Gurumayum V.

Key Skills

Software

Don't disclose

Other

Top Subject Matter

Quantum Machine Learning for medical imaging (Alzheimer’s disease detection)

AI-powered creative tools (chatbots and websites)

Top Data Types

Text

Video

Top Task Types

Prompt + Response Writing (SFT)

Freelancer Overview

QML Research Intern - NIT Manipur (Quantum Machine Learning hybrid model for Alzheimer’s detection from MRI scans). Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and Other. Education includes Bachelor of Technology, Assam Royal Global University (2022) and High School Diploma (Class XII), Comet School (2022). AI-training focus includes data types such as Medical, DICOM, and Text and labeling workflows including Evaluation, Rating, and Prompt + Response Writing (SFT).

Labeling Experience

AI-Creative & Technical Intern - College Tips.in (AI-powered tools and chatbot-related development)

OtherTextPrompt + Response Writing (SFT)

Contributed to building AI-powered creative and technical tools, including chatbots and websites, as part of an applied AI development workflow. The internship involved creating content and technical assets that would be used to shape conversational and interactive experiences. The resume does not specify dataset labeling, but it reflects hands-on AI tool development and training-adjacent content creation. • Developed AI chatbot-related assets and conversational content. • Created campaign and social media content for AI initiatives. • Built and contributed to AI-powered websites and related UI elements. • Gained experience with UI/UX design and web/app development.

2025 - 2025

QML Research Intern - NIT Manipur (Quantum Machine Learning hybrid model for Alzheimer’s detection from MRI scans)

Don't disclose

Worked on quantum machine learning research for Alzheimer’s disease detection using MRI brain scans, involving preparation of model-ready datasets and performance evaluation. The work supported an AI training workflow by preprocessing input MRI data and integrating results into a GUI. No manual ground-truth labeling is explicitly stated, but the intern performed data preprocessing and evaluation as part of the training pipeline. • Performed data preprocessing on MRI brain scan inputs. • Implemented and trained hybrid quantum-classical model components. • Evaluated model performance and reported metrics. • Integrated outputs into a GUI for research usability.

2025 - 2025

Education

Work History