Ramana Vinjamuri

Biography

Ramana Vinjamuri received his undergraduate degree in Electrical Engineering from Kakatiya University (India) in 2002. He received his MS in Electrical Engineering from Villanova University in 2004 specialized in Bioinstrumentation. He received his PhD in Electrical Engineering in 2008 specialized in Dimensionality Reduction in Control and Coordination of Human Hand from the University of Pittsburgh. He worked as a postdoctoral fellow (2008-2012) in the field of Brain Machine Interfaces (BMI) to control prosthesis in the School of Medicine, University of Pittsburgh. He worked as a Research Assistant Professor in the Department of Biomedical Engineering at the Johns Hopkins University (2012-2013). In 2010, he was awarded Mary E. Switzer Merit Fellowship by National Institute on Disability and Rehabilitation Research (NIDRR) for his proposal, "A synergy based BMI to reanimate paralyzed hands". He has a pending patent as the lead inventor on related technology. In 2011, he was elevated to IEEE Senior Member. He has authored several publications in IEEE Transactions and conferences and other journals in the fields of biomedical engineering. He serves as reviewer for journals and conferences including IEEE Transactions in Biomedical Engineering, IEEE Transactions in Neural Networks, IEEE Transactions on Intelligent Transport Systems, IEEE EMBC, Journal of Neural engineering, Sensors, International Journal of Nano Medicine, etc. He also serves as grants reviewer for NIDRR

Research Interest

Our lab studies Brain-Machine Interfaces (BMIs) that control upper-limb prosthesis. In particular, we are interested in how brain controls complex hand movements. The human hand has about 30 dimensions in contrast to human arm that has only 7 dimensions. BMIs that control human arms have already been demonstrated with decent accuracies. What type of interface is needed to extend the control from 7 to 37 dimensions forms the central topic of research of the lab. This question is answered from several angles of approach including: studying the human hand kinematics, studying the nature and representation muscle activities during hand movements, and finally studying the neural signals in the brain. These studies involve application of linear and nonlinear signal processing, control and optimization methods.