For a financial analysis firm processing corporate financial documents, I developed a RAG system that combines vector search with graph-based provenance tracking. The solution enables natural language queries across complex financial documents while maintaining complete traceability of information sources through Neo4j’s native graph capabilities.
For a customer service organization processing thousands of daily support conversations, I developed an automated summarization system using fine-tuned FLAN-T5. The solution generates concise summaries for management review, reducing manual analysis time and enabling rapid identification of critical issues and customer sentiment trends.
In this project, I collaborated with a client developing a revolutionary non-invasive glucose monitoring system. Our initial focus was on calibrating the physical device to accurately convert raw signal measurements into glucose levels. We began by analyzing chronoamperometry data to identify optimal parameters for the device and establish a robust calibration method.
This project aimed at providing means to compute how likely it is that two bitcoin addresses belong to the same entity.
The goal of this project was to develop a Generative Adversarial Network (GAN) capable of generating unique logotypes for teams on a gaming platform. The trained GAN architecture enabled the creation of 1M unique logotypes for the players of the platform.
This project aimed at analyzing customers’ bitcoin transactions in order to provide their liquidity estimation and spending analysis.
The client was facing trouble collecting and analyzing data on customers using their API platform. I was requested to research and develop a solution to enable data analytics and visualization on their platform. The delivered solution allowed my client to understand their clients’ behavior.
The goal of this project was to research and evaluate deep reinforcement learning methods to improve order placement in limit order markets.
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.