Embedded Files
About me:
About me:
Dr. Laavanya Rachakonda is an Assistant Professor in the Department of Computer Science at the University of North Carolina Wilmington (UNCW). She is the Director of the Smart and Intelligent Physical Systems (SIPS) Laboratory, leading a diverse, multidisciplinary research team including high school students, undergraduates, and graduate students.
Dr. Rachakonda earned her Ph.D. and Master of Science in Computer Science and Engineering from the University of North Texas, Denton, under the guidance of Dr. Saraju P. Mohanty in the Smart Electronic Systems Laboratory (formerly Nano System Design Laboratory) in May 2021 and December 2019, respectively. She completed her Bachelor of Technology in Electronics and Communication Engineering at VMTW, JNTUH, India 2015.
Her research focuses on advancing Machine Learning (ML) and Artificial Intelligence (AI) methodologies to address critical challenges in Smart Healthcare, Transportation, and Smart Living Environments. She is also exploring Security and Privacy in these domains, with particular emphasis on:
IoMT (Internet of Medical Things) for Smart Healthcare, and
IoT-Enabled Consumer Electronics for Smart Living.
Dr. Rachakonda actively engages in outreach programs, including initiatives to broaden participation in Carolinas Women in Computing and STEM. She is a passionate educator and researcher committed to fostering inclusive and collaborative learning environments.
She believes in the power of resilience, self-belief, and continuous growth to achieve success. She values people and the situations that shape her journey, finding joy and inspiration in her surroundings. She enjoys sports, dancing, painting, reading novels, singing, cooking, and home decorating in her leisure time.
Research Interests:
Research Interests:
Smart Healthcare Applications: Developing innovative solutions to improve daily life and healthcare accessibility through IoMT-based approaches.
Smart Agriculture and Smart Cities: Leveraging IoT and AI technologies to enhance sustainability, efficiency, and quality of life.
IoT-Enabled Consumer Electronics: Designing application-specific architectures for smarter and more efficient electronic systems.
Machine Learning: Advancing computational methods and models to solve real-world problems in diverse domains.
Embedded Systems for IoT: Creating efficient and scalable embedded systems for seamless IoT integration.
Real-Time Operating Systems (RTOS): Implementing real-time solutions to optimize IoT applications requiring high reliability and low latency.
Google Sites
Report abuse