Hua Wei (him/his) is an assistant professor at the School of Computing and Augmented Intelligence (SCAI) in Arizona State University (ASU). His research is mainly focused on machine learning and data mining. He got his PhD from Pennsylvania State University in 2020 under the supervision of Dr. Zhenhui (Jessie) Li. He is a recipient of the NSF CAREER Award 2025, Amazon Research Award, Cisco Faculty Research Award, and Toyota Faculty Research Award. His research has been funded multiple times by the National Science Foundation, the Department of Energy, and the Department of Transportation.

Research Interests: Reinforcement Learning, Data Mining, Urban Computing, Human-in-the-loop Computations

News

[04/21/2026] I’ll be giving a talk at the NAIRR Pilot Classroom Expansion Conference: Capacity and Community for Research-Emerging Institutions with Large Computing Programs, on “From Reliable LLMs to Instructional Agents: Bridging NAIRR Research and Large-Scale Classroom Implementation.”

[04/06/2026] Our papers “Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition“, “Uncertainty Quantification of Large Language Models through Multiple Uncertainty Sources“, “A Multi-Agent Framework for High-Interaction Terminal Simulation“, and “Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models” have been accepted by ACL 2026 (3 Main, 1 Findings).

[03/04/2026] Glad to receive support from NSF on our open-source system development.

[02/28/2026] Glad to receive support from Toyota.

[01/26/2025] Our papers “Latent Adaptation of Foundation Policies for Sim-to-Real Transfer” and “CAR-LoRA: Training Compression-Aware and Robust LoRA Adapters for Evolving LLMs” have been accepted by ICLR 2026.

[01/04/2025] Our papers “Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design“, “Zer0-Jack: A memory-efficient gradient-based jailbreaking method for black box Multi-modal Large Language Models“, and “Conformal Feedback Alignment: Quantifying Answer-Level Reliability for Robust LLM Alignment” have been accepted by EACL 2025.

[12/17/2025] Congratulations to our amazing undergrad researchers Shreyas Bachiraju and Albert Vo, for getting the honorable mention for the CRA 2025/2026 Outstanding Undergraduate Researchers Award!

[11/08/2025] Our paper “Measuring What Matters: Scenario-Driven Evaluation for Trajectory Predictors in Autonomous Driving” has been accepted by AAAI 2026.

[10/28/2025] Our research on shade modeling for heatwave is covered by local news: “Can AI help Arizonans find the shadiest route to keep cool? This ASU professor thinks so – KJZZ“

[10/17/2025] Glad to receive support from the Arizona Department of Transportation.

[09/03/2025] Glad to receive the Cisco Faculty Research Award.

[08/06/2025] Our paper “cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending” is accepted by CIKM 2025.

[07/28/2025] Glad to receive support from the Google Cloud Research Credits Program.

[07/10/2025] Glad to receive NSF CAREER Award.

[07/07/2025] Our paper “Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology” is accepted by the Second Conference on Language Modeling (COLM’25).

[07/01/2025] One paper “MC-BEVRO: Multi-Camera Bird Eye View Road Occupancy Detection for Traffic Monitoring” is accepted by ITSC’25.

[06/30/2025] Glad to receive consecutive support from the OpenAI API Researcher Access Program.

[05/17/2025] We will organize “The 4th Workshop on Uncertainty Reasoning and Quantification in Decision Making (UDM)” and “The 4th Workshop on AI Agent for Information Retrieval: Generating and Ranking“. Please consider submitting papers and attending our workshops!

[05/15/2025] Two papers are accepted by ACL’25.

[05/14/2025] Two papers are accepted by KDD’25 Research Track.

[05/09/2025] Our paper “Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control” is accepted by Reinforcement Learning Conference (RLC’25).

[05/08/2025] Our paper “Uncertainty Quantification for Physics-Informed Traffic Graph Networks” is awarded the Best Artifact Award by ICCPS’25.

[05/07/2025] Our survey and tutorial “Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey” is accepted by KDD’25. See you in Toronto!

[05/04/2025] Longchao is awarded the Best Poster Award by SIAM SDM 2025.

[05/01/2025] Two papers are accepted by ICML’25.