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Master

University of Cambridge

AI Ethics and Society

• Machine Learning Alignment
• AI Ethics
• AI Governance
• CS230 Deep Learning
• ML Safety
• Discrete Mathematics
• CS234 Reinforcement Learning
• CS109 Probability for Computer Scientists
• Mechanistic Interpretability
• Algorithms and Data Structure

Summer Student

Stanford University

Cognitive Science & Philosophy

• Mathematics Foundation of Computing
• Minds and Machines
• Introduction to Neuroscience

Bachelor of Arts

Shantou University

English & Global Studies

• Machine Learning and relevant maths
• Research Methodology
• Linguistics

Algorithms that Assist Truth Seeking and Prevent Lock-in

We propose an alternative to RLHF that uses " helping humans to seek truth as training objective " and human opinion change data as ground truth. By doing so we aim to remove feedback loop-incurred lock-in from its root and revolves alignment evaluation around " LLM-assited human performanceWe ".

• Position: Project Co-founder
• Collaborators: Prof Andreea Bobu (MIT), Tianyi (Alex) Qiu (CHAI, Berkley & PKU

Position: AI Systematically Rewires the Flow of Ideas

We propose "AI influence " as a field of studies on AI's impact on epistemics and morality. Early algorithmic interventions and data curation practices will remove mechanisms (such as dual influence) leading to systematic negative impact on human knowledge and values.

• Position: Project Co-founder
• Collaborators: Prof Max Kleiman-Weiner (UW), Tianyi (Alex) Qiu (CHAI, Berkley & PKU, Prof Atoosa Kasirzadeh, Prof John P Wihbey, Dr Moshe Glickman, Tao Lin.

The Lock-in Hypothesis: Stagnation by Algorithms

We are concerned with the problems of LLM-incured value lock-in and knowledge collapse (as probable as model collapse since increasingly our discourses are mediated by AI systems and iterative training becomes more prevalence), with the consequence being more destructive. Our team is working on toy model demonstration of the mechanisms of LLM-incurred value lock-in, as long as establishing real-world evidence of lock-in from human-AI interactions.

• Position: Project Co-founder
• Collaborators: Prof Max Kleiman-Weiner (UW), Tianyi (Alex) Qiu (CHAI, Berkley & PKU

Multilevel analytical framework for interpretability

Research on cognitive science and neuroscience to address interpretability challenges in ML.

• Position: Project Lead
• Goal: Publication in Transactional Machine Learning Research
• Senior authors: Grace W. Lindsay(NYU), Prof Anna Ivanova (GeorgiaTech)

Comprehensive Survey on AI Alignment

Survey paper on alignment research for newcomers.

• Focus: Interpretability challenges in ML
• Collaborators: Yaodong Yang, Jiaming Ji, Tianyi Qiu

Harms from agentic algorithmic systems

Research on safety and harms from agentic systems in AI.

• Highlight: Published paper cited by GPT-4 and high-profile AI safety reports

Stanford Existential Risks Initiative (SERI)

Research on China's AI governance approach.

• Position: Research Fellow

Open Philanthropy Graduate Scholarship

Open Philanthropy

A full scholarship for the master of study in AI Ethics and Society at the University of Cambridge.

Manifund AI Research Scholarship

Manifund

A research scholarship to support work on neuroscience and mechanistic interpretability.

Hong Kong Cyberport Creative Micro Funding

Hong Kong Cyberport Management Company Limited, wholly owned by the Hong Kong SAR Government

Early-stage startup funding to support work on Homeal, a sharing economy app about food and culture.