PPPT Prospective PhD supervisors and research groups / 15 min
A cinematic research laboratory connects mathematical equations, network models, market data, agent systems, and computing infrastructure under a blue scientific light.

PHD APPLICATION PRESENTATION / V0.6

PHD

I build research systems that turn complex questions into testable evidence.

Pengyi Peng / complex systems, AI agents, quantitative research, and research engineering.

Evidence: profile-positioning-001 / education-kcl-001
01
A glass research environment shows equations, graph structures, market displays, software systems, and physical compute arranged as one connected research atlas.

01 / RESEARCH IDENTITY

My research advantage is integration across models, systems, and evidence.

I move from mathematical abstraction to reproducible software, measurable experiments, and Human-reviewed decisions.

  • Complex systems / model interactions and emergent behavior
  • AI agents / memory, tools, workflow, evaluation
  • Quantitative systems / markets, risk, data, and execution
Evidence: profile-positioning-001 / project-agent-systems-001 / project-quant-reproduction-001
02
A mathematical research studio contains network equations, differential models, probability diagrams, academic books, and a distant London skyline.

02 / EDUCATION

Mathematics and complex systems trained me to model interactions, not isolated variables.

The academic foundation combines applied mathematics with a systems view of dynamics, networks, uncertainty, and computation.

  • MSc Complex Systems Modelling / King's College London / 2022-2024
  • BSc Mathematics and Applied Mathematics / University of Reading and NUIST / 2018-2022
  • First-Class Honours / top 10% of programme / pending final CV fact review
Evidence: education-kcl-001 / education-bsc-001 / education-bsc-achievement-001
03
A large glass board visualizes interacting equations, network states, model transitions, and measurement loops in a rigorous complex-systems workspace.

03 / METHOD

I treat research as a governed loop from question to falsifiable result.

Problem -> system model -> hypothesis -> experiment -> benchmark -> review -> new question.

  • Make state, assumptions, and invariants explicit
  • Separate exploratory code from reference implementation
  • Preserve failures and reviewer feedback as learning data
Evidence: project-agent-systems-001 / project-quant-reproduction-001
04
A quantitative research desk displays factor diagnostics, order-book structure, yield curves, futures data, rankings, and experimental comparisons.

04 / QUANTITATIVE EVIDENCE

Global competitions tested my ability to convert ideas into ranked quantitative performance.

WorldQuant results provide external evidence of research iteration under common rules and competitive evaluation.

  • International Quant Championship 2024 / global 232 of 34,142 / top 0.7% / UK 5
  • MAPC 2024 / global 11 of 850 / UK 1
  • Research habit / generate, test, compare, diagnose, iterate
Evidence: competition-iqc-001 / competition-mapc-001
05
A Shenzhen financial workspace combines skyline, corporate analysis, transaction flows, risk documents, and cross-functional decision maps.

05 / PROFESSIONAL EXPERIENCE

Banking and securities work grounded my systems thinking in real institutions and constraints.

The experience exposed how data quality, process ownership, disclosure boundaries, and cross-functional coordination shape financial decisions.

  • Ping An Bank / management trainee / corporate, retail, and operations contexts
  • CITIC Securities / quantitative private-fund comparison and manager communication
  • Research implication / institutional workflows are socio-technical systems
Evidence: experience-pingan-001 / experience-citic-001
06
A modular AI agent laboratory shows planner, memory, retrieval, tool execution, evaluation, audit traces, and a clearly marked Human approval gate.

06 / AI AGENT SYSTEMS

My Agent work focuses on reliable workflows, not impressive single prompts.

State, memory, tools, evaluation, provenance, and Human approval must operate as one research system.

  • Versioned schemas and deterministic state transitions
  • Append-only traces, regression tests, and explicit failure semantics
  • Private evidence separated from public-safe artifacts
Evidence: project-agent-systems-001 / project-pweb-001
07
A software engineering lab shows code review, repository graphs, focused diffs, automated tests, issue discussion, and release infrastructure.

07 / OPEN-SOURCE RESEARCH ENGINEERING

Upstream review taught me to make small claims survive large codebases.

Merged fixes across AI retrieval, trading infrastructure, and knowledge systems required bounded changes, regression evidence, and maintainer dialogue.

  • LightRAG / token-limit and query-boundary failure handling
  • NautilusTrader / Rust test runtime without changing behavior
  • WeKnora / archived-page semantics in active statistics
Evidence: opensource-lightrag-001 / opensource-nautilus-001 / opensource-weknora-001
08
A research station compares futures signals, market microstructure, order-flow heatmaps, feature diagnostics, and backtest evaluation panels.

