More than 9 years of experience in computational problem-solving, ranging from academia to the financial industry. Machine Learning quantitative developer and PhD candidate in computational astrophysics, with planned completion in August 2025.
Currently a Machine Learning Quantitative Developer at Morgan Stanley, Budapest, with over 2 years of experience. Focused on designing, developing, deploying, monitoring, and maintaining end-to-end AI/ML solutions to improve workplace efficiency, with ongoing work on integrating LLM-based solutions using RAG to harness the potential of generative AI.
I am the first and co-author of three peer-reviewed publications in high-impact journals addressing topics in galactic dynamics and space weather. The galactic dynamics research explores the equilibrium distribution of stars and compact objects in galactic nuclei, regions with high object density and potential for gravitational wave production. The space weather study leverages machine learning for precise segmentation of spectrograms containing whistler traces, enhancing processing efficiency and automation.
Possess expertise across multiple stages of the software development lifecycle and associated tools, along with deep knowledge and extensive experience in artificial intelligence and machine learning.