M.Ozan Unal

About

Hello Everyone!

I am M.Ozan. I am a data Engineer with 6+ more years of experience in transforming business requirements into analytical models, designing algorithms, building models, developing data mining and reporting solutions that scale across a massive volume of structured and unstructured data, and expertise working in various industries.

Skills

Data Engineering: Apache Spark, Spark Streaming, real-time analytics, SQL, ORM, Python (Numpy, Pandas, Flask), data modeling, distributed computing, Kafka, RabbitMQ (with Celery library), MQTT and IoT systems, advanced Linux command line knowledge, Scala, functional programming, Golang, concurrent programming.

Machine Learning: Theoretical understanding of ML Algorithms , Ability to implement new algorithms from scientific documents, PyTorch, Scikit-Learn, Experience in Computer Vision (OpenCV).

Software Development: Git and version control, TDD and software testing, Docker, Docker-Compose, Kubernetes, CI, CD pipelines, Jenkins, AWS, Google Cloud Platform.

Experience

Electrolux (Stockholm, SWE): 2021–2022, Senior Data Engineer

Developing ETL framework and pipelines for connected appliances using technologies Apache Spark, Airflow, Databricks, Azure Cloud. Discuss and comprehend the business requirements for data consumption. Design, develop and optimize data pipelines Implement real-time streaming data processing. Support in the implementation of Machine Learning capabilities. Analyzed data quality, and propose and implement improvements. Architecture design. Development of data-related instruments/instances. Data pipeline maintenance/testing. Manage data and meta-data. Provide data-access tools. Track pipeline stability.

muTech (Hannover DE): 2018–2021, Data/AI Team Leader

Setting up big data and stream processing pipelines. Development of deep learning-based alarm systems. Setting up Kubernetes-based deployment. Setting up CI/CD systems for the company.

BasesTech (Istanbul TR): 2016–2018, Software Engineer

part-time(1 year), full-time(2 years), development of the backend of IoT product, setting up version management systems for the company wiCow is an end-to-end system that is developed to monitor the health conditions of cows in dairy farms. From the beginning of the project, one of the big parts of the backend system is developed from scratch which consists, of management of IoT devices, database architecture design, real-time data ingestion, and processing pipeline design, deep learning supported autonomous alarm services.

Achievements and Side Projects

SparseCT 2021, Github Link This repo is a tool to develop sparse view CT reconstruction projects and compare different methods easily.

Img2sh 2019, Github Link. Img2sh is a tool to show images directly on the terminal. For color images, 256 xterm color support is required. This script basically resize the image with antialiasing and quantized its colors to xterm color pallette.

SimpleDSP 2018, Github Link. SimpleDSP is a basic DSP library which is for Arduino and most of the microcontrollers which can be programmed in C/C++.

Digilent Design Contest 2017, Digilent Cluj, Romania. It was a competition about Xilinx FPGA based systems. The project for this competition was the detection of sound direction using the microphone arrays.

NASA Space Apps Challenge 2017, Istanbul, Turkey, 1st Place. It was an international hackathon organized by NASA. 1st place was taken by a project to assist people with their own UV exposure using Internet of Things systems.

ITUNOM 2014 - 2016, Maryland, USA. Unmanned Air Vehicle Team. It was a team about unmanned air vehicles at Istanbul Technical University. This team participated in AUVSI Student Unmanned Air Systems Competition 2015 and 2016. In 2016, ITUNOM was 13th in total and 1st among multi-copter participants. My task in this project was the software team leader. I worked on image processing OpenCV, networking, and flight control systems.

Siemens Hackathon Industry 4.0 2016, 3rd place.

Nasa Space Apps Challenge 2015, People’s Choice Award.

Intercollegiate Rocket Engineering Competition 2013, Utah, USA. ITU Iskandil Team, is a team that is involved in rocket-engineering. In June 2013, this competition was attended. I was on the avionics team of this group. Arduino and PIC microprocessors are to be programmed in accordance with their competition duties. Providing communication with the ground station tracking interface and long-distance radio modules required for real-time tracking of the rocket.

My Blog 2013 - present, mozanunal.com. I decided to open a blog (2013) so that I could archive my projects and make them available to everyone. In my blog, you can reach various kinds of content which are usually about Programming, Data Science, Drones, Electronics. Since 2013, I have been writing around 100 blog posts and projects and they have been visited by more than 800.000 people.

Education

  • 2018 - 2021 M.Sc. Istanbul Technical University. Electronics and Communication Engineering
  • 2012 - 2017 B.Sc. Istanbul Technical University. Electronics and Communication Engineering
  • 2008 - 2012 High School. Bornova Anatolian High School

Publications

  • M.O. Unal, M. Ertas, I. Yildirim. Proj2Proj: self-supervised low-dose CT reconstruction, PeerJ Computer Science 10, e1849, 2024.
  • M.O. Unal, M. Ertas, I. Yildirim. An Unsupervised Reconstruction Method For Low-Dose CT Using Deep Generative Regularization Prior, Biomedical Signal Processing and Control 75 (1746-8094), 2022.
  • M.O. Unal, M. Ertas, I. Yildirim. Self-Supervised Training For Low Dose CT Reconstruction, IEEE International Symposium on Biomedical Imaging, 2021.
  • D. Gunduzalp, B. Cengiz, M.O. Unal, I. Yildirim. 3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3D Convolutions, arxiv.org, 2021.
  • O. Calisici, S. Salar, M. Erturk, H. Kircioglu, A. Bajcsy, M. O. Unal, T. Cirak, Determination of the time of calving in Holstein-Friesian and Simmental cows by using a novel intravaginal temperature recording smart device, Reproduction In Domestic Animals, 2019.

Certificates

  • 2020, Practical Deep Learning with PyTorch, Udemy
  • 2020, Deep Learning: GANs and Variational Autoencoders, Udemy
  • 2020, Machine Learning, Data Science and Deep Learning with Python, Udemy
  • 2020, Docker and Kubernetes: The Complete Guide, Udemy
  • 2019, Spark and Python for Big Data with Pyspark, Udemy
  • 2019, Taming Big Data with Apache Spark and Python, Udemy