---
type: Research experience
title: Experience
description: Research experience and affiliations.
tags:
  - en
  - experience
  - profile
  - research-experience
timestamp: '2026-06-13T20:46:02+08:00'
modified: '2026-08-09T18:32:45.762Z'
content_hash: 'sha256:c213babca2918a9728aaac57badb72797578f588269356d9c47e712d7906703b'
reviewed_at: '2026-08-10T02:34:00+08:00'
review_due: '2026-11-07'
name: Experience
summary: Research experience and affiliations.
occupation: Research experience
---
## Current affiliation

- PhD student in the Department of Information Engineering at [[The_Chinese_University_of_Hong_Kong|The Chinese University of Hong Kong]] since 2026-08-01, advised by [[Angela_Yingjun_Zhang|Angela Yingjun Zhang]].

## Doctoral research in AI and networks

**PhD student**, 2026-08-01-present. Advisor: [[Angela_Yingjun_Zhang|Angela Yingjun Zhang]], [[The_Chinese_University_of_Hong_Kong]].

Qiao's current doctoral work is centered on [[Data_Centric_Machine_Learning|data-centric ML]] and [[AI_and_Networks|AI and networks]], including AI for Networks and Networks for AI. The research asks how learning systems should operate when data, computation, and evaluation signals are distributed across clients, institutions, or networked infrastructure. [[Distributed_Wasserstein_Barycenter|Distributed Wasserstein barycenter]] computation remains one related method for constructing shared distributional references without assuming that all raw data can be pooled.

Related pages: [[AI_and_Networks]], [[Distributed_Learning]], [[Wasserstein_Geometry]], [[Distributed_Wasserstein_Barycenter]], and [[Collaborative_Evaluation]].

## Research on data-centric ML systems

**M.Eng. / master's research student**, 2023-03 to 2025-12. Advisor: [[Meng_Zhang|Meng Zhang]], [[Zhejiang_University]].

This line of work developed theoretically grounded approaches for data influence attribution, machine unlearning, and data-centric trade-offs in trustworthy AI. It also included cloud-edge collaborative human-space healthcare, where multimodal video, audio, and sensor signals were considered for real-time monitoring.

Related pages: [[Machine_Unlearning]], [[Influence_Functions]], [[Certified_Data_Removal]], [[Trustworthy_AI]], and [[Data_Centric_Machine_Learning|Data Centric ML]].

## Research on trustworthy LLM systems

**Full-time research intern**, 2025-06 to 2025-12. Advisor: Pang Yan, James, [[NUSRI_CQ]].

This research line studies trustworthy model behavior and synthetic-data evaluation, including distributed Wasserstein methods for low-resource synthetic-data evaluation when real data are limited or fragmented.

Related pages: [[LLM_Reliability]], [[Synthetic_Data_and_Model_Collapse|Synthetic Data]], and [[Wasserstein_Geometry]].
