Aryan Rastogi

Machine Learning & AI Engineer

Jaipur, India

About Me

I train models to learn & they teach me patience.

I am a computer science student working on machine learning, computer vision, NLP, and applied intelligent systems. I craft effective prompts, build clean frontends, and work comfortably across backend systems.

Off screen, I like to slow down, eat well, and catch up on sleep.

Tech Stack

Languages

PythonCppSQLTypescript

ML/AI Frameworks

PyTorchTensorFlowScikit-learnHugging FaceLangChainOpenCV

Full Stack

ReactNext.jsNode.jsREST APIsSupabase

Tools & Platforms

FastAPIAWSGCPDockerKaggle

GitHub Activity

Loading GitHub activity...

Work Experience

AI Engineer Intern

Elumina Street Solutions • Jaipur

Developed a violence detection model using CNN (MobileNet) and LSTM in TensorFlow for video-based behavior recognition; improved accuracy with focal loss and class weighting. Implemented and evaluated a real-time weapon detection system using YOLOv8 and OpenCV. Collaborated with the AI research team to analyze model performance, optimize hyperparameters, and enhance detection.

April - July 2025

Research Intern

AI²: computAtional linguIstics & vIsion Lab • MUJ

Conducting an ablation study on LLM creativity, evaluating the impact of fine-tuning methods (SFT, LoRA, QLoRA, RLHF, DPO) on creative performance using DAT and AUT benchmarks.

Jan 2025 - Present

Featured Projects

Roadeye

An AI powered road audit system that uses deep learning and computer vision to analyze highway videos for detecting road defects and assets. The system automatically generates structured insights and produces professional road audit reports for cost effective infrastructure assessment.

PythonPyTorchOpenCVFastAPI

Finalist @ National Road Safety Hackathon 2025, IIT Madras

Prompt2Plugin

An AI powered Chrome extension and automation workflow generator that transforms natural language prompts into fully functional Chrome extensions and n8n automation pipelines. The system streamlines automation creation by converting ideas into deployable workflows with minimal manual effort.

ReactTypescriptn8nChrome API

Project @ CodeClash 2025

Uniway

Developed UniWay, a campus navigation platform for MUJ featuring interactive maps, smart search, route guidance, and student services. The system is designed to support future ready extensions such as AI chatbot assistance, crowd heatmaps, volunteer support, public APIs, and 360 degree campus street view.

ReactOSRMGoogle JS APIsLeaflet

Winner @ Uniway Hackathon 2025

Geoverse

An AI powered conversational information retrieval system that transforms complex satellite data from the MOSDAC web portal into instant, contextual answers. It leverages NLP, knowledge graphs, and semantic search to enable efficient and intuitive access to dynamic geospatial content.

PythonNLPKnowledge GraphsFastAPI

Project @ BAH ISRO, 2025

Achievements & Recognition

National Road Safety Hackathon Finalist

National Road Safety Hackathon 2025, IIT Madras

Shortlisted among the top 10 finalist teams nationwide from 3.6k+ participants at the National Road Safety Hackathon organised by COERs, IIT Madras.

UniWay Hackathon Winner

Uniway MUJ Hackathon 2025

Secured 1st place, won a cash prize, and earned an opportunity to collaborate on a project with Southern Federal University, Russia.

Student Excellence Awardee

Manipal University Jaipur, CSE dept 2025

Awarded for outstanding technical performance, hackathon achievements, and project based innovation during the 3rd semester.

Solana Bounty Winner

Superteam India, 2025

Won a $60 bounty and became a Global Superteam Contributor within the Solana ecosystem.

Dean's List Awardee

Manipal University Jaipur, CSE dept 2025

Named to the Dean's List in the 2nd semester for academic excellence.

#press
#shift+enter
#to execute cell

import time
import sys
import random

WIDTH = 60
HEIGHT = 20

def clear():
    sys.stdout.write("[2J[H")

# Generate data (y = 0.5x + noise), first quadrant only
data = [(x, 0.5*x + random.uniform(-1, 1)) for x in range(1, 21)]

# Initial line y = mx + b
m = random.uniform(0, 1)
b = random.uniform(0, 2)
lr = 0.01

def predict(x):
    return m*x + b

for step in range(5):
    clear()
    print('Linear Regression Learning\n')
    print(f'Step {step+1}/5 | slope={m:.2f} intercept={b:.2f}\n')

for y in range(HEIGHT, -1, -1):
        row = ""
        for x in range(WIDTH + 1):
            px = x / 3
            py = y / 2

char = " "

# Axes (first quadrant)
            if x == 0:
                char = "|"
            if y == 0:
                char = "-"
            if x == 0 and y == 0:
                char = "+"

# Data points
            for dx, dy in data:
                if abs(px - dx) < 0.3 and abs(py - dy) < 0.3:
                    char = "●"

# Regression line
            if abs(py - predict(px)) < 0.3:
                char = "="

row += char
        print(row)

# Gradient descent update
    dm = sum((predict(x) - y)*x for x, y in data) / len(data)
    db = sum(predict(x) - y for x, y in data) / len(data)

m -= lr * dm
    b -= lr * db

time.sleep(0.3)

print("\n...where learning begins!")

Starting Python environment...