08 / QUANTITATIVE RESEARCH

I am converting scattered market ideas into reproducible research pipelines.

Data -> feature -> signal -> backtest -> evaluation -> risk -> implementation boundary.

  • Futures / ATR, RSI, and term-structure signals
  • Microstructure / L1 features and simplified market-making models
  • Evaluation / assumptions, tests, regimes, failure analysis
Evidence: project-quant-reproduction-001
09
A fixed-income visualization desk contains yield curves, duration and convexity geometry, DV01 exposures, spread structures, and research diagnostics.

09 / FIXED INCOME

Fixed income gives me a domain where mathematics, data, risk, and Agent research meet.

The current Rates Bond Quant laboratory connects duration, convexity, DV01, curves, and interactive explanation.

  • Rates / term structure, carry, roll-down, relative value
  • Risk / duration, convexity, DV01, scenario response
  • Next research / factor robustness and Agent-assisted evidence synthesis
Evidence: project-rates-001 / project-ficc-agenda-001
10
A full-stack engineering environment links frontend interfaces, typed APIs, databases, CI pipelines, observability, servers, and repository evidence.

10 / RESEARCH INFRASTRUCTURE

I build the interfaces and infrastructure needed to make research inspectable.

Research software matters when another person can run it, question it, and trace a conclusion back to evidence.

  • PWEB / React, TypeScript, Worker API, D1 persistence
  • PINF / software and hardware constraints as one measured system
  • Deployment / tests, CI, public-safe surfaces, versioned releases
Evidence: project-pweb-001 / project-pinf-001
11
A unified research atlas connects separate workbenches for mathematical models, AI agents, financial systems, software infrastructure, and communication artifacts.

11 / PROJECT PORTFOLIO

The portfolio is becoming a federated laboratory rather than a list of unrelated repositories.

Shared evidence, Agent workflows, benchmarks, and deployment patterns let each project improve the next one.

  • Research / agent systems, quant, FICC, papers, experiments
  • Engineering / polyglot coding, infrastructure, full stack, open source
  • Delivery / CV, presentation, interview, communication, feedback
Evidence: profile-positioning-001 / project-pweb-001 / project-rates-001 / project-pinf-001
12
A systems-design chalkboard maps data, types, states, invariants, transitions, interfaces, and module boundaries into one architecture.

12 / CODING + SYSTEM DESIGN

Across languages, I look for the same system truths.

Data -> type -> state -> invariant -> transition -> interface -> boundary -> architecture.

  • Python / fast research and Agent workflows
  • TypeScript / full-stack contracts and product interfaces
  • Rust, Go, C++, OCaml / systems, correctness, performance, and comparative design
Evidence: skills-engineering-001 / project-agent-systems-001
13
Researchers and engineers collaborate around code review, experiment records, reproducibility checks, technical documentation, and shared computing systems.

13 / WORKING WITH A RESEARCH GROUP

I can contribute as both a researcher and the engineer who makes research usable.

My strongest contribution is translating uncertain questions into experiments, systems, artifacts, and clear review surfaces.

  • Methods / quantitative modelling, backtesting, RAG and Agent evaluation
  • Engineering / Git, Linux, APIs, testing, CI, Cloudflare, technical writing
  • Communication / Mandarin, English, Cantonese; cross-functional and open-source review
Evidence: skills-engineering-001 / experience-pingan-001 / opensource-lightrag-001
14
A future research studio shows experiment rigs, papers, collaborative worktables, agent-system diagrams, financial models, and a university campus beyond the windows.

14 / PHD RESEARCH AGENDA

I want to study how Agent systems can produce reliable evidence in complex, changing domains.

The initial agenda joins Agent architecture, evaluation, domain knowledge, and real-world decision constraints.

  • Reliability / when do memory, retrieval, or tools improve decisions rather than add noise?
  • Evaluation / how do we benchmark long-horizon, multi-step research behavior?
  • Domain proving ground / finance, markets, and scientific research workflows
Evidence: research-agenda-agent-evaluation-001 / project-ficc-agenda-001
15
A collaborative PhD research space combines literature review, benchmark design, reproducible code, experiment hardware, papers, and a clear path to the next study.

15 / FIRST 90 DAYS

I want the first collaboration to end with a benchmark, a reproducible artifact, and a sharper question.

Map the literature -> reproduce a baseline -> define failure cases -> run one bounded study -> release evidence for review.

  • Choose one supervisor-aligned question
  • Fix the dataset, baseline, protocol, and evaluation contract
  • Publish code, report limitations, and decide the next experiment
Evidence: profile-positioning-001 / research-agenda-agent-evaluation-001
16