Erdem Karaköylü

Erdem Karaköylü has held several positions throughout their career. Erdem started as an Asteğmen in the Pervari J. Kmd Bölüğü at 3. Komando Tugayı, TSK from April 2002 to June 2002. Erdem then became a Tim Komutanı in the same division from June 2002 to March 2003. After that, Erdem worked as a Research Assistant at Scripps Institution of Oceanography from September 2003 to April 2010, conducting research on plankton ecology and zooplankton behavior. Erdem continued their research as a Postdoctoral Researcher at Scripps Institution of Oceanography from May 2010 to April 2011. Erdem then joined the University of Maryland Center for Environmental Science as an Associate Research Scientist from April 2011 to September 2013. Following that, they worked as a Software Engineer & Data Scientist at SAIC, contractor at NASA Goddard Space Flight Center from September 2013 to December 2019, focusing on processing and analyzing satellite remote sensing data. Erdem's most recent role is as a Data Scientist at Research Innovations Incorporated starting from December 2019.

Erdem Karaköylü holds a Doctor of Philosophy (PhD) degree in Biological Oceanography from Scripps Institution of Oceanography, which they obtained from 2003 to 2010. Prior to that, they completed a Bachelor of Science (B.S.) degree in Oceanography from Florida Institute of Technology from 1997 to 2001. Erdem also holds a Bachelor of Arts degree in Business Administration from Eastern Mediterranean University, which they earned from 1993 to 1997. Erdem completed their French Baccalaureate in Biology and Mathematics from Lycée Antoine de St Exupéry (Alliance Française) from 1986 to 1990.

In addition to their formal education, Erdem Karaköylü has also obtained several certifications. Erdem completed a course on Sequence Models from Coursera in September 2020. Erdem also completed a course on Fake News Detection with Machine Learning from Coursera, and a Kaggle Python Tutorial on Machine Learning from DataCamp. Furthermore, they have a certification in Probabilistic Graphical Models 1: Representation from Coursera.