Hi, I'm Shubham Gajjar
AI Researcher & M.S. Artificial Intelligence Student at Northeastern University, exploring deep learning, computer vision, and biomedical imaging.
About Me
I'm an AI researcher and graduate student at Northeastern University, passionate about advancing deep learning applications in biomedical imaging and computer vision. My work focuses on hybrid neural architectures for tumor segmentation and skin lesion classification, bridging healthcare and AI.
I've published my research on "Hybrid ResNet-ViT for Skin Cancer Classification" at the 4th IEEE World Conference on Applied Intelligence and Computing (AIC 2025), achieving 96.3% accuracy and Area Under Curve of 1.00 across all classes. I've also contributed to research currently under review at Elsevier, focusing on VGG16-MCA UNet for brain tumor segmentation. My work demonstrates state-of-the-art performance in medical image analysis, and I'm currently exploring the integration of deep learning with healthcare and biological imaging as part of my ongoing research journey.
Technical Skills
Specialized expertise in AI/ML, computer vision, and research methodologies
AI/ML Core
Deep Learning Frameworks
Computer Vision
Data Science & Analytics
Research & Development
Game AI & RL
Cloud & DevOps
Tools
Leadership & Adaptability
Work Experience
Professional experience in AI research and software development
Artificial Intelligence Engineer
Developed and deployed AI solutions, optimized system performance, and delivered mobile applications in an Agile environment.
- •Demonstrated problem-solving by architecting a multi-agent Application Programming Interface system using distributed computing, reducing report generation from 20 to 5 minutes for 10,000+ queries
- •Exhibited creativity by engineering pagination and authentication systems for dashboards, accelerating page load times by 80%, and ensuring model deployment stability for 500+ concurrent sessions
- •Applied continuous learning to deliver iOS applications using React Native, increasing mobile engagement by 45% within the first quarter
- •Collaborated with a 5-member team in Agile sprints; performed code reviews to improve quality metrics by 30%
Research Publications
Published research in medical AI and computer vision, contributing to healthcare advancement
Hybrid ResNet-ViT for Skin Cancer Classification
Shubham Gajjar, Harshal Joshi, Om Rathod, Vishal Barot, Deep Joshi
4th IEEE World Conference on Applied Intelligence and Computing (AIC 2025)
2025 • DOI: 10.1109/AIC60235.2025.11212073
Designed hybrid architecture combining frozen ResNet50 feature extractor with four-head Vision Transformer blocks, attaining 96.3% accuracy and macro F1 of 0.961 on HAM10000 dataset. Integrated Global Average Pooling and multi-head self-attention for seven-class skin lesion classification, achieving Area Under Curve of 1.00 across all classes. Published and presented at IEEE World Conference on Applied Intelligence and Computing (AIC 2025) to 100+ attendees.
Research Areas
VGG16-MCA UNet for Brain Tumor Segmentation
Shubham Gajjar, Deep Joshi, Avi Poptani, Vishal Barot
Elsevier
2025 • DOI: Pending
Led innovation by designing VGG16-based encoder with Multi-Channel Attention decoder achieving 99.59% accuracy and 99.71% specificity on LGG Brain MRI Segmentation dataset from 110 low-grade glioma patients. Implemented ensemble learning combining multiple model configurations, improving Dice coefficient by 3.7% over standard UNet. Applied data engineering with preprocessing pipeline implementing skull stripping, intensity normalization, and resizing to 256x256 pixels for FLAIR MRI scans.
Research Areas
Extended ResNet50 with Inverse Soft Mask Attention for Skin Cancer
Shubham Gajjar, Harshal Joshi, Om Rathod, Vishal Barot, Deep Joshi
Journal Submission
2025 • DOI: Pending
Developed two-stage pipeline combining U-Net++ hair segmentation with Extended ResNet50 classifier featuring Inverse Soft Mask Attention mechanism, achieving 97.89% accuracy on HAM10000 dataset with 10,015 dermoscopic images. Applied creativity by integrating dense residual blocks and Squeeze-and-Excitation modules with learnable weighted feature aggregation for hair-occluded and unoccluded regions. Utilized Nadam optimizer with Cosine Decay Restarts and Sparse Categorical Crossentropy loss, incorporating explainable AI principles ensuring model deployment readiness.
Research Areas
Projects
Cutting-edge research in medical AI and innovative AI/ML projects showcasing deep learning expertise
TrackMania Reinforcement Learning Agent
Developed an advanced reinforcement learning agent for TrackMania racing game using Implicit Quantile Networks (IQN). The agent learns optimal racing strategies through trial and error, achieving competitive lap times and demonstrating robust decision-making in unpredictable racing situations.
Twitter Sentiment Analysis (NLP Project)
Built a comprehensive sentiment analysis system using Twitter API to analyze public sentiment on various topics. Implements NLP techniques and machine learning models for real-time sentiment classification.
Interactive Image Mosaic Generator
Developed an interactive web application for generating artistic image mosaics using vectorized NumPy operations. Built with Gradio for an intuitive user interface, enabling users to create stunning mosaic art from input images through efficient computational image processing techniques.
Badges
Digital badges and credentials from Northeastern University
Foundations of Software Engineering and Data Management Learning
Northeastern University
View CredentialLet's Collaborate
Open to research collaborations in AI for healthcare, biomedical imaging, and computer vision.
Based at The Roux Institute, Northeastern University — Portland, Maine.
Research Focus
Medical AI, Computer Vision, Deep Learning
Expertise
Medical AI, Hybrid Deep Learning Architectures, Multi-Agent Systems, IEEE Publications
Send a Message
Interested in collaborating on cutting-edge AI research or innovative machine learning projects? Let's connect!