Shreeraksha R Adiga

I am Shreeraksha R Adiga, a B.Tech student specializing in Artificial Intelligence and Machine Learning, driven by a deep interest in research, real-world problem solving, and knowledge sharing. My academic and professional journey reflects a strong commitment to building impactful, deployable AI systems rather than limiting my work to theoretical exploration.

One of my core strengths lies in research and innovation at an early stage. I am a Best Paper Award recipient at the DSISYS 2025 Springer International Conference, and I have authored and co-authored multiple research papers published across IEEE, Springer Nature, IJERT, and JIMS 8i. My research primarily focuses on medical AI, IoT-enabled safety systems, explainable AI, and multimodal learning, with an emphasis on improving accuracy, efficiency, and real-world usability.

From a technical perspective, I possess strong expertise in Machine Learning, Deep Learning, Explainable AI, Multimodal AI, IoT systems, and full-stack development. I have designed and implemented several end-to-end solutions, including Amrit Life, a multimodal disease diagnosis platform with multilingual and explainable AI support; FibroFusion, an advanced liver histopathology modeling system using ensemble meta-learning and clinical vectors; and Bone Cancer Detection systems leveraging CNN architectures such as VGG16 and DenseNet. Many of my projects are fully deployed and evaluated, delivering measurable outcomes such as reduced diagnosis time, improved safety response, and increased system reliability.

Leadership and initiative form a significant part of my profile. I am a Co-founder of the HARVEST Club at my university and have served as its Secretary, where I managed operations, documentation, reporting, and team coordination. I have also led interdisciplinary teams as a Project Team Leader, overseeing design, testing, and real-world validation. My participation and recognition in national-level hackathons, ideathons, expos, and entrepreneurship competitions have further strengthened my ability to innovate under constraints and deliver competitive solutions.

Equally important to me is teaching and mentorship. I have worked as a Teaching Intern at Adore, teaching Class 10 students and focusing on conceptual clarity, structured learning, and confidence building. I also served as a Mentor at GirlScript Summer of Code (GSSOC) 2025, where I guided contributors in open-source development, conducted code reviews, promoted best practices in Git and collaborative workflows, and helped mentees grow into confident contributors. These experiences have reinforced my passion for education, mentorship, and community-driven growth.

My academic foundation is supported by elite and silver certifications from NPTEL, Microsoft Azure Fundamentals, and recognized credentials in data science and machine learning. I have a strong grounding in linear algebra, advanced calculus, and numerical methods, which allows me to approach model design and evaluation with theoretical rigor rather than surface-level implementation.

Overall, I see myself as a research-oriented AI practitioner in the making, with strengths in applied AI, healthcare-focused systems, leadership, and mentorship. I am motivated to pursue opportunities that allow me to contribute to meaningful AI research, collaborate with driven teams, and create technology that delivers measurable social impact.