Jingfeng Zhang

Trustworthy Machine Learning · Safe Embodied AI

Building Responsible Intelligence for Safe Embodied AI

Jingfeng Zhang · Associate Professor & PhD Supervisor
Fudan University

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Google Scholar ↗ ORCID ↗ GitHub ↗

College of Intelligent Robotics and Advanced Manufacturing · Institute of Trustworthy Embodied AI

Portrait of Jingfeng Zhang
Based in Shanghai · working globally
30+ Papers at leading AI venues ICML, NeurIPS, ICLR and IEEE TPAMI Research Distinctions Oral · Spotlight · Best Paper ICLR 2021 · NeurIPS 2023 · AAAI 2025 RIKEN Ohbu Award Research recognition RIKEN AIP, FY2021 8 countries Global experiences China · U.K. · U.S.A. · Singapore · Japan · New Zealand · Saudi Arabia · U.A.E.

Research with the TrustAI mandate

Trustworthy AI, from learning theories to embodied intelligence.

My research asks a practical question: how can an intelligent system stay dependable when its data are imperfect, its environment shifts, or an adversary actively tries to break it? I develop learning principles, evaluations, and defenses that make failure visible—and safety actionable.

I am an Associate Professor and PhD Supervisor at Fudan University. My primary career path includes a permanent lectureship at the University of Auckland and research appointments at RIKEN AIP, following a bachelor's degree from Shandong University and a PhD from the National University of Singapore. Complementary international experience includes research roles with NYU Abu Dhabi and KAUST, internships at IBM Research, Singapore and RIKEN AIP, part-time teaching at the University of Tokyo and Ochanomizu University, and undergraduate study at UCLA and the University of Cambridge.

Formal appointments & education

2026-CurrentAssociate Professor & PhD SupervisorFudan University 2023—2026Lecturer (permanent) & PhD SupervisorUniversity of Auckland 2021—2023Postdoctoral Researcher → Research ScientistRIKEN AIP 2016—2020PhD in Computer ScienceNational University of Singapore 2012—2016BSc in Computer Science & TechnologyTaishan College, Shandong University

Exchange, visiting, internships & part-time roles

2023-CurrentVisiting ScientistRIKEN AIP 2022—2026Guest Lecturer (part-time)The University of Tokyo 2026Guest Lecturer (part-time)Ochanomizu University 2024-2026Research AssociateCenter for Artificial Intelligence and Robotics (CAIR), New York University Abu Dhabi 2024-2026Visiting ProfessorPRADA Lab, King Abdullah University of Science and Technology (KAUST) 2019Research InternRIKEN Center for Advanced Intelligence Project (RIKEN AIP), Tokyo, Japan 2018Research InternIBM Research, Singapore 2015Undergraduate ExchangeUniversity of California, Los Angeles (UCLA) 2015Summer SchoolUniversity of Cambridge

Showing 5 of 9 entries · scroll within the list

Awards, funding & academic leadership

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Industry research awardGoogle Research Award

Research award from Google supporting academic research, 2026

 Research recognitionRIKEN Ohbu Award

Researcher Incentive Award, FY2021

 International recognitionKAUST Rising Stars in AI

Selected speaker · “Adversarial Learning: Foundations and Applications,” 2023

 International fellowshipDAAD AInet Fellow

Selected for the April 2021 AI and Data Science cohort, representing RIKEN AIP

 International collaborationRoyal Society Te Apārangi

Catalyst: Seeding project on robust and private multimodal learning

 Competitive fundingJST ACT-X

Project Lead · Adversarial-training robustness · 2021–2023 · JPMJAX21AF

 Competitive fundingJSPS KAKENHI

Principal Investigator · Robust learning with imperfect data · 2022–2024 · 22K17955

 Academic community buildingFounder, TrustML YSS

Founded at RIKEN AIP in 2022 · Young Scientist Seminars in trustworthy machine learning

 Conference leadershipAJCAI 2026 Program Chair

Australasian Joint Conference on Artificial Intelligence · Auckland, New Zealand

 Conference leadershipIEEE CAI 2024 Workshop Co-Chair

IEEE Conference on Artificial Intelligence · Singapore

 Workshop leadershipAIGC 2023 Workshop Chair

Foundation Models & Fine-tuning Strategies · Shanghai, China

 Conference leadershipSOICT 2023 Track Chair

AI Foundations & Big Data · Ho Chi Minh City, Vietnam

 Conference leadershipIEEE MIPR 2025 Publicity Chair

Multimedia Information Processing & Retrieval · San Jose, USA

 Workshop leadershipACML WSL Program Co-Chair

Weakly Supervised Learning workshops · ACML 2021 & 2022

 Academic leadershipEditor & Area Chair

IEEE TAI / Neural Networks · ICML / NeurIPS / ICLR

 Academic volumeAdvances in Artificial Intelligence

Co-edited academic volume published by Springer Nature, 2026

Editorial Associate Editor, IEEE Transactions on Artificial Intelligence and Neural Networks

Conferences Program Chair, AJCAI 2026 · Workshop Co-Chair, IEEE CAI 2024 · Workshop Chair, AIGC 2023 · Track Chair, SOICT 2023 · Publicity Chair, IEEE MIPR 2025 · Program Co-Chair, ACML WSL 2021–2022 · Area Chair, ICML, NeurIPS and ICLR · Senior Program Committee, AAAI

Research agenda

Four layers of trustworthy intelligence.

