Research
Academic and applied work on causal inference, experimentation, marketplace systems, and applied AI. Papers below are linked to their canonical source (SSRN, arXiv, INSEAD, or conference proceedings). Full list on Google Scholar.
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Restaurant Menu Categorization at Scale: LLM-Guided Hybrid Clustering
S. Latif, A. Mehmood, S. Turki, H. Ameer, Ivan Gorban, F. Fateh
Proceedings of the 18th International Natural Language Generation Conference (INLG 2025), 2025
Hybrid clustering approach that uses LLMs to categorize restaurant menus across heterogeneous schemas at scale.
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Unraveling the Implications of Silent Labor Time in the Gig Economy
S. Arora, V. Choudhary, S. Hasija, Ivan Gorban, S. Turki, A. Shekhani
INSEAD working paper, 2025
Empirical study of silent labor time — unpaid time that gig-economy workers spend waiting between paid tasks — and its implications for platform design and worker welfare.
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STEF-DHNet: Spatiotemporal External Factors Based Deep Hybrid Network for Enhanced Long-Term Taxi Demand Prediction
S. Hassan, M. Tahir, M. Uppal, Z. Khalid, Ivan Gorban, S. Turki
arXiv preprint arXiv:2306.14476, 2023
A deep hybrid network for long-term taxi demand forecasting that incorporates spatiotemporal patterns alongside external factors such as weather and events.
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Do Recommendation Systems Work: A Field Experiment
V. Choudhary, Ivan Gorban, S. Turki
SSRN 4284103, 2022
A field experiment evaluating the causal impact of recommendation systems on user behavior and platform metrics.