Hello, I'm Sahil Menon
Computer Engineering student at UNSW. Building at the intersection of embedded hardware, AI, and autonomous systems.
01.About Me
Who I am
I’m Sahil, a first-year Computer Engineering student at UNSW, building towards a future in robotics and intelligent systems.
I’m fascinated by autonomous machines — the intersection of embedded hardware, low-level software, and AI. I’m actively seeking research opportunities and internships in robotics and AI.
Outside of engineering, I’m passionate about applying deep tech to help organisations scale, make better decisions, and turn research into real-world impact.
Time
Education
BE (Computer) (Honours)
University of New South Wales, Sydney
2026 – 2029
Raffles Institution
Singapore — 96th percentile
2024 – 2025
Technical Skills
- Python & C
- Java / JS / TS
- TensorFlow / PyTorch
- NumPy / Pandas / SciPy
- Docker & CI/CD
- SQL & Data Analysis
- OpenCV & NLTK
- Git & Linux/Bash
02.Experience
BuildingBloCS
Overall-In-Charge
Led Singapore’s largest student-led Computing Advocacy Program, personally overseeing annual conferences of 1,000+ participants from 60+ schools. Coordinated organisers from 30+ institutions and secured $200,000+ in sponsorships to fund industry expert talks, hackathon judging, and national-scale mentorship programs.
Walled AI
AI Safety Researcher
Specialised in LLM hallucination detection for context-based QA. Individually designed and built a 50,000+ entry evaluation benchmark to measure detection model effectiveness — evaluating Lynx and HaluBench across precision, recall, and contextual faithfulness metrics to expose systematic accuracy gaps.
03.Featured Projects
Slide Games
Python framework (published on PyPI) that compiles arcade game logic into fully playable Google Slides via BFS state enumeration — one slide per reachable state, hyperlink-navigated.
How do you build interactive games with zero runtime, zero JavaScript, and zero server — just a URL?
1,000-state ceiling bounds exponential growth (Pac-Man scales as positions × 2ⁿ with n pellets). Token-bucket rate limiter at ≤50 API writes/min with 5 concurrent batch uploads generates ~500-state presentations in 1–3 min. Pygame-inspired 1920×1080 rendering API with 40+ colours and 3 themes; campaign system across 4 bundled games (491–600 states each).
IMC Prosperity 4 — Algorithmic Trading
Solo competitor in IMC Prosperity 4 — top 10% worldwide and top 200 in Australia out of 22,000+ global teams across 5 rounds of algorithmic and manual trading.
How do you build a market-making engine that stays profitable across changing volatility regimes on a simulated order book?
Devised a three-tier market-making engine (take/clear/make) using Welford online mean, online AR(1) on price deviations, z-score tiered sizing, and asymmetric bid/ask anchoring. Built a separate trend-following MM with hardcoded-slope discovery, online OLS blending (70/30), and full-book order imbalance microprice adjustment.
ASCII / Unicode Art Converter
Zero-dependency, fully client-side ASCII/Unicode art converter — 7 character modes, 3,163-codepoint Unicode pool, 123-emoji mosaic, image/video/webcam input.
How do you faithfully reduce a full-colour image to a grid of text characters at 30 fps without losing perceptual detail?
O(1) nearest-colour lookup via a precomputed 32³ = 32,768-entry RGB quantisation table enables 30 fps video at ~2.1 ms/frame. Implements Floyd-Steinberg, Atkinson, and Bayer dithering, Sobel edge detection, and a particle drift system. Supports 6 export formats (PNG, SVG, TXT, WebM via MediaRecorder API) and reports live render time at 1920×1080.
PixelVault
Python file-to-video codec that encodes any file into MP4 for lossless storage on YouTube — recovering original data perfectly despite H.264/VP9 re-encoding.
How do you store arbitrary binary data on a platform that lossy-compresses every video you upload?
2×2 uniform pixel blocks survive ±127 DCT luma ringing in H.264/VP9. Three-tier Reed-Solomon ECC over GF(2&sup8;) (vectorised → Berlekamp-Massey → parallel) with byte interleaving for burst-error recovery. AES-256-GCM + PBKDF2-SHA256 encryption, zlib compression, and 38× faster encoding at 0.82–2.88 MB/s via NVENC/AMF/QSV hardware acceleration with YouTube OAuth2 upload.
Interested in working together or have a research opportunity?
Get In Touch