Conference Speaker Callvia xScore 37

Machine learning economics

From: Yucheng Yang (@YuchengYang1993)

📢📢Call for Papers: Conference on Machine Learning in Economics 📍Beijing | July 11–12, 2026 Topics: AI+macro, finance, metrics, theory, applied, etc. Confirmed speakers (so far): F Kübler, @comp_simon, S Maliar, @zhigangfeng, Gopalakrishna, Payne 🗓️Submit by May 1: https://t.co/Ia1tAOkFMK

Deadline: May 1, 2026Detected Apr 15

Suggested angles

Position your work within the macro-finance intersection that the conference explicitly targets—highlight how your ML research directly addresses inflation forecasting, monetary policy transmission, or asset pricing in ways traditional econometric approaches cannot

Reference the specific caliber of confirmed speakers (Kübler's structural work, Maliar's computational methods, Feng's finance research) to frame your submission as building on or contrasting with established approaches in ML+economics rather than entering a generic field

Emphasize applied implementation if your work has real-world deployment in financial institutions or central banks—the conference's balance of theory and applied tracks suggests they want papers that bridge academic rigor with practitioner relevance

Submit a paper that directly engages with one of the confirmed speakers' research areas (macro forecasting, computational economics, or quantitative finance) to signal coherence with the conference's specific intellectual community.