Şadi Evren Şeker is an academic and engineer whose work spans computer science, management information systems, and artificial intelligence. Since January 2023, he has been continuing his academic activities at Istanbul University’s Department of Computer Engineering with the title of Professor Doctor, and since April 2023, he has served as Dean of the Faculty of Computer and Information Technologies at the same university. His research primarily focuses on data mining, natural language processing, text mining, and machine learning.

He completed his undergraduate education between 1996 and 2000 at Yeditepe University’s Department of Computer Engineering, graduating as the top student in his class. He earned his master’s degrees in 2003 from the Institute of Science and Technology at Yeditepe University in the Computer Science and Engineering program, and in 2004 from the Institute of Social Sciences at Istanbul Technical University in the Science, Technology and Society program. He completed his doctoral studies between 2005 and 2010 at the Institute of Science and Technology at Yıldız Technical University in the Computer Engineering program, with a thesis titled “Event Ordering in Basti Turkish Texts.” His postdoctoral research was conducted between 2012 and 2014 in the United States at the University of Texas at Dallas, focusing on data mining, text mining, and natural language processing.

He began his academic career as a Research Assistant at Yeditepe University between 2000 and 2004. From 2007 to 2010, he taught as a faculty member at Istanbul Commerce University and Okan University. Between 2011 and 2016, he served as a Doctoral Lecturer and Associate Professor in the Business Department at Istanbul Medeniyet University, during which he also held administrative roles including Head of the Sub-Discipline (2013–2015), Department Head (2014–2015), and Center Director (2014–2015).

During the 2016–2017 academic year, he was a Visiting Faculty Member in the Department of Computer Science at Smith College in the United States. Upon returning to Türkiye, he worked as an Associate Professor and Professor in the Business Department at Istanbul Şehir University (2016–2019) and in the Department of Computer Engineering at Antalya Science University (2019–2023). In 2023, he joined the faculty of Istanbul University and was appointed Dean of the Faculty of Computer and Information Technologies.

Şeker’s academic work encompasses data mining, artificial intelligence, natural language processing, algorithms, and business intelligence. In addition to his academic research, he founded the platform “Computer Concepts” in 2007 to develop Turkish terminology and content in the field of computer science. From 2019 to 2023, he founded and led OptiWisdom, a company that developed big data and artificial intelligence solutions for industrial applications. He also served as a manager on the Kolay.AI project, which provides automated machine learning (AutoML) services for small and medium-sized enterprises.

Among the projects he has led or participated in are “Deep Learning-Based MRI Analysis for Autoimmune Diseases of the Central Nervous System,” supported by TÜSEB (2025–2027); “An Artificial Intelligence Engine for Solving End-to-End Matching and Recommendation Problems Using Automated Machine Learning,” supported by TÜBİTAK (2021–2025); and the “OptiSegment” project (2019–2020).

His books on artificial intelligence, programming languages, and data mining include:

• Algorithms (Nobel, 2021).
• Machine Learning with Python (2020).
• Introduction to C++ at Beginner Level (2018).
• Data Mining with RapidMiner (2016).
• Introduction to Data Mining with Weka (2016).
• Introduction to Business Intelligence and Data Mining: With WEKA (2013).
• Digital Business (2013).
• Introduction to Programming and Data Structures: JAVA, C, C++ Languages (2009).

Additionally, his works such as “A Systematic Evaluation of Large Language Models and Retrieval-Augmented Generation for the Task of Kazakh Question Answering” (2025) and “Performance prediction of roadheaders using ensemble machine learning techniques” (2019) have been published in international peer-reviewed journals^【1】.