Princeton University

School of Engineering & Applied Science

Verma receives E-council Excellence in Teaching Award

Prof. Naveen Verma is one of the recipients of E-council Excellence in Teaching Award for the course ELE 462. 

Abbe receives Google Research Faculty Award

Prof. Emmanuel Abbe received the Google Research Faculty Award for his proposal entitled "Detecting hidden structures in networks".

Abbe receives NSF Career Award

Prof. Emmanuel Abbe is the recipient of NSF CAREER Award entitled "Information-theoretic foundations of community detection and graphical channels".

Mittal receives Google Faculty Research Award

Prof. Prateek Mittal has received the Google Faculty Research Award for his work on "Enabling Privacy-preserving Computations".

Poor awarded prestigious Fritz Medal

Prof. Vincent Poor, dean of Princeton University’s School of Engineering and Applied Science, was awarded the 2016 John Fritz Medal by the American Association of Engineering Societies for his fundamental contributions to wireless technology.

Book co-edited by Sengupta published

A book edited by