3d Presentation | A Personalized Machine-Learning-enabled Method for Efficient Research in Ethnopharmacology | Andreas Kontogiannis
This week’s main speaker is Andreas Kontogiannis (M.Eng) and the presentation is titled “A Personalized Machine-Learning-enabled Method for Efficient Research in Ethnopharmacology. The case of Southern Balkans and Coastal zone of Asia Minor”
A discussion will follow in which everyone is invited to share their perspectives on the previous/future actions of NanoAI.
Description: Ethnopharmacology experts face several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, the different quality of language use across sources, present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research, aimed at the Southern Balkans and Coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “Expert-Apprentice” paradigm in an automatic URL extraction process, through crawling, where the apprentice is a Machine Learning (ML) algorithm, utilizing a combination of Active Learning (AL) and Reinforcement Learning (RL), and the Expert is the human researcher. ML-powered research improved 3.1 times the effectiveness and 5.14 times the efficiency of the domain expert, fetching a total number of 420 relevant ethnopharmacological documents in only 7 hours versus an estimated 36-hour human-expert effort. Therefore, utilizing Artificial Intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.
Education: M.Eng in ECE NTUA (2015-2021)
Research Experience:
Research Assistant in University of Piraeus, AI Laboratory (2021-present),
Undergraduate Research Assistant in NCSR “DEMOKRITOS”, IIT, SKEL | The AI Lab (2020-2021)
Research Interests: Reinforcement Learning, Imitation Learning, Active Learning, Hierarchical ML, Bayesian Inference
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You can find more info on this upcoming talk as well as on our previous talks at http://nano-ai.iit.demokritos.gr/
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