Vishwa Patel

AI/ML & Computer Vision Research Engineer

Vadodara, Gujarat, India

@vishwa_patel

0 followers

🎓 Bachelor of Technology in Computer Engineering at CVM University · Graduating 2027

About

Computer Engineering student specializing in AI/ML and computer vision, with experience building machine learning and deep learning projects. Interested in research-driven AI applications, with strengths in analysis, problem solving, and leadership.

Experience

Machine Learning Intern

BrainyBeams · Ahmedabad, India

Jun 2026 – Jun 2026

Applied machine learning concepts to practical projects involving data preprocessing, model development, evaluation, and deployment; strengthened hands-on experience with Python and ML workflows.
Artificial Intelligence Intern

Acmegrade

Jan 2023 – May 2024

Gained hands-on experience in AI and deep learning, including CNN-based modeling, image classification, model development, and evaluation.
Student Designer

BRDS

Jan 2022 – May 2023

Created visual content and collaborated on diverse design projects, combining creativity, communication, and problem-solving to deliver impactful outcomes.

Education

2027

Bachelor of Technology in Computer Engineering

G H Patel College of Engineering & Technology

Awarded by CVM University

Computer Engineering · Aug 2023 – Jun 2027

Computer Engineering · Aug 2023 – Jun 2027

Skills

1 to 2 years

Deep Learning Model Evaluation Image Classification Convolutional Neural Networks

<3 months

NumPy C/C++ Python Pandas OpenCV PyTorch Matplotlib Fine-tuning scikit-learn Optimization Machine Learning Image Processing Object Detection Transfer Learning Data Augmentation Feature Engineering Statistical Analysis Research Methodology Exploratory Data Analysis

Tools / apps / platforms

<3 months

Git GitHub Google Colab Jupyter Notebook

Projects

Computer Vision & Deep Learning Laboratory

OpenCV, NumPy, Matplotlib, PyTorch, CUDA, ResNet18, EfficientNet

Developed an image-processing pipeline for enhancement, denoising, histogram equalization, CLAHE, transformations, contour/shape detection, and edge-detection benchmarking across Sobel, Prewitt, Canny, and Laplacian operators. Built and evaluated MLP/CNN classifiers on CIFAR-10 and performed GPU-accelerated transfer learning/fine-tuning with ResNet18 and EfficientNet for Cats vs Dogs classification.
Credit Card Fraud Detection

Scikit-learn, Pandas, NumPy

Developed a fraud detection pipeline using PCA, Decision Tree, Random Forest and Gradient Boosting, evaluating performance through ROC-AUC and other classification metrics.
Forest Fire Detection Using CNN

Developed a CNN-based image classification system for forest-fire detection, applying image preprocessing, augmentation, model training, validation, and performance evaluation to classify fire-related imagery.
Orbitguard: AI-Assisted Orbital Traffic Management & Space Debris Intelligence

Python, Streamlit, CelesTrak, SGP4, Machine Learning, 3D Visualization, Optimization

Built an orbital-intelligence platform integrating CelesTrak data, SGP4 propagation, conjunction detection, machine learning, and multi-objective optimization to assess close-approach risks and rank collision-avoidance maneuvers. Developed interactive 3D Earth/orbital visualization, threat dashboards, maneuver simulation, space-weather integration, and offline orbital-crisis demonstrations for spacecraft traffic and debris intelligence.
Asteroid Impact Risk Simulation & Mitigation

Python, NumPy, Pandas, SciPy (odeint), Streamlit, Plotly, Folium, Requests, JSON

NASA Space Apps Challenge 2025 Global Nominee. Led a team in developing an interactive asteroid-risk simulation and mitigation platform; conducted research and analysis, and is extending the work toward a research paper.

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