• HOME
  • RÉSUMÉ

Qian Jiang

Applied Scientist

AWS AI Labs

Santa Clara, CA

qianj3 AT illinois DOT edu

github.com/qianjiangcn

linkedin.com/in/qianj3

Google Scholar

Updated on 2025/1/12. 2025, Qian Jiang

About

I am an Applied Scientist at Amazon AWS AI Labs, working on LLM-assisted code development. I obtained my Ph.D. degree in 2024, from the department of Electrical and Computer Engineering (ECE) at the University of Illinois at Urbana-Champaign (UIUC). My research spans Computer Vision, Large Language Models(LLMs), Multi-modal Learning and Generative Modeling, with a particular focus on developing efficient and robust approaches for real-world applications.

Research Interests

Machine Learning, Computer Vision, LLMs, Multimodal Learning, Generative Modeling, Foundation Models, Efficient ML.

Current and Past Affiliations

Amazon
2024-Now
Summer-Fall 2022, Summer 2023
UIUC
2019-2024
Microsoft
Fall 2023
IBM Watson
Summer 2020
C3SR
2019-2021
UCLA
Summer 2018
Technion
Summer 2017
UESTC
2015-2019

Projects

Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning

Qian Jiang, Changyou Chen, Han Zhao, Liqun Chen, Qing Ping, Son Dinh Tran, Yi Xu, Belinda Zeng, Trishul Chilimbi

CVPR 2023

PDF

EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search

Qian Jiang*, Xiaofan Zhang*, Deming Chen, Minh N. Do, Raymond A. Yeh

ICML Workshop 2023

PDF Code

Federated Recommendation via Hybrid Retrieval Augmented Generation

Huimin Zeng, Zhenrui Yue, Qian Jiang, Dong Wang

IEEE Big Data 2024

PDF

SLA: Stochastic Label Augmentation for Robust Vision-Language Contrastive Learning.

Qian Jiang, Jingjing Meng, Alireza Bagheri Garakani, Yang Jiao, Yetian Chen, Yikai Ni, Yan Gao, Yi Sun, Changyou Chen

Under Review

PDF

When Contrastive Learning Meets Bayesian Modeling: Learning Multi-Modal Representation Alignments with Noisy Data-Pairs

Qian Jiang, Jingjing Meng, Alireza Bagheri Garakani, Yang Jiao, Yetian Chen, Yikai Ni, Yan Gao, Yi Sun, Changyou Chen

Under Review

PDF

Multi-source transfer learning by learning to weight past tasks

Qian Jiang, Raymond A. Yeh, Minh N. Do.

Under Review

Work Experiences

  • Research Intern @ Microsoft (Fall 2023)
  • Applied Scientist Intern @ Amazon (Summer 2023)
  • Applied Scientist Intern @ Amazon (Summer Fall 2022)
  • Research Intern @ IBM Watson (Summer 2020)

Teaching Experiences

  • ECE311 - Digital Signal Processing Lab @UIUC (Spring 2022)
  • ECE310 - Digital Signal Processing @UIUC (Fall 2021)

Other Experiences

  • Cross-disciplinary Scholar in Science and Technology (CSST) @ Univesity of California, Los Angeles (UCLA) (Summer 2018)
  • Summer School of Engineering @ Israel Institute of Technology (Technion) (Summer 2017)