Building intelligent systems at the intersection of Computer Vision, NLP & Generative AI
Faculty Valedictorian with published IEEE research, multiple national competition finalist titles, and hands-on production experience in AI-driven healthcare solutions.
I'm a Software Engineer and AI Researcher with a Faculty & Department Valedictorian distinction from Mehmet Akif Ersoy University. Currently pursuing an M.Sc. in Software Engineering (thesis track) while working as a Lecturer at Ankara University.
My research focuses on real-time object detection (YOLO architectures), NLP pipelines, and Retrieval-Augmented Generation systems. I've shipped production AI to Google Play, published at IEEE UBMK, and competed as a finalist in Turkey's most prestigious tech competitions.
As the founder of MAKUSE Research Group, I led a cross-functional team of engineering students to develop award-winning IoT and AI solutions — from smart greenhouse systems to real-time bacterial colony counters.
Real-time YOLO inference, edge deployment
LLMs, RAG pipelines, LangChain
Docker, Linux, CI/CD, cloud systems
LoRaWAN, ESP32, Raspberry Pi
AI-powered diagnostics, mobile health
Technologies and tools I use to build intelligent, production-ready systems.
Award-winning R&D projects combining AI, IoT, and full-stack engineering.
A mobile application that leverages YOLOv5 to automatically detect and count bacterial colonies on agar plates in real-time. Deployed on Google Play for use by microbiology researchers and lab technicians. The system achieves production-grade accuracy with edge-optimized inference.
A real-time drowsiness detection system built on ESP32-CAM and Raspberry Pi 4. Uses computer vision to continuously monitor the driver's eye state and triggers an audible buzzer alert when fatigue is detected — reducing accident risk through low-cost, AI-powered hardware.
An AI-driven decision support system for greenhouse climate control, leveraging LoRaWAN (LPWAN) to transmit environmental sensor data over long ranges with minimal power. The system predicts optimal temperature and humidity conditions for crops.
A pioneering IoT project using LPWAN technology for long-range, low-power greenhouse monitoring. Collects soil temperature, humidity, and climate data to forecast environmental conditions and automate actuator control via a cloud dashboard.
A university-coordinated research project (Project No. 0834-Güdümlü-22) focused on LPWAN / LoRa (Long Range) communication technologies. Conducted academic and applied research on low-power wide-area network performance and applications.
A web-based tracking platform developed in collaboration with the local Governor's Office and 112 Emergency Response Center. Enables real-time geolocation of earthquake victims and facilitates rapid communication during disaster response.
Professional roles spanning academia, government, and health-tech startups.
Ankara University
Teaching programming languages and database management courses at the university level. Covering fundamentals of software development, data structures, and relational database design with hands-on lab sessions.
Oruba Technology & Innovation (METU Technopark)
Full-time software engineer in a health-tech R&D company within METU Technopark. Building and maintaining AI-driven healthcare applications with Docker, Linux infrastructure, and modern web technologies.
Oruba Technology & Innovation (METU Technopark)
Contributed to health-sector R&D projects as a long-term engineering intern. Gained production experience in Docker-based deployments, Linux systems administration, and AI application development.
Ministry of Interior, Republic of Türkiye
Selected through the National Internship Program (Presidency HR Office). Worked within the AI Unit of the IT General Directorate, focusing on Natural Language Processing (NLP) research and development for government-scale document analysis.
Nationally recognized programs and competitive milestones.
Ministry of Industry & Technology, Republic of Türkiye
Selected among the first 1,000 participants out of thousands of applicants nationwide.
Completed foundational training and advanced to the Top 120 cohort for specialized deep-dive modules.
A prestigious government-backed AI program conducted in partnership with leading Turkish tech companies including Arçelik, Baykar, HAVELSAN, Huawei, and TÜBİTAK. Covered deep learning, computer vision, NLP, and real-world AI deployment.
Peer-reviewed research in computer vision and IoT.
UBMK 2025 — 10th International Conference on Computer Science and Engineering
Developed a complete pipeline for detecting and counting small, overlapping bacterial colonies using YOLO architectures. Benchmarked YOLOv3-Tiny, YOLOv7-Tiny, YOLOv5, and YOLOv8-Small, achieving 96.1% mAP accuracy. Deployed the optimized model on mobile devices for real-time inference in microbiology laboratories.
Data Science Journal (Veri Bilimi) · October 2022
A comprehensive survey comparing leading LPWAN technologies — LoRa, Sigfox, and NB-IoT — across critical IoT criteria including range, power consumption, device capacity, and cost-effectiveness. Provides a decision framework for selecting the optimal technology for various IoT deployment scenarios.
Read Full PaperAcademic foundation and leadership activities.
Mehmet Akif Ersoy University
Specialization in advanced research and engineering methodologies.
Mehmet Akif Ersoy University
Activities & Leadership:
I'm actively looking for opportunities in AI/ML engineering, computer vision, and full-stack development roles at innovative global companies. If you think I'd be a good fit, let's connect.
Send Me an Emailmuhsindolu06@gmail.com
