Ding Zhao

Assistant Professor

Carnegie Mellon University

United States

Research Interests

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About

Ding Zhao is an Assistant Professor at Carnegie Mellon University in the United States. His research focuses on various areas including reinforcement learning, autonomous vehicles, and simulation methodologies. Recent publications highlight his contributions to safety-critical systems, scene generation, and machine learning applications in medical imaging.

Articles (18)

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2025

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2022

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2022

Collaborators (2)

Kwai Hung Henry Lam

University of Michigan

UNITED STATES

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WENHAO DING

Carnegie Mellon University

UNITED STATES

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Ding Zhao

Research Interests

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About

Articles (18)

Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications

Black-Box Rare-Event Simulation for Safety Testing of AI Agents: An Overview

DiffScene: Diffusion-Based Safety-Critical Scenario Generation for Autonomous Vehicles

Semantically Adversarial Scene Generation With Explicit Knowledge Guidance

Zero-shot classification for cardiac magnetic resonance imaging using multimodal pretraining

Safety-Aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving

Online legal driving behavior monitoring for self-driving vehicles

Constrained Decision Transformer for Offline Safe Reinforcement Learning

Learning to View: Decision Transformers for Active Object Detection

A Survey on Safety-Critical Driving Scenario Generation—A Methodological Perspective

Semantic Traffic Law Adaptive Decision-Making for Self-Driving Vehicles

Overconservativeness of Variance-Based Efficiency Criteria and Probabilistic Efficiency in Rare-Event Simulation

Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables

Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation

Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability

Robust unsupervised learning of temporal dynamic vehicle-to-vehicle interactions

GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

Collaborators (2)

Kwai Hung Henry Lam

WENHAO DING

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