accelerated by Kworks. RotaGo provides real-time competitor price tracking, enabling firms to dynamically adjust their pricing strategies. The platform also features route and tour optimization, allowing companies to execute more trips with fewer vehicles. Additionally, our AI engine, Opti, generates strategic sector analysis reports to guide business decisions.
Visit RotaGo →A senior capstone project developed on Azure using Python (backend) and Dart/Flutter (frontend). The system utilizes Machine Learning models to analyze real-time market data to detect and notify users of cryptocurrency anomalies, bridging cloud computing with mobile real-time alerting.
Research
This research proposes an end-to-end framework that integrates satellite-based damage detection, GIS road network analysis, and optimization modeling to support post-disaster aid distribution planning. The system translates detected building damage into risk-aware road network costs, enabling accessibility-aware routing and improved planning of aid distribution points (PODs).
- Developed a ResNet-based model for binary damage detection using satellite imagery.
- Converted building damage into road exposure penalties through GIS-based impact zones.
- Computed accessibility using least-cost paths on the weighted road network.
- Formulated a mixed-integer location–allocation model to optimize POD placement and demand assignments.