Sai Kiran Reddy Boreddy
Welcome to my personal portfolio and research website! I am a graduate student specializing in electrical and biomedical engineering. My passion lies in AI-driven medical imaging research and the intersection of healthcare and technology. Let's explore my academic background, research experience, technical skills, and key projects in microfluidics and bioengineering.
About Me
My Vision
As a dedicated researcher and graduate student, I have a strong foundation in electrical and biomedical engineering. My vision is to leverage AI-driven solutions to revolutionize medical imaging and contribute to the advancement of healthcare technologies. I am committed to delivering impactful research that enhances patient care and improves diagnostic accuracy.
Experience
7D Imaging (U.S.A)
Advisor: Dr. Cliff (CEO)
AI Research & Development Intern
At 7D Imaging, I am actively involved in developing and deploying deep learning models for breast cancer detection. My role covers the end-to-end pipeline: from dataset preparation and exploratory data analysis to designing, training, and optimizing neural networks on GPU infrastructure. I also collaborate on backend support, contributing to AWS-based cloud deployment and enhancing application performance. Additionally, I assist with the integration of React JSX for building user-friendly interfaces
University of Colorado Anschutz Medical Campus(U.S.A)
Advisor: Dr. Tim Lei
Graduate Research Assistant
I co-lead the development of AI models for cardiac MRI analysis. My primary focus is on designing, training, and deploying deep learning pipelines specifically using U-Net with ResNet-34 encoders for automated left ventricular segmentation, achieving 95% accuracy.
Chang Gung University (Taiwan)
Advisor: Dr.Thomas Lei
Graduate Research Assistant
As a Graduate Research Assistant in the Bio-MEMS Lab, I specialized in microfluidic biochip development for biomedical applications. My work focused on fabricating advanced microfluidic chips using soft lithography, photolithography, and 3D printing techniques. I engineered agarose-based drug screening platforms and conducted experiments to analyze dual-drug gradients on cancer cells, advancing precision medicine research.
Education
Academic Background
Master's Degree in Electrical and Biomedical Engineering
Undergraduate in Electronics and Communication Engineering
Continuous Learning and Skill Enhancement
Achievements
Research Impact
Publications and Presentations
Contributing to Advancements in Medical Imaging
Published research articles and presented at international conferences in the fields of AI-powered medical imaging and microfluidic technologies. Recognized for pioneering work in deep learning–based MRI segmentation, cardiac diagnostics, and single-cell analysis, with an emphasis on translating technical innovations into clinical applications.
Project Excellence
Successfully Launching High-Impact Websites
Successfully Launching High-Impact Research Projects
Led and contributed to over 6 major research projects in medical imaging, computational biology, and microfluidic device development. Developed AI-driven pipelines that improved cardiac MRI segmentation accuracy to 95% and reduced manual processing time by 50%. Engineered microfluidic drug screening platforms tested across 10+ microzones, accelerating experimental throughput by 60%. Collaborated with teams of 5–10 researchers to consistently deliver results within strict timelines, ensuring all project milestones were met and published in leading journals.
Innovative Solutions
Pioneering Scalable and Future-Proof Technologies
Implemented scalable AI and cloud-based solutions that reduced image segmentation time from 1 minute to 0.5 seconds per image. Developed and deployed API-driven tools for processing over 100 MRI datasets, cutting manual bias by 50%. Designed and integrated custom pipelines using AWS and high-performance computing, resulting in a 40% boost in research workflow efficiency and supporting real-time clinical data analysis.
Team Collaboration
Fostering a Productive and Inclusive Work Environment
Worked closely with interdisciplinary teams of 5–12 members, including engineers, clinicians, and data scientists, to drive projects from concept to completion. Facilitated agile workflows and regular team meetings, resulting in a 40% reduction in project turnaround times. Promoted a collaborative culture that enabled knowledge sharing and supported the successful publication and deployment of AI-driven medical solutions.
Leadership Excellence
Guiding and Mentoring Junior Developers
Served as head organizer for the IEEE NanoMed 2023 Conference in Okinawa, Japan, and the IEEE Reliability conference in the USA, engaging 150+ professionals and researchers. Mentored 8+ undergraduate and graduate interns in lab settings, teaching hands-on skills in microfluidic chip fabrication, AI medical imaging, and 3D CAD modeling. Delivered 12+ training sessions, advancing technical expertise and fostering a collaborative research environment.
Community Engagement
Sharing Knowledge and Giving Back
Actively contributed to open-source AI and medical imaging projects, and participated in 10+ research seminars and academic meetups globally. Published technical articles and how-to guides, sharing insights on biochip design, deep learning, and 3D CAD modeling. Volunteered as a mentor for aspiring engineers and researchers, providing guidance to over 15 students and early-career professionals. Passionate about advancing the community by fostering collaboration, supporting diversity in STEM, and staying at the forefront of scientific innovation.
