Pramey Upadhyaya

Pramey Upadhyaya is an Assistant Professor of Electrical and Computer Engineering at Purdue University. Before joining Purdue, Pramey was a postdoctoral scholar in the Physics and Astronomy Department, University of California Los Angeles, working under the mentorship of Prof. Yaroslav Tserkovnyak. He earned his bachelor’s degree in Electrical Engineering from the Indian Institute of Technology Kharagpur, India, in 2009, and Master’s and PhD degrees in Electrical Engineering from the University of California, Los Angeles, in 2011 and 2015, respectively. During his PhD, he was a resident theorist in the experimental group (Device Research Laboratory) led by Prof. Kang Wang.
His research has explored the theory of classical and quantum spintronic phenomenon and their device applications, enabled by electrical and thermal control of magnetism. Along with his teammates, this work has resulted in one of the earliest demonstrations of current-induced room-temperature skyrmion manipulations, spin torque switching by topological surface states and NV-center probing of spin-caloritronics. These works have resulted in over 30 publications in journals including Science, Physical Review Letters, Nature Nanotechnology, Nature Materials and Nature Communications with an H-index of 24.
He is a recipient of an NSF Career award (2020), a Qualcomm Innovation Fellowship (2013) and an Intel Summer Fellowship (2011).

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Applied Quantum Computing III: Algorithm and Software (edX)

Mar 28th 2022
Applied Quantum Computing III: Algorithm and Software (edX)
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Learn domain-specific quantum algorithms and how to run them on present-day quantum hardware. This course is part III of the series of Quantum computing courses, which covers aspects from fundamentals to present-day hardware platforms to quantum software and programming. The goal of part III is to discuss some of [...]

Applied Quantum Computing II: Hardware (edX)

Feb 14th 2022
Applied Quantum Computing II: Hardware (edX)
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Learn how present-day material platforms are built to perform quantum information processing tasks. This course is part 2 of the series of Quantum computing courses, which covers aspects from fundamentals to present-day hardware platforms to quantum software and programming. The goal of part 2 is to provide the essential [...]

Applied Quantum Computing I: Fundamentals (edX)

Jan 10th 2022
Applied Quantum Computing I: Fundamentals (edX)
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Learn the fundamental postulates of quantum mechanics and how they can be mapped onto present-day quantum information processing models, including computation, simulation, optimization, and machine learning.