Dharsini Jayaprakash. AI & ML Engineer
const engineer = {
focus: "AI / ML / Full-Stack",
status: "Graduating 2027",
passion: () => "Innovative tech solutions"
}
About Me
AI and Machine Learning student, with a strong interest in developing intelligent systems that solve real-world problems.
Experienced in building AI-based projects in healthcare, agriculture, and automation using machine learning and computer vision. Passionate about innovation, research, and applying AI to impactful domains such as medical robotics.
Technical Skills
Experience & Certifications
Completed the Google Virtual Internship in AIML, gaining practical exposure to real-world AI applications. Worked on intelligent systems, IoT projects, and web development. Developed a strong foundation in applying AI to meaningful problems.
Currently working as a Tech Associate Intern, contributing to technical initiatives and hands-on development tasks across projects.
Achievements
Selected as a finalist in the Bhumi Hackathon for developing an innovative AI-based solution, competing against teams across multiple institutions.
Reached the finals of the ICT Web Development Hackathon by showcasing full-stack application skills, demonstrating proficiency in end-to-end development.
Projects
Developed an intelligent chatbot prototype to assist engineering students with academic guidance, study planning, and learning resources. Implemented AI-driven personalized study plans with progress tracking and gamification elements.
Proposed a multimodal AI framework combining DNA genomic data with ECG/EEG bio-signals for automated medical diagnosis. Designed CNN-LSTM models for bio-signal anomaly detection and BioBERT approach for genomic disease analysis with explainable AI techniques.
Developing an online platform to help students improve aptitude and problem-solving skills for competitive exams and campus placements. Features include timed quizzes, instant feedback, and performance tracking with questions on quantitative aptitude and logical reasoning.
WildTrack is an AI-powered wildlife monitoring system that uses a fine-tuned YOLOv8 model to detect and classify animals from camera images. It provides real-time results via a FastAPI backend, stores geo-tagged data in MongoDB, and features an interactive dashboard with maps and analytics to help track biodiversity efficiently.
A fun, privacy-first photo booth in the browser with 28 filters, 20 frames, and stickers. Users can sign in, snap photos, and download memories while keeping their gallery private. Photos never leave the device, with no cloud uploads and no tracking.