Bartosz Roguski

Activities: collecting, preprocessing data, feature engineering, exploring/implementing ML algorithms, training models, and everything in between

• Refactored the old codebase to improve its structure, readability, and maintainability.
• Implemented new functionalities in the platform to enhance user engagement and promote CO2 reduction.
• Managed user accounts, ensuring a smooth onboarding process and maintaining data security.
• Generated reports to provide insights into user activity, CO2 reduction progress, and overall platform performance.
• Improved codebase refactoring resulted in increased code efficiency, reducing system resource usage and enhancing platform performance.
• The implementation of new functionalities enhanced user engagement and encouraged active participation in CO2 reduction activities.
• Efficient user management contributed to a seamless user experience, attracting and retaining a growing user base.
• Generated reports provided valuable data-driven insights, enabling better decision-making and optimization of CO2 reduction strategies.

• Fine-tuned the Polish RoBERTa model specifically for the Question Answering task, adapting it to handle Polish language nuances and context.
• Collected and preprocessed training data for the Coreference Resolution task, ensuring the availability of high-quality data to train the model.
• Reimplemented the training procedure for Word-Level Coreference Resolution and modified it to suit the requirements of training a Polish language model.
• Developed a chatbot using the Rasa framework, leveraging its capabilities to handle natural language interactions.
• Implemented a custom Question Answering component within the chatbot, enabling it to query and provide answers based on the preprocessed Polish Wikipedia dump.
• The fine-tuning of the Polish RoBERTa model for the Question Answering task improved its performance in understanding and generating accurate answers in the Polish language.
• The collection and preprocessing of training data for Coreference Resolution facilitated the training of models capable of resolving references more effectively in Polish text.
• The reimplementation of the training procedure for Word-Level Coreference Resolution for Polish language models enhanced their performance in resolving coreferences accurately.
• The development of the chatbot provided users with an interactive and conversational interface to access information and get answers in Polish.
• The implementation of a custom Question Answering component using the preprocessed Polish Wikipedia dump enriched the chatbot's knowledge base, enabling it to provide relevant and informative responses to user queries.

• Fine-tuned GPT-2 on Polish poems, focusing on Modernism poetry and Interwar period poetry, respectively.
• Conducted data collection from poezja.org, utilizing web scraping techniques to gather a substantial dataset of Polish poems.
• By training models on specific poetic styles, a deeper understanding of the characteristics, themes, and linguistic nuances of these periods in Polish poetry was achieved.
• The data collected from poezja.org through web scraping provided a valuable resource for training the models and contributed to the availability of a diverse and comprehensive dataset for further research and analysis in the field of Polish poetry.

• Collaborated as part of a two-member team to develop a fraud detection model specifically for Ethereum transactions.
• Conducted data preprocessing and feature engineering to prepare the Ethereum transaction data for model training.
• Explored and implemented various machine learning algorithms and techniques suitable for fraud detection in Ethereum transactions.
• Fine-tuned the model parameters and evaluated its performance to achieve the best possible accuracy and precision in identifying fraudulent transactions.
• The creation of a fraud detection model for Ethereum transactions addressed a critical need for ensuring the security and integrity of transactions in the Ethereum blockchain network.
• By effectively detecting fraudulent transactions, the model contributed to safeguarding the assets and trust of users and investors in the Ethereum ecosystem.
• The model's ability to identify fraudulent activities with high accuracy helped in reducing financial losses and mitigating potential risks associated with fraudulent transactions.
• The successful implementation of the fraud detection model showcased the potential of machine learning in enhancing security measures within blockchain systems and inspired further research and development in this domain.