Yifei Zhang | Amazon Alexa AI

About Me

Welcome! I’m Yifei Zhang, an Applied Scientist at Amazon Alexa AI in Boston. I received my Ph.D. in Computer Science from Emory University in March 2026, where I was fortunate to be advised by Dr. Liang Zhao. Before Emory, I received my M.S. degree in Data Science from Columbia University, my B.E. degree in Engineering Mechanics from Dalian University of Technology, and a Master of Engineering Management degree from Duke University.

My research focuses on Trustworthy AI, AI Agents and Large Language Models, and Multimodal AI, with particular interests in reasoning, post-training, evaluation, and reliable deployment. I build methods, agents, and benchmarks that improve the capabilities, interpretability, and robustness of modern AI systems.

Research Interests

Trustworthy and Explainable AI: Interpretability, Reliability, Evaluation and Benchmarking
AI Agents and LLM Systems: Agentic Workflows, Reasoning, Memory, Post-Training
Multimodal AI: Vision-Language Models, Multimodal Reasoning, AI for Healthcare

News

[Aug. 2026] Our survey paper Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models is accepted by ACM Computing Surveys (CSUR)!
[Apr. 2026] I joined Amazon Alexa AI as a full-time Applied Scientist in Boston.
[Mar. 2026] I successfully defended my Ph.D. dissertation. Many thanks to my dissertation committee: Dr. Liang Zhao, Dr. Wei Jin, Dr. Xiaofeng Yang, and Dr. Fenglong Ma.
[May. 2025] I passed my PhD qualifying examination. Thanks to my qualifying committee, Dr. Wei Jin (committee chair), Dr. Xiaofeng Yang, and Dr. Liang Zhao.
[May. 2025] One first-author paper Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanations is accepted by KDD 2025 Datasets and Benchmarks Track!

Show earlier news

[Jan. 2025] I will rejoin Amazon Alexa AI as an Applied Scientist Intern in Boston for Summer 2025.
[May. 2024] One first-author paper is accepted by Findings of ACL 2024, and one co-authored paper is accepted by KDD 2024!
[May. 2024] I will join Amazon AGI as a Research Scientist Intern at Boston in Summer 2024!
[Apr. 2024] One first-author paper Visual Attention Prompted Prediction and Learning is accepted by IJCAI 2024!
[Jan. 2024] Our survey paper Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models is available on arXiv.
[Nov. 2023] Our work about Datasets and Benchmarks for Visual Explanation is available online!
[Jul. 2023] One first-author paper is accepted by ICCV 2023!
[May. 2023] One co-first-author paper is accepted by KDD 2023!

Selected publications and preprints

arXiv

EmoLLM: Appraisal-Grounded Cognitive-Emotional Co-Reasoning in Large Language Models

Yifei Zhang, Mingyang Li, Henry Gao, Liang Zhao

arXiv

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ICLRW'26

ATOD: An Evaluation Framework and Benchmark for Agentic Task-Oriented Dialogue Systems

Yifei Zhang, Hooshang Nayyeri, Rinat Khaziev, Emine Yilmaz, Gokhan Tur, Dilek Hakkani-Tür, Hari Thadakamalla

ICLR 2026 Workshop on Memory for LLM-Based Agentic Systems

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arXiv

Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from LDCT

Yifei Zhang, Jiashuo Zhang, Mojtaba Safari, Xiaofeng Yang, Liang Zhao

arXiv

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TechRxiv

Agentic Commerce: A Survey of How AI Agents Are Reshaping Commerce

Yifei Zhang, Bo Pan, Mengdan Zhu, Jian Pei, Liang Zhao

TechRxiv

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arXiv

Cross-modal RAG: Sub-dimensional Retrieval-Augmented Text-to-Image Generation

Mengdan Zhu, Senhao Cheng, Guangji Bai, Yifei Zhang, Liang Zhao

arXiv

PDF Code

CSUR'26

Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models

Guangji Bai, Zheng Chai, Chen Ling, Shiyu Wang, Jiaying Lu, Nan Zhang, Tingwei Shi, Ziyang Yu, Mengdan Zhu, Yifei Zhang, Xinyuan Song, Carl Yang, Yue Cheng, Liang Zhao

ACM Computing Surveys (CSUR), 2026.

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KDD'25

Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanations

Yifei Zhang, James Song, Siyi Gu, Tianxu Jiang, Bo Pan, Guangji Bai, Liang Zhao

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025 (Datasets and Benchmarks Track).

