Mathematics & AI · Learning theory · Systems research

Shengye Tao

Undergraduate in Mathematics & AI at BIMSA

I am interested in learning theory, sequential decision-making, quantitative systems, digital life, and world models. I build small reproducible systems and research prototypes to turn abstract questions into testable structures.

GitHub CV Posts

Research Interests

Learning Theory & Sequential Decision-Making
Digital Life & Minimal Formal Systems
World Models & Cognitive Transformation
Quantitative Systems & Market Structure

Selected Work

Work in progress

Visual Deadline

A cognitive scheduling system for visualizing task pressure, deadlines, attention load, and long-term goals.

GitHub

Draft

MarketManifold

A market structure visualization project based on correlation, distance, MDS, PCA, and clustering.

Notes

Digital Life Minimal Model

A formal exploration of boundary maintenance, endogenous value, persistent identity, and environment coupling in digital systems.

Recent Activities

2025.11 – Now · Undergraduate Student

BIMSA Math & AI Program

Joined the Mathematics & AI undergraduate program at BIMSA, focusing on mathematical foundations, artificial intelligence, and research-oriented training.

2026.06 · Participant

Tsinghua Qiuzhen / YMSC AI Summer School

Attended lectures and discussions on generative models, diffusion models, and mathematical perspectives on modern AI.

2026.07 · Participant

Peking University Machine Learning Workshop

Participated in academic talks and discussions on machine learning, research practices, and possible future research directions.

Academic Profile

I am transitioning from a student of existing ideas to a researcher-builder who turns abstract questions into reproducible systems, notes, prototypes, and papers. See Academic for education, research interests, notes, and CV.