Hi, I’m Brice — an engineer and researcher-in-training working at the intersection of Artificial Intelligence, Mathematics, and systems-level engineering.
This wiki is the public layer of that work: a growing graph of course notes, topic hubs, and implementation-heavy essays.
I use this site as a long-term research atlas. It is where concepts, proofs, systems ideas, experiments, and implementation notes are allowed to connect rather than remain isolated.
Navigate The Atlas
- Notes if you want the topic-map version of the site
- Blog if you want polished essays and write-ups
- Master of AI if you want the course-connected path
- Transformers, Mathematical Foundations, and AI Systems if you want the three strongest conceptual routes
My core interest lies in generative and multimodal AI systems, with a particular focus on how representation learning, optimization dynamics, and system constraints interact in large-scale models.
At a high level, I care about one broad question:
How do we design intelligent systems that are mathematically grounded, architecturally expressive, and efficient under real-world constraints?
Profile
- Name: Brice
- Base: Auckland, New Zealand
- Current program: Master of Artificial Intelligence, University of Auckland
- Primary direction: Generative AI, multimodal systems, probability, and AI systems engineering
- Site role: a public wiki for research notes, technical essays, and evolving conceptual maps
Currently, I am especially focused on:
- Video generation — temporal coherence, motion modeling, and scalable generative architectures for video synthesis
- Probability theory & stochastic processes — building rigorous mathematical foundations for understanding generative models, diffusion processes, and uncertainty in AI systems
More broadly, I am drawn to foundational and system-aware questions, such as:
- How architectural choices influence representation and generalization
- How optimization dynamics affect stability and efficiency
- How system constraints (memory, parallelism, latency) shape model design and inference
I am especially interested in research spaces where these threads overlap:
- multimodal generative systems with strong representation learning structure
- mathematically principled views of uncertainty and dynamics
- system-level trade-offs in training and inference
- geometric and computational ways of understanding model behavior
Starting February 2026, I will pursue a Master of Artificial Intelligence at the
University of Auckland (New Zealand), with the long-term goal of advancing toward a
research-oriented path (Research Assistantship / PhD) in generative and multimodal AI systems.
🎓 Education
B.Eng. in Electronic Information Engineering
Harbin University of Science and Technology · Aug 2021 – Jul 2025
My undergraduate training emphasized a dual foundation in systems and mathematics, including:
- Computer systems and embedded architectures
- Mathematical modeling and numerical optimization
- High-performance and parallel computing
- Compiler theory and low-level program optimization
GPA: Top 1% of cohort
Master of Artificial Intelligence (180 points)
University of Auckland · Feb 2026 – Dec 2027 (expected)
Planned research focus:
Generative AI × Multimodal Modeling × System Optimization
I am particularly interested in:
- Transformer-based, diffusion-style, and hybrid generative architectures
- Multimodal representation learning and cross-modal alignment
- Training and inference efficiency under system constraints
- Mathematical perspectives on optimization, stability, and generalization
with the intention of continuing toward research assistantship and doctoral-level study.
💼 Industry Experience
System Technology Intern — Tencent Cloud
CSIG Division · Xingxinghai Lab · Jul 2024 – Sep 2024
Worked on production-grade cloud infrastructure with an emphasis on
automation, reliability, and performance-aware system design.
Key contributions included:
- Automating cloud server deployment pipelines for stable production environments
- Improving internal system workflows to enhance efficiency and reliability
- Collecting and analyzing operational metrics to support data-driven optimization
- Authoring technical analysis reports emphasizing correctness and traceability
- Participating in discussions around distributed system reliability and
kernel-adjacent performance tuning
This experience reinforced my interest in large-scale AI systems, where
model design and system constraints must be considered jointly.
🧠 Research & Technical Interests
-
Video Generation
Temporal modeling, motion-aware architectures, diffusion-based video synthesis, scalable generation pipelines -
Generative & Multimodal AI
Representation learning, cross-modal alignment, generative modeling, inference efficiency -
Probability Theory & Stochastic Processes
Measure-theoretic probability, Markov chains, SDEs, probabilistic graphical models — as rigorous foundations for generative AI -
Mathematics for AI
Optimization, numerical methods, information theory -
Systems & Rust Engineering
Async runtimes, compilers, concurrency models, performance-aware design -
Algorithms & Data Structures
Graphs, optimization algorithms, compiler IRs, complexity-aware implementations
🌲 Skill Tree
🧩 Programming Languages
C · C++ · Rust · Go · Python · Java · JavaScript · Swift
Languages are treated as tools, with emphasis placed on
abstraction, correctness, and performance trade-offs.
🔬 Research & Technical Projects
(Ongoing and planned research-oriented work)
| Project | Description | Keywords |
|---|---|---|
| LeRobot Reproduction & Extension | Reimplemented and analyzed an open-source robotic control framework during undergraduate research, focusing on system understanding and control abstractions. | Robotics · Control · Learning |
| Video Generation (Active) | Studying diffusion-based and autoregressive architectures for video synthesis — temporal coherence, motion modeling, and scalable generation pipelines. | Video Generation · Diffusion · Temporal Modeling |
| Probability Theory Study (Active) | Systematic study of measure-theoretic probability and stochastic processes as mathematical foundations for generative models and diffusion-based AI. | Probability · Stochastic Processes · Math |
| Generative Multimodal Modeling (Planned) | Exploring generative architectures for cross-modal representation and alignment, with attention to training dynamics and inference efficiency. | Generative AI · Multimodal |
| AI Systems Optimization (Planned) | Studying system-level trade-offs in training and inference pipelines for large generative models. | AI Systems · Performance |
🌐 Contact & Presence
- GitHub → https://github.com/BriceLucifer
- X (Twitter) → https://x.com/Bricelucifer
- Email → 2376671337@qq.com
This site is built with **Quartz** and shaped as a geometric knowledge atlas for AI systems, mathematics, and computational research.