Hiranmayi Ranganathan PhD
About Hiranmayi Ranganathan PhD
Hiranmayi Ranganathan, PhD, is a Senior Staff Researcher specializing in Machine Learning at Lawrence Livermore National Laboratory. With over ten years of research experience, she has a strong background in Deep Learning and Computer Vision, having worked at various prestigious institutions including Intel Corporation and Arizona State University.
Work at Lawrence Livermore National Laboratory
Hiranmayi Ranganathan currently holds the position of Senior Staff Researcher in Machine Learning at Lawrence Livermore National Laboratory. She has been with the laboratory since 2022. Prior to her current role, she worked as a Machine Learning Researcher from 2021 to 2022 and as a Postdoctoral Research Staff Member from 2019 to 2021. Her work at the laboratory focuses on the application of machine learning techniques in various research projects.
Education and Expertise
Hiranmayi Ranganathan has a strong educational background in engineering and computer science. She earned her Bachelor's Degree in Electrical, Electronics and Communications Engineering from Anna University from 2002 to 2006. She then completed her Master's Degree in Electrical and Computer Engineering at the University of California, Santa Barbara from 2006 to 2009. Ranganathan obtained her Doctor of Philosophy (Ph.D.) in Computer Vision and Machine Learning from Arizona State University, where she studied from 2009 to 2018.
Background in Industry
Ranganathan has extensive experience in the technology sector, particularly with Intel Corporation. She held multiple roles at Intel, including Graduate Software Intern in 2010 and 2011, Graduate Technical Intern in 2012, and Graduate Research Intern in 2013. Her internships spanned various locations, including Phoenix, Arizona, Santa Clara, California, and Hillsboro, Oregon. These experiences contributed to her development skills in MATLAB and Python.
Research Contributions
Hiranmayi Ranganathan has ten years of research experience focused on Deep Learning and Computer Vision. She authored a Ph.D. dissertation titled 'Deep Active Learning Explored Across Diverse Label Spaces,' which reflects her expertise in machine learning methodologies. Her research has implications for advancing the field of machine learning and its applications across diverse domains.