The programme moves from learning theory to data governance, model and agent security, and finally trustworthy behaviour in the physical world.

01

Learning Theory: Trustworthy AI Foundations

What makes learning reliable under adversarial data, supervision, and evaluation?

Core algorithms and theory for trustworthy machine learning, from adversarial training and robust generalisation to representation alignment.

Adversarial learningRobust generalisationRepresentation theoryLearning dynamics
Explore programme 02

AI Governance: Data Safety & Content Integrity

How do we keep data-driven systems reliable when data can be poisoned, biased, noisy, or private?

Security and governance at the data layer: poisoning and backdoors, algorithmic bias, privacy leakage, and defensible data use.

Data poisoningAlgorithmic fairnessPrivacy protectionData governance
Explore programme 03

Model Security: Local LLMs & Trustworthy Agents

How can locally deployed models and agents be evaluated, aligned, and controlled before they act?

Safety for LLMs and agentic systems, spanning local and private deployment, jailbreak robustness, alignment, and repeatable evaluation harnesses.

Local deploymentLLM alignmentAgent safetyEvaluation harnesses
Explore programme 04

Embodied Intelligence: Trustworthy Multimodal Robotics

How can intelligent agents remain safe when perception becomes physical action?

Trust mechanisms for vision-language-action models and embodied agents, including inference-time safeguards, adversarial testing, and physical-world backdoors.

Embodied AIVLA safetyPhysical backdoorsInference-time control
Explore programme

Open to strong prospective students

Prospective students & collaborators

Join RISE-AI and work on problems where reliability truly matters.

We welcome prospective master's students, direct-entry PhD students, research interns, and visiting collaborators who want to build rigorous and useful trustworthy AI.

“Excellent research grows from sustained effort, not last-minute sprints; from teamwork, not individual heroics.”— Jingfeng Zhang

Master's & direct-entry PhD

For students seeking deep training in trustworthy machine learning, embodied intelligence, or their intersection.

Research interns

For students ready to commit to a well-scoped research problem and a reproducible implementation.

Visiting researchers & collaborators

For complementary expertise in robotics, multimodal learning, AI safety, or application domains.

Who thrives here

  • You are curious about why a model fails—not only how to improve a score.
  • You can work independently, communicate clearly, and contribute to a team.
  • You value careful experiments, readable code, and honest negative results.
  • You are ready for long-horizon work with high standards and humane mentoring.

How to start

  1. Read the project themes and two or three recent papers that genuinely interest you.
  2. Prepare a concise CV, transcript, and links to code, papers, or substantial course projects.
  3. Email a short note explaining the problem you want to study, why it matters, and why RISE-AI is a fit.
Email subject[RISE-AI Application] Name — programme — intended year
Email your application

RISE-AI Lab

A rigorous, open, and collaborative lab.

RISE-AI combines high standards with academic freedom and warm, direct mentorship. We value rigorous reasoning, reproducible systems, intellectual generosity, and research that holds up beyond the benchmark.

Portrait of Jingfeng Zhang
Principal Investigator

Jingfeng Zhang

Trustworthy machine learning · safe embodied AI

Prospective member

Your work could begin here.

Tell us what difficult reliability problem you want to solve.

Start a conversation →

Selected publications

Research, organised by year.

This is a curated record. For the complete and most current list, visit Google Scholar ↗.

Talks & lectures

Ideas shared across institutions and communities.

Selected invited talks, lectures, and conference presentations on reliable and adversarial learning.

View the complete 46-item archive

Teaching & mentoring

Teaching that connects foundations to current systems.

Machine learning, generative modelling, trustworthy AI, and research mentoring across institutions in China, Japan, and New Zealand.

Current

Fudan University

Graduate Research Mentoring

Research supervision in trustworthy machine learning and safe embodied AI, grounded in rigorous process, academic freedom, and team collaboration.

Join RISE-AI

Students · collaborators · partners

Let’s make intelligent systems worthy of trust.

zjf@fudan.edu.cn

Room 508, Building D2, Bay Valley, Yangpu District, Shanghai, China

Scholar ↗ ORCID ↗ GitHub ↗