Engineer, Researcher, Builder
Driven by curiosity and a passion for building systems that learn.
I am an AI/ML Engineer and Computer Engineering graduate student at Johns Hopkins University, where I work at the intersection of artificial intelligence, machine learning, and scalable software systems.
My research and engineering focus spans computer vision, natural language processing, and deep learning — with a commitment to building production-grade systems that translate research innovations into real-world impact. I bring a strong foundation in systems design, algorithm development, and software engineering best practices.
I thrive in environments where I can push the boundaries of what's possible with intelligent systems, collaborating with teams that value rigor, creativity, and purposeful engineering.
Education
Johns Hopkins University
M.S. Computer Engineering
Focus Areas
Where I’ve Worked
A track record of research, engineering, and impact across AI and software.
Computer Vision Research Intern
Johns Hopkins University
- ›Developing novel deep learning architectures for medical image analysis and 3D reconstruction
- ›Implementing state-of-the-art computer vision pipelines for real-time object detection and segmentation
- ›Contributing to research publications in top-tier AI conferences
AI/ML Engineer Intern
SimpleTicket
- ›Designed and deployed NLP models for automated customer support ticket classification and routing
- ›Built scalable ML inference pipelines serving thousands of predictions per minute
- ›Reduced average ticket resolution time by optimizing AI-driven response generation
Software Engineer Intern
Safarii Infosoft
- ›Developed full-stack web applications using modern frameworks and cloud services
- ›Implemented RESTful APIs and microservices architecture for enterprise clients
- ›Collaborated with cross-functional teams following agile development practices
Selected Work
Research and engineering projects that push the boundaries of AI and software systems.
A biometric authentication system leveraging keystroke dynamics and AI-generated prompt analysis. Uses deep learning models to create unique behavioral fingerprints from typing patterns, achieving high-accuracy user verification without traditional passwords.
An AI-driven clinical decision support system for early bipolar disorder detection using multimodal data analysis. Combines NLP-based sentiment analysis of patient records with physiological signal processing to improve diagnostic accuracy.
Technical Toolkit
Technologies and frameworks I work with to build intelligent, scalable systems.
Languages
AI / ML
Systems & Tools
Let’s Connect
Interested in collaborating on AI research, engineering projects, or opportunities? Reach out.
I'm always open to discussing new opportunities, research collaborations, or interesting engineering challenges. Feel free to reach out through any of the channels below.
shaiv@example.com
linkedin.com/in/shaivpatel
GitHub
github.com/shaivpatel