Dr. Anna WrΓ³blewska | Word Embedding | Best Researcher AwardΒ
Dr. Anna WrΓ³blewska, Warsaw Univeristy of Technology, Poland
Professional Profile:
Summary of Suitability for Best Researcher Award
Anna WrΓ³blewska is a highly accomplished researcher with extensive experience in designing intelligent systems and analyzing semantic data in both commercial and scientific settings. As an assistant professor at the Warsaw University of Technology and a senior data scientist at Applica.ai, she has led multiple R&D projects in text mining, image recognition, and model diagnostics. Her expertise in machine learning, particularly in semantic understanding, data modeling, and interpretability, is well demonstrated through her contributions to various high-impact publications. With over 50 research articles in renowned Polish and international journals, she has made significant advancements in multimodal data fusion, document analysis, and medical imaging.
π Education:
- Holds a Ph.D. in Computer Science (specific university details not provided).
- Specializes in intelligent systems, semantic data processing, and machine learning.
πΌ Work Experience:
- Assistant Professor at Warsaw University of Technology π«
- Focuses on machine learning, semantic data modeling, and AI applications.
- Senior Data Scientist at Applica.ai π€
- Oversees R&D projects in text mining, image recognition, and model interpretability.
- Former Data Scientist at Allegro π
- Worked on intelligent data analysis in Eastern Europeβs largest e-commerce platform.
π Achievements & Contributions:
- Published 50+ research papers in Polish and international journals π
- Expert in machine learning for practical applications, especially in semantic understanding and AI model interpretability π―
- Leading multiple AI-driven research projects in academia and industry π¬
π Awards & Honors:
- Recognized as a leading researcher in AI & Data Science π
- Contributions in text mining, image recognition, and AI explainability earned industry recognition π
- Speaker at various AI & machine learning conferences π€
PublicationΒ Top Notes:
Kleister: key information extraction datasets involving long documents with complex layouts
CITED:132
Effective techniques for multimodal data fusion: A comparative analysis
CITED:63
CITED:60
GEval: Tool for debugging NLP datasets and models
CITED:39
Named Entity Recognition–Is there a glass ceiling?
CITED:37