Papers

Research Interests

My research lies at the intersection of operations management, causal inference, and machine learning, with a focus on developing data-driven methods for decision-making in uncertain service environments.

Submitted & Working Papers

Queueing Causal Models: Comparative Analytics in Queueing Systems
(with Opher Baron, Dmitry Krass, Mark van der Laan, and Arik Senderovich)
Minor revision, MSOM, 2025 (third round)

• 🥇 First prize, CORS 2025 Queueing and Applied Probability SIG
• 🏆 Winner, 2024 Oded Berman Student Paper Competition
• 🎖️ Finalist, CORS 2024 Best Student Paper Competition (Open Category)
• Service Management SIG, MSOM 2024
• 2023 Rotman TD MDAL Research Grant

Bayesian Pricing for Impatient Customers with Unknown Valuation
(with Philipp Afèche, Opher Baron, and Dmitry Krass)
Work in progress

• 2024 Rotman TD MDAL Research Grant

Queueing Causal Models for Emergency Department Efficiency
(with Opher Baron, Dmitry Krass, Mark van der Laan, and Arik Senderovich)
Work in progress

• 2025 Rotman TD MDAL Research Grant

Leveraging Advanced Analytics to Streamline the Emergent Dialysis Process at Parkland Hospital
(with Olga Bountali, Sila Cetinkaya, Michael Hahsler, Farnaz Nourbakhsh, and Henry Quinones)
Submitted to Healthcare Analytics

Publications

Pan, Y., Xu, Z., Guang, J., Chen, X., Dai, J. G., Wang, C., … & Pan, H. (2021).
A high-fidelity, machine-learning enhanced queueing network simulation model for hospital ultrasound operations.
Winter Simulation Conference (WSC), IEEE, 2021, pp. 1–12.