Keith McCormick. Keith is an independent instructor, at both the University of California at Irvine (UCI) and on LinkedIn Learning. There, you can find his KNIME based courses Introduction to Machine Learning with KNIME, and Data Science Foundations - Data Assessment for Predictive Modeling. Keith not only works as a teacher, he is a teacher at heart. He enjoys preparing his students for the professional world, provoking that effect of surprise when revealing insights from data, and grading his students. Yes, grading, since this is the moment where he can interact with the work of the students, not only to criticize, but also to explain and praise (see Keith’s interview in DCE Magazine).

Giuseppe Di Fatta. Giuseppe Di Fatta is Head of the Department of Computer Science at the University of Reading, where he also teaches data science courses using KNIME Analytics Platform: Data Science Algorithms & Tools, and Data Analytics and Mining. Among the many tools for data science, he also has introduced KNIME Analytics Platform, because he sees the benefit of an open source, low code data science tool in a student’s portfolio for their future career. Giuseppe also collaborated with KNIME to develop the current KNIME Certification Program and designed many of the examination questions. So, if you took the exam and found the questions too hard to answer … well, now you know what it takes to become a KNIME Contributor Of The Month

Alzbeta Tuerkova. Alzbeta Tuerkova is a postdoctoral researcher at Uppsala University. Alzbeta obtained a PhD in biology, with a thesis on hepatic organic anion transporting polypeptides belonging to the SLCO family, from the University of Vienna. Together with Barbara Zdrazil, Alzbeta published a notable research paper about an efficient and reproducible, fully KNIME based, drug-repurposing application to identify new drug candidates for rare diseases and COVID-19 [1]. The workflow, tutorials, and information gained on COVID-19 data have been made freely available to the scientific community for follow-up studies and learning (see “Automated Drug Repurposing Pipeline in KNIME".).

[1] Tuerkova, A., Zdrazil, B. A ligand-based computational drug repurposing pipeline using KNIME and Programmatic Data Access: case studies for rare diseases and COVID-19. J Cheminform 12, 71 (2020). https://doi.org/10.1186/s13321-020-00474-z

Angus Veitch. Angus is an active member of the Australian community and has written numerous blog articles about KNIME. His interest lies on the analysis of social media data, especially Twitter data. In his TweetKollidR workflow article, he describes his KNIME workflow for creating text-rich visualizations of Twitter data around a given hashtag. He is also a member of the current editorial board of the newly created KNIME publication on Medium “Low Code for Advanced Data Science”, a journal with the aim of publishing articles by the KNIME community.

Dennis Ganzaroli. Dennis has been on fire these past 12 months. He has been successfully predicting everything, like Nostradamus and the octopus Paul, but using data science models and KNIME Analytics Platform. First, he predicted the curve for the COVID spread worldwide; then he broke it down to predict the COVID spread country by country; and lastly he even predicted the outcome of this July’s European soccer championship, UEFA Euro 2020, with the help of a curious character: Yodime. The beauty of it is that he explained it all in detail, how the models were created and trained to perform such predictions, without disdaining a mix of tools, like KNIME, Jupyter, and Tableau, when needed.

@Makkynm. Unfortunately, I do not speak Japanese, but my colleagues, who do, have ensured me that the blog run and maintained by @Makkynm is a precious resource to grow your knowledge of KNIME Analytics Platform, be it for beginner, intermediate, or advanced KNIME users. The blog is written in Japanese, and it is aimed at the Japanese data science community. Notice that we do not know who @Makkynm is since he sent his Twitter avatar to represent him in the Hall of Fame of COTM 20/21.

Armin Ghassemi Rudd. Armin Ghassemi Rudd's Translator and Get Request Plus components can be found on the KNIME Hub and are also featured on our Verified Components webpage. The Translator component uses Google Translate to translate any input text from/to supported languages. The Get Request Plus component adds the “Retry” option to the GET Request node. Besides creating awesome components, Armin has been a long term and very active contributor to the KNIME Forum, where he goes by the name of armingrudd.

SJ Porter. SJ Porter’s GUID Generator and Text Preprocessing components were the community contributions in October 2020, which at that time had the most number of downloads on the KNIME Hub. The first component was, finally, a Globally Unique Identifier (GUID) generator for KNIME. This component is useful for creating a unique key that is not based on Row ID. The Text Preprocessing component uses extremely fast regex-based text processing functions to remove specific types of characters from a String column and normalize the data as much as possible without over-processing. This component eliminates the need to convert text to a Document type in order to preprocess it.