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arXiv

MEGL: Multimodal Explanation-Guided Learning

Yifei Zhang*, Tianxu Jiang*, Bo Pan, Jingyu Wang, Guangji Bai, Liang Zhao (* denotes equal contribution)

arXiv

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ACL'25

GraphNarrator: Generating Textual Explanations for Graph Neural Networks

Bo Pan, Zhen Xiong, Guanchen Wu, Zheng Zhang, Yifei Zhang, Liang Zhao

The 63rd Annual Meeting of the Association for Computational Linguistics (ACL), 2025.

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KDD'24

DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation

Qilong Zhao, Yifei Zhang, Mengdan Zhu, Siyi Gu, Yuyang Gao, Xiaofeng Yang, Liang Zhao

The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024.

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CIKM' 24

Distilling Large Language Models for Text-Attributed Graph Learning

Bo Pan, Zheng Zhang, Yifei Zhang, Yuntong Hu, Liang Zhao

The 33rd ACM International Conference on Information and Knowledge Management (CIKM), 2024.

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ACL' 24

ELAD: Explanation-Guided Large Language Models Active Distillation

Yifei Zhang, Bo Pan, Chen Ling, Yuntong Hu, Liang Zhao

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL-Findings), 2024.

PDF Poster

IJCAI' 24

Visual Attention Prompted Prediction and Learning

Yifei Zhang, Bo Pan, Siyi Gu, Guangji Bai, Meikang Qiu, Xiaofeng Yang, Liang Zhao

International Joint Conference on Artificial Intelligence (IJCAI), 2024.

PDF Page

SDM' 24

STES: A Spatiotemporal Explanation Supervision Framework

Dazhou Yu, Binbin Chen, Yun Li, Suman Dhaka, Yifei Zhang, Zhenke Liu, Minxing Zhang, Jie Zhang, Liang Zhao

SIAM International Conference on Data Mining (SDM), 2024.

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ICCV'23

MAGI: Multi-Annotated Explanation-Guided Learning

Yifei Zhang, Siyi Gu, Yuyang Gao, Bo Pan, Xiaofeng Yang, Liang Zhao

The IEEE/CVF International Conference on Computer Vision (ICCV), 2023.

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KDD'23

ESSA: Explanation Iterative Supervision via Saliency-guided Data Augmentation

Siyi Gu*, Yifei Zhang*, Yuyang Gao, Xiaofeng Yang, Liang Zhao (* denotes equal contribution)

The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023.

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Education Experience

	Emory University, 2022.08-2026.03 Ph.D. in Computer Science Advisor: Dr. Liang Zhao Dissertation: Benchmarking, Correcting, and Accelerating AI Reasoning across Diverse Modalities
	Columbia University, 2020.09-2021.12 M.S. in Data Science
	Duke University, 2019.08-2020.05 Master of Engineering Management
	Dalian University of Technology, 2015.09-2019.06 B.E. in Engineering Mechanics (Excellence Program)

Work Experience

	Amazon Alexa AI, 2026.04- Applied Scientist Boston, MA
	Amazon Alexa AI, 2025.05-2025.08 Applied Scientist Intern Boston, MA
	Amazon AGI, 2024.06-2024.09 Research Scientist Intern Boston, MA
	Guotai Junan Securities, 2018.12-2019.03 Equity Research Intern

Services

Journal Reviewer

ACM TIST, IEEE TBD, IEEE TNNLS, JACMP, Expert Systems With Applications, Information Processing and Management, Neurocomputing

Conference Reviewer / Program Committee

ICLR 2025, 2026; CVPR 2025, 2026; ICCV 2025; ECCV 2026; BMVC 2026; ACL ARR; KDD Research Track 2025, 2026; KDD Datasets and Benchmarks Track 2025, 2026; ICLR 2026 Workshop on Memory for LLM-Based Agentic Systems.

Workshop Organizer

Publicity Chair, 4th ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems (DeepSpatial 2024).
Student Co-organizer, Foundation Models for Biological Discoveries (FM4sBio) at AAAI 2025.

Awards

KDD 2023 Student Travel Award
DUTOAA Seagle Scholarship Award, DUTOAA Foundation
Academic Excellence Scholarship Award, Dalian University of Technology
Technological Innovation Scholarship Award, Dalian University of Technology