Open to robotics & AI research and internship opportunities

Hello, I'm Sahil Menon

Computer Engineering student with a passion for new and emerging technologies.

Developer Student Engineer Tinkerer
Workspace with computer and keyboard

About Me

I’m Sahil, a first-year Computer Engineering student at UNSW.

I’m fascinated by robotics and intelligent systems, particularly building autonomous machines through embedded hardware, low-level software, and AI. I’m actively seeking opportunities in robotics and AI research.

I’m also passionate about applying deep tech to help companies scale, make better decisions, and turn research into real-world impact.

UNSW, Sydney (2026 – 2029)

Experience

BuildingBloCS

Overall-In-Charge

2023 – 2025

I led Singapore’s largest student-led Computing Advocacy Program as its Overall-In-Charge, personally overseeing annual conferences that welcomed 1,000+ participants from 60 schools. I coordinated a team of organizers from 30+ institutions and secured over $200,000 in sponsorships, reaching 3,000+ students across 70+ educational institutions.

Leadership Event Planning Sponsorship

Walled AI

AI Safety Researcher

2024 – 2025

I specialised in LLM Hallucination Detection for Context-Based QA tasks. I individually designed and built an evaluation benchmark of 50,000+ samples to measure the effectiveness of detection models including Lynx and HaluBench, evaluating them across multiple precision, recall, and calibration metrics.

AI Safety LLM Evaluation Python

What I Work With

Data & ML

Python TensorFlow PyTorch NumPy Pandas Matplotlib NLTK

Systems & Software

C Java SQL OOP Computer Networks Database Systems

Web & Tooling

JavaScript HTML/CSS Streamlit Git Agile

Featured Projects

Palimps — Stochastic Text Generation

Generating coherent, infinite prose from arbitrary text corpora without a neural network.

Built an n-gram Markov Chain engine with a dynamic backoff strategy for zero-probability states, NLTK POS tagging for grammatical coherence, and binary serialization that cuts initialization time by 85%.

Python NLTK Markov Chains

Black Scholes Option Calculator

Visualising how volatility and time-decay non-linearly affect options pricing across thousands of market scenarios.

Built a real-time pricing engine using the Black-Scholes-Merton formula with vectorized NumPy/SciPy computations, 3D Matplotlib surfaces, and Seaborn heatmaps — simulating 2,500+ scenarios instantly in Streamlit.

Python Streamlit Quantitative Finance

Interested in working together or have a research opportunity?

Get In Touch

Contact Me