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wlu03

repo of notes

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Introduction#

I am an undergrad student studying Mathematics and Computer Science at Georgia Tech. This site serves as my curated collection of notes, research, and project documentation.

View on GitHub · School: wesleylu@gatech.edu · Personal: wesleylu03@gmail.com · LinkedIn

Map of Content#

Mathematics#

Foundational math course notes and proof techniques.

  • Linear Algebra — vector spaces, decompositions, eigentheory
  • Probability Theory — distributions, MGFs, limit theorems
  • Applied Combinatorics — counting, generating functions
  • Foundation to Proof — proof techniques
  • Putnam Prep — competition problem solving + proof techniques

Computer Science — Theory#

  • CS 3510 · Algorithms — Big-O, DP, graphs, NP-completeness
  • CS 4510 · Automata & Complexity — DFA/NFA, CFG, decidability

Computer Science — Systems#

  • CS 2200 · Systems & Networks — ISA, pipelines, memory, processes
  • CS 3210 · OS Design — xv6, processes, scheduling
  • CS 4290 · Advanced Computer Architecture — caches, prefetching, ILP
  • CS 4240 · Compilers — IR, dataflow, register allocation, parsing

Computer Science — ML / AI#

  • CS 4644 · Deep Learning — regression, CNNs, training
  • CS 4641 · Machine Learning — classical ML
  • CS 4476 · Computer Vision — filters, features, CNNs for vision
  • CS 3630 · Perception & Robotics — see Georgia Tech/CS 3630/ (PDF-only lecture slides)

Research#

  • HPC Research — high-performance computing project notes
  • OM-LEU · LLM × Human Behavior — NeurIPS submission: outcome-mediated utility learning for discrete choice
  • Directed Reading Program — graph theory (f25), generating functions (s26)
  • VIP · Research Group — SA-NAS literature & working notes
  • VIP · Kaggle Titanic — see VIP/Kaggle Titanic Competition/ for notebooks and the EMADE submission

Quant / Markets#

  • Option Theory — pricing, Greeks, implied volatility
    • IV solvers: Newton-Raphson Method, Bisection Method, Brent's Method
  • Prediction Markets — Kalshi, derivatives-implied probability
  • Quant Interview Prep — distributions, brainteasers, OAs

Practice & Implementation#

  • Competitive Programming — DSU, MST, SCC, DP, graphs
  • FPGA / Clash — Haskell hardware description
  • AI Agents — LangChain, MCP, agent architectures
  • Books — see Books/ for PDFs (Axler, Dummit, Rudin, Stein, Alon-Spencer, generatingfunctionology)

Writing#

  • Blog — write-ups: Markov / Hybrid prefetcher, custom scheduling, take-homes

Cross-Domain Threads#

How the domains connect:

  • Linear Algebra is the backbone of Deep Learning, Computer Vision, Advanced Computer Architecture (matrix-multiply benchmarks), and VIP / SA-NAS (GNN math).
  • Probability Theory feeds Option Theory, Prediction Markets, Interview Prep, and Deep Learning (loss/regularization).
  • Automata & Complexity is the formal-language partner of Compilers (lexing/parsing) and shares NP-completeness machinery with CS 3510.
  • CS 3510 (Algorithms) is the theory side of CP (Competitive Programming).
  • CS 2200 / Adv Comp Arch / OS Design form the systems stack; FPGA sits adjacent at the hardware layer.
  • AI Agents and OM-LEU share LLM tooling and prompt-engineering patterns.
  • Blog posts grow out of project work in Advanced Computer Architecture (Markov / Hybrid Prefetcher) and other domains.

Top-Level Files#

  • Methodology — workflow notes
  • Checklist — recurring task tracker
  • Custom Scheduling Algorithm Report — project write-up

Notes are linked extensively; use the graph view or local search to traverse by topic.