Results-driven Machine Learning Engineer with 2+ years of hands-on experience crafting data systems across diverse industries, fueled by nothing but the finest brews of coffee ☕️. Passionate about coding, open to new technologies, and committed to completing all tasks given with diligence and hard work. Proven track record in academia and professional level.
In my senior year, I conducted research on Medical Image Captioning using uni-modal, cross-modal, multi-modal Deep Learning approaches such as CNNs, Transformers, BERT-based models, RNNs, and Neural Retrieval algorithms. This work was a part of my Thesis, and also tied to my involvement in the 2022 ImageCLEFmedical campaign, where I led the Caption Prediction Task. I'm pleased to report my implemented systems ranked 1st and 2nd out of 10 teams based on the main evaluation metrics. You can read our publication "AUEB NLP Group at ImageCLEFmedical Caption 2022", Proceedings of the CLEF 2022 at this link.
