Ramashish Gaurav is a Ph.D. student in Neuromorphic Computing at Virginia Tech with 11 years of software engineering experience and advanced degrees in AI and Computational Neuroscience. He designs energy- and resource-efficient Spiking Neural Networks for time-series classification and develops local and online learning algorithms for both off-chip and on-chip training, with hands-on deployments on Intel Loihi-1 and Loihi-2. Prior to academia he built production systems at Nutanix—designing RPCs for hypervisor VM management and a metadata service—and has mentored interns on ML-driven scheduling prototypes. Combining rigorous academic results (4.0 PhD GPA, 95/100 MASc) with production-grade engineering, he bridges theoretical neuromorphic research and practical system implementation. Open to neuromorphic research internships, he brings a rare mix of chip-level deployment experience and scalable software craftsmanship.
10 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Neuromorphic Computing, Spiking Neural Networks, 4.0/4.0, Doctor of Philosophy - PhD, Neuromorphic Computing, Spiking Neural Networks, 4.0/4.0 at Virginia Tech
Dual Degree (M.Tech. + B.Tech.), Computer Science & Engineering, 9.10/10.0, Dual Degree (M.Tech. + B.Tech.), Computer Science & Engineering, 9.10/10.0 at Indian Institute of Technology (Banaras Hindu University), Varanasi
MASc., Artificial Intelligence, Computational Neuroscience, 95/100, MASc., Artificial Intelligence, Computational Neuroscience, 95/100 at University of Waterloo
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Ramashish Gaurav - Doctoral Student at Virginia Tech