Bio
Shruti R. Kulkarni is a Research Scientist in the Learning Systems group at Oak Ridge National Laboratory (ORNL). Her primary research focus is in neuromorphic computing, where she studies bio-inspired learning algorithms and evolutionary algorithms for Spiking Neural Networks (SNNs), and their application to diverse class of problems ranging from graph analysis, autonomous navigation and in smart instrumentation in High Energy Physics experiments. Some of her work also involves studying explainability of SNNs and exploring causal representations and learning in neuroscience models. She is also involved in the co-design effort for optimal realization of neuromorphic algorithms coherently with the design of energy efficient analog hardware platforms such as memristive crossbar arrays and digital FPGAs. She is also involved in developing performant neuromorphic simulator - SuperNeuro that can be run on HPC platforms, and also used for neuromorphic hardware co-design.
Professional Experience
Her industry experience includes internships at Micron Technologies and Texas Instruments during her PhD, in the areas of acceleration and optimal realization of machine learning models. She also worked as an ASIC front-end engineer at Juniper Networks India Pvt. Ltd. for ASIC design verification.
Awards
She is also a co-developer of a highly performant neuromorphic simulator - SuperNeuro, which won the R & 100 award for 2023.
Education
She received her Ph.D. in Electrical Engineering from New Jersey Institute of Technology in 2019 under the supervision of Dr. Bipin Rajendran, where she completed her dissertation on bio-inspired learning and hardware acceleration with emerging memories. She has over 50 peer reviewed journal and conference publications.
Professional Service
Organizing member for the International Conference on Neuromorphic Systems (ICONS).