I have a background in Computer Science and experience with data engineering, data science, machine learning and web development.
You can find an online version of this resume here
MedApp development of AI
|Machine learning||LightGBM, Scikit-learn, Keras (Tensorflow, Pytorch), NLTK, Word2Vec, Gensim, OpenCV, Spark MLlib|
|Automation||Beautiful Soup, Selenium, Telegram Bot|
|Frontend||React, Vue, Angular|
|Backend||Flask, Django, Laravel|
|DevOps||CI/CD, Kubernetes, Linux, Git, Docker, Jenkins|
|Databases||NoSQL (MongoDB), SQL (MySQL, MariaDB, PostgresQL, T-SQL)|
|Data Engineering||Spark, ETL, SQL, Data Factory, Databricks, Airflow|
My master thesis was on the topic of Automated Machine Learning, which is the field that strives to automate the data scientist's process of finding and optimizing the right machine learning model. We do so by testing machine learning models with different configurations and observing their performance to determine interesting configurations to test next. This can be done in many different ways: genetic programming, bayesian optimization, bracket-wise competition, etc.
I developed a new method closely related to bayesian optimization that uses much less time to decide interesting configurations, and converges faster to the optimum.