Strengthening Global Academic Reputation: ITS Brings Foreign Adjunct Professor to Deliver Guest Lecture on Econometrics in the Age of AI

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Two lecturers from the Department of Statistics of ITS stand on the left and right sides accompanying Prof. Dr. Anton Abdulbasah Kamil who stands in the middle.
Two lecturers from the Department of Statistics of ITS stand on the left and right sides accompanying Prof. Dr. Anton Abdulbasah Kamil who stands in the middle.

Department of Statistics of Institut Teknologi Sepuluh Nopember (ITS) invited Prof. Dr. Anton Abdulbasah Kamil, a Visiting Adjunct Professor in the EQUITY WCU program, to deliver an international guest lecture titled “Econometrics in the Age of Artificial Intelligence” on Thursday, April 30, 2026, at Hall Tower 2, ITS. 

Prof. Dr. Anton Abdulbasah Kamil explained how artificial intelligence (AI) and econometrics complement each other in data analysis. The international lecturer emphasized that AI offers strong predictive power and high flexibility in handling complex data.

The lecturer further stated that AI still requires the theoretical foundation of econometrics to ensure valid interpretation. This foundation serves to prevent researchers from producing misleading results such as spurious correlations.

Econometrics provides a robust theoretical framework despite its occasional lack of flexibility. The integration of both approaches represents the future direction for generating more accurate, reliable, and meaningful insights.

This guest lecture provides significant benefits for ITS students in facing the challenges of data analysis in the digital era. Students gain an understanding of how to remain critical of AI-based analysis results by grounding their work on econometric theory.

Prof. Dr. Anton Abdulbasah Kamil, Visiting Adjunct Professor in the EQUITY WCU program
Prof. Dr. Anton Abdulbasah Kamil, Visiting Adjunct Professor in the EQUITY WCU program.

Students also learn to identify potential interpretation errors such as spurious correlations that often emerge when analyzing big data without a strong theoretical foundation. This knowledge equips students to avoid blindly accepting predictive results from AI models without conducting proper econometric validation.

Furthermore, students acquire insights into best practices for integrating traditional econometric methods with machine learning approaches. This integration enables students to produce analyses that are not only accurate in prediction but also explainable in terms of causality.

Another benefit obtained by students is the enhancement of their competitiveness in the global job market. Companies and research institutions currently highly need experts who master both fields simultaneously: econometrics and artificial intelligence.

This activity constitutes one of ITS’s strategic efforts to realize its vision as a world-class university. ITS actively presents global perspectives through the involvement of international faculty members in student learning processes.

Participant enthusiasm remained high throughout the guest lecture session. This activity also supports the achievement of the Sustainable Development Goals (SDGs), particularly Goal 4 on quality education and Goal 9 on innovation and technological infrastructure.

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