Projects

Fine-tuned · 14 classes · Apple Silicon

Mental Health AI

Local symptom classifier and chatbot fine-tuned on Qwen using Apple Silicon. Served via FastAPI.

14-class symptom classifier (anxiety, low mood, stress/burnout, and more) with a confidence score per session
Runs entirely locally on Apple Silicon through a fine-tuned Qwen2.5 model — no user data ever leaves the device
Generates a risk-level assessment and recommended next steps at the end of each triage conversation

PythonMLX-LMLoRAQwen2.5FastAPIOllama

10x compression · Fine-tuned local models

Semantic memory engine for local LLMs using Symbolic Prompt Language (SPL) for 10x prompt compression.

Converts long conversations into Symbolic Prompt Language (SPL), a compressed representation the model reads directly
Achieves roughly 10x prompt compression compared to passing raw conversation history
Qwen2.5 fine-tuned via QLoRA to reason from compressed SPL tokens without decompressing back to words, served locally through Ollama

PythonOllamaQwen2.5LoRAChromaDBSQLitespaCy

R²=0.9501 · Built from scratch

Neural network built from scratch in NumPy to forecast Delhi PM2.5 levels using six years of data.

Neural network written entirely from scratch in NumPy — no ML frameworks — including a custom backprop implementation
Trained on six years of real CPCB sensor data, reaching R² = 0.9501, ahead of a Random Forest baseline
Final-year project pairing classical feature engineering with the from-scratch model

PythonNumPyPandasScikit-learnCPCB Data

Sub-millisecond Redis Matching

High-performance Redis-backed matching and pricing engine for shared ride pooling and solo assignments.

Real-time dispatch map matches riders on overlapping routes using Redis geospatial lookups at ~34ms latency
Scores each potential pool match and estimates solo vs. shared fare before confirming a route
Live driver availability, route detour %, and a full pricing breakdown surfaced to dispatchers

PythonRedisGeoSpatial