---
type: Research experience
title: Experience
description: Research experience and affiliations.
tags:
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  - experience
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timestamp: '2026-06-13T20:46:02+08:00'
modified: '2026-08-09T18:32:45.762Z'
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review_due: '2026-11-07'
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## Current affiliation

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

## Doctoral research in AI and networks

**PhD student**, 2026-08-01-present. Advisor: [Angela Yingjun Zhang](./Angela_Yingjun_Zhang.md), [The Chinese University of Hong Kong](./The_Chinese_University_of_Hong_Kong.md).

Qiao's current doctoral work is centered on [data-centric ML](./Data_Centric_Machine_Learning.md) and [AI and networks](./AI_and_Networks.md), 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.md) computation remains one related method for constructing shared distributional references without assuming that all raw data can be pooled.

Related pages: [AI and Networks](./AI_and_Networks.md), [Distributed Learning](./Distributed_Learning.md), [Wasserstein Geometry](./Wasserstein_Geometry.md), [Distributed Wasserstein Barycenter](./Distributed_Wasserstein_Barycenter.md), and [Collaborative Evaluation](./Collaborative_Evaluation.md).

## Research on data-centric ML systems

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

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](./Machine_Unlearning.md), [Influence Functions](./Influence_Functions.md), [Certified Data Removal](./Certified_Data_Removal.md), [Trustworthy AI](./Trustworthy_AI.md), and [Data Centric ML](./Data_Centric_Machine_Learning.md).

## Research on trustworthy LLM systems

**Full-time research intern**, 2025-06 to 2025-12. Advisor: Pang Yan, James, [NUSRI CQ](./NUSRI_CQ.md).

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](./LLM_Reliability.md), [Synthetic Data](./Synthetic_Data_and_Model_Collapse.md), and [Wasserstein Geometry](./Wasserstein_Geometry.md).
