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aravinth-kanesh/README.md

Aravinth Kaneshalingam

Final-year Computer Science student at King's College London (Predicted First Class). I work close to the metal - systems programming, low-latency infrastructure, and understanding exactly why things break.

My interests sit at the intersection of systems and security: compilers, memory safety, and how low-level vulnerabilities actually manifest. I built a coverage-guided fuzzing framework targeting Poly/ML (the runtime at the core of Isabelle/HOL's trusted computing base), finding UBSan and memory safety bugs in ARM64-specific compiler code.

Projects

Trust Me, I am a Verifier! (Or should you?) - Fuzzing the Poly/ML Compiler · C, Standard ML, AFL++, LLVM, ASan/UBSan

  • First systematic coverage-guided fuzzing framework for Poly/ML (Isabelle/HOL's trusted computing base) on ARM64
  • Direct AFL++ binary fuzzing with LLVM LTO PCGUARD instrumentation; ASan/UBSan layered at runtime to avoid bootstrap failures; CMPLOG and rare power schedule added for Phase 2
  • 72 curated SML seeds + Isabelle/HOL corpus; two-phase lexer/parser strategy with afl-cmin minimisation and evolved corpus handoff between phases
  • Pre-campaign UBSan overflow in arm64.cpp:246; SIGSEGV crashes in module elaboration; EC2 Graviton: 2,017 edges, 28.57% libpolyml/ coverage, ~24.9 exec/sec (Phase 1)

Crux - Optimal Rubik's Cube Solver · C++17, CMake

  • Guarantees minimum-move solutions (≤20 moves, God's Number) via IDA* with pattern databases
  • 88M-state corner pattern database stored as 4-bit nibbles (~42 MB); three 6-edge partial DBs built via BFS
  • O(1) heuristic evaluation via coordinate move tables - no per-node arithmetic on the search hot path
  • Parallel search across 18 root moves with atomic abort; 12-move scrambles under 30ms, 32-test suite validates optimality

DCache - High-performance distributed in-memory cache · Go, Docker, Prometheus

  • 256-shard concurrent map achieving 50M+ ops/sec with sub-25ns GET latency; 80+ Redis-compatible commands
  • AOF persistence (buffered-channel writer, configurable fsync) + CRC-32C binary snapshots for crash recovery
  • Async master-slave replication via PSYNC with bounded ring-buffer backlog and TCP connection hijacking
  • Prometheus observability with per-command latency histograms; Docker Compose stack with Grafana

Real-Time Market Data Simulator - Low-latency market data streaming engine · Python, asyncio, NumPy

  • 1.4M+ ticks/s raw generation; 127k+ msg/s end-to-end across 10 subscribers with sub-200µs p99 latency and zero loss
  • GBM price dynamics; asyncio fan-out with per-subscriber queue isolation and backpressure handling
  • p50/p95/p99/p99.9 percentile tracking via NumPy vectorised operations, decoupled from the generation hot path
  • 53-test suite covering GBM statistical properties, backpressure behaviour, and high-load end-to-end scenarios

Experience

Software Engineer Intern - The Kusp Hub (June - September 2025)

  • Architected AI-powered career discovery platform with 97%+ CV skill extraction accuracy using LLM structured outputs (Groq API) and coordinate-based multi-column PDF reconstruction with PyMuPDF
  • Engineered two-stage semantic job matcher combining sentence-transformer bi-encoder retrieval with category-weighted reranking; reduced pipeline latency by 95% (75s → 2s) through offline vector pre-computation
  • Built full-stack Flask application with async processing, confidence scoring and semantic match explanations; deployed to production on Render with Gunicorn
  • Developed work credits generator using custom LLM prompts to categorise and surface candidates' creative industry experience as a portfolio PDF

Technical Skills

Languages: Python, C/C++, Java, Go, Scala, JavaScript, SQL, Standard ML

Systems & Tools: Docker, Bash, AFL++, LLVM, ASan/UBSan, asyncio, NumPy, Redis, Prometheus, CMake

Frameworks: Flask, Django, React

Contact

LinkedIn · Email

Pinned Loading

  1. distributed-cache distributed-cache Public

    High-performance distributed cache in Go - 50M+ ops/sec, sub-25ns GET latency, Redis-compatible

    Go

  2. crux crux Public

    Optimal Rubik's cube solver in C++ - guaranteed minimum-move solutions via IDA* with pattern databases, 12-move scrambles under 30ms

    C++

  3. market-data-simulator market-data-simulator Public

    High-performance market data simulator - 1.4M+ ticks/s, sub-200µs p99 latency, 10 concurrent subscribers

    Python

  4. my-watch-stats my-watch-stats Public

    Visualise your Letterboxd or IMDb watch history. Upload a CSV, get charts. Runs entirely in the browser

    JavaScript