Dingyi Zhuang

Feb	2025	Paper “GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks” accepted at ICLR 2025 (Spotlight).
Feb	2025	Paper “Time Series Supplier Allocation via Deep Black-Litterman Model” accepted at AAAI 2025 (Oral).
Feb	2025	Paper “Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks” accepted at WWW 2025 and also appearing in TRB 2025.
Nov	2024	Paper “ItiNera: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning” accepted at EMNLP 2024 (Industry Track), also KDD UrbComp 2024 Best Paper.
Oct	2024	Presented “Quantifying Uncertainty: Advancing Robustness, Reliability, and Fairness in AI-Driven Transportation Demand Modeling” at INFORMS 2024.
Aug	2024	Paper “Uncertainty-aware Probabilistic Graph Neural Networks for Road-level Traffic Crash Prediction” accepted at Accident Analysis & Prevention (AAP) 2024.
Jun	2024	Presented “Uncertainty Quantification on Sparse Spatiotemporal Data Prediction” at Machine Learning Seminar, Morgan Stanley.
Jun	2024	Started my internship at Morgan Stanley advised by Majid Behbahani
Mar	2024	Paper “SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks” accepted at SIGSPATIAL 2024 (Oral), also Spotlight talk at TGL Workshop, NeurIPS 2023.
Oct	2023	Paper “Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction” accepted at CIKM 2023.
Oct	2023	Presented “Modeling Multi-perspective Nature of Urban Dynamics” at the Allen Turing Institute.
Oct	2023	Presented “Modeling Multi-perspective Nature of Urban Dynamics” at The Space Time Lab, University College London.
Oct	2023	Presented “Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks” at The Bartlett Centre for Advanced Spatial Analysis, UCL.
Jul	2023	Presented “Uncertainty Quantification of Sparse Trip Demand Prediction” at Lyft.
Jul	2023	Presented “Deep Hybrid Model with Urban Road Network for Travel Demand Analysis” at the Transit Data Section, WCTR 2023.
May	2023	Presented “Deep Hybrid Model with Urban Road Network” at the MIT Mobility Initiative Forum.
Apr	2023	Presented “Uncertainty Quantification of Sparse Trip Demand Prediction” at the Urban Artificial Intelligence Laboratory, University of Florida.
Feb	2023	Presented “Deep Hybrid Model with Urban Road Network for Travel Demand Analysis” at CEE Research Days, MIT Media Lab.
Oct	2022	Presented “Uncertainty Quantification of Sparse Trip Demand Prediction” at the College of Computer Science, Sichuan University.
Aug	2022	Paper “Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks” accepted at KDD 2022 (Oral).

Spatio-temporal Data Modeling

Uncertainty Quantification

Intelligent transportation

GenAI (LLM/MMLM)