This project aimed at analyzing bitcoin graph data and providing sophisticated information on bitcoin transaction data and aggregate on-chain statistics derived from graph data. The outcome statistics in the form of time series were later used in predictive machine learning models.
The goal of this project was to develop a Deep Learning powered solution detecting a disease on medical X-ray images. The model I have developed achieved an AUC of 0.98. The solution is part of a bigger system which is developed to assist medical doctors in their daily work.
The goal of this project was to develop a machine learning backed investment strategy based on multiple binary positive and negative signals sourced from price, market, and environmental analysis. The strategy I have developed performed 4x better than the baseline.
The goal of this project was to build an ETL system processing live online bitcoin transactional data and loading it to Neo4J graph database on the fly. The data included not only block chains but all details about transactions and interactions between addresses.
The goal of this project was to model blockchain historical data as a graph and to build an ETL system loading the entire data (blocks, transactions, inputs, outputs, addresses) into a Neo4j graph database. The solution I designed performed 10x faster than the best-reported system.
The goal of this project was to develop an AI backend engine for an intelligent decision support system which asses an ischemic stroke risk. The system was developed in collaboration with a health insurer to allow preventive interventions for patients with high risk of a stroke.
The goal of this project was to build an ETL system collecting and computing data about trades and trading positions for various cryptocurrencies. The data about trades and trading positions were integrated on the fly from multiple trading platforms via REST APIs and web socket.
The goal of this project was to develop a rules-based decision support system. The solution was assisting medical doctors to make a fast decision about blood transfusion. The project was conducted in collaboration with medical doctors as domain experts.
The goal of this project was to develop an AI-backed decision support system helping medical doctors to decide about blood transfusion. In collaboration with multiple hospitals, we collected historical data about blood transfusions, patients, and medical tests relevant to blood transfusion.
The goal of this project was to build an ETL system collecting, normalizing, and aggregating data about the popularity index (search volume) of various keywords from Google Trends. The system had two subsystems one for pulling historical data in bulk, the other for current data.