Mayank Jain
Greater Bengaluru Area India
Summary
👤
Senior🎓
Top SchoolMayank Jain is a pre-final year B.Tech CSE student at IIIT Jabalpur with a strong focus on building scalable software and applied AI/ML. He specializes in C++, data structures & algorithms, MERN full-stack development, and has solved 800+ problems across LeetCode, Codeforces, and GFG, reflecting rigorous problem-solving discipline. Mayank has practical experience from hackathons and internships where he delivered full-stack features and integrated machine learning solutions for real-world use cases. Based in Uttar Pradesh, India, he brings eight years of hands-on coding exposure despite being early in his formal degree timeline, demonstrating sustained commitment to craft. Colleagues describe him succinctly as someone who "writes code"—but with the depth to translate algorithms into production-ready services. He is eager to collaborate on software development and AI/ML projects that bridge research and application.
9 years of coding experience
Bachelor of Technology - BTech, Computer Science, Bachelor of Technology - BTech, Computer Science at Indian Institute of Information Technology, Design and Manufacturing, JabalpurSt Dominic Savio schoolALLEN Career institute
Github Skills (50)
angular-app8
algorithm8
spell7
java7
transfer-learning6
redirect6
charm6
convolutional-neural-networks6
visual-recognition5
3d5
css5
python5
javascript5
openstreetmap5
algorithms4
Programming languages (11)
Github contributions (5)
arpitjainds/Information-Extraction-from-Scientific-Data-Charts
Apr 2018 - Nov 2018
This repository is an Image-Processing application for the automatic extraction of Data-Points from Scientific Data Charts.
Contributions:17 commits, 16 pushes, 1 branch in 7 months
image-processing
arpitjainds/Chart-Image-Classification
May 2018 - Nov 2018
An image classification program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception. This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images. This repository classifies four image data sets - 2D-line, 2D-Pie, 2D-Bar, 3D-Plots and returns a prediction score denoting the possibility of it being in a particular class.
Contributions:7 commits, 6 pushes, 1 branch in 6 months
3dconvolutional-neural-networksdeep-learningimage-classificationimagenet
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