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Degaga Wolde
Addis Ababa, Ethiopia
Addis Ababa Science and Technology University(2019-2022)
MSc. (Computer Engineering)
MSc. (Computer Engineering)
Email: degagawolde@gmail.com
Programming Languages
Programming Languages
Python
JavaScript
C++
Java
ML Tools
ML Tools
PyTorch, TensorFlow
Scikit-Learn, Pandas
Kafka, Airflow
SQL, MongoDB
Automation Tools
Automation Tools
Docker
GitHub Actions
MLFlow, DVC, CML
Unit-Testing
General Skill-Sets
General Skill-Sets
ETL Pipeline Building
Data Visualization
ML Model Life Cycle Management
About me
A machine learning engineer with two years' worth of relevant work experience developing and using multiple ML models for the healthcare industry. Working knowledge in end-to-end computer vision, natural language processing, and explainable AI projects using technologies like Kafka, TensorFlow, Scikit-Learn, Flask, Docker, and CI/CD tools. Proficient in data preparation, modeling, visual analytics, scalable deployment, and maintenance.
Education
(Machine Learning Engineering/Data Engineering/WEB 3 Engineering)
(Machine Learning Engineering/Data Engineering/WEB 3 Engineering)
A Project-based Intensive training where I got to work on ten projects that helped me develop the following skill sets:
Machine Learning Pipeline Development
Data Engineering Principles and large Scale Implementations
WEB 3 dAPP Development
Statistical Reasoning
Addis Ababa Science and Technology University (2019-2022)
MSc. (Computer Engineering)
MSc. (Computer Engineering)
Relevant Courses
Relevant Courses
Machine Learning
Computer Vision
Computational and Applied Mathematics
Random and Stochastic Processes
Advanced Distributed System
High Performance Computing
Addis Ababa Science and Technology University (2015-2021)
BSc. (Computer Engineering)
BSc. (Computer Engineering)
Relevant Courses
Relevant Courses
Introduction to AI
Embedded Systems
Computer Architecture and Organization
Relational Database Management
Digital Signal Processing
Probability and Statistics
Data Structures & Algorithm Design
Image Processing & Pattern Recognition
Work Experience
AI Research Engineer
Ethiopian Artificial intelligence Institute (February 2021- To date)
Ethiopian Artificial intelligence Institute (February 2021- To date)
Published an article titled "Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification" with a 12% increase in the f1 score.
Developed a deep learning model with 98% accuracy for the lung disease detection system from a chest x-ray.
Developed an ECG Annotation desktop app that assisted in automating the data annotation process for a project involving diagnosing heart disease using explainable deep learning from ECG signals.
Embedded System Developer Intern
blueMoon Ethiopia (June 2019- September 2019)
blueMoon Ethiopia (June 2019- September 2019)
Developing embedded systems using microcontrollers, sensors, and communication devices
Writing firmware for the microcontrollers using the C programming language
Volunteer Experience
Core Team Member
Google Developer Students Club AASTU (September 2020 - August 2021)
Google Developer Students Club AASTU (September 2020 - August 2021)
Organized tech-related events study jams in which more than 100 students were introduced to Flutter, and Kotlin for android app development.
Organized tech competition events in which more than 50 students used their knowledge from the study jam and solved real-world problems.
Core Team Member
SheTech Club (2018 - 2021)
Organized women's hackathon competitions in which 30 female students participated to demonstrate the solution they come up with to solve the traffic jam in Addis Ababa City.
Projects
This project focuses on using computer vision methods based on deep learning for creative optimization in mobile advertising. The customer has been running a sizable number of ads, and each one has its own creatives. These designs were produced based on the designers' prior work and the requirements of the business. Because of this, it is impossible to gauge a creative's potential performance before running them and to predict how well they will do after. As a result, the creation of deep learning-based algorithms that extract elements from creative materials and relate them to the KPI parameters of the related campaigns was the primary aim of this project.
This was a team effort to build a Speech to text engine that would be able to transcribe two African languages. These were Amharic and Swahili. We explored some deep-learning architectures that would give the best results. And at the end, we build a web-app that a user could use to interact with our model.
This was a project for helping an advertisement company measure the effectiveness of the ads they made for a client, It was also built in a way that the company could use the system for measuring future ads performance. The project was completed in teams. I was responsible for setting up the MLOps components and the ML-based A/B testing pipeline.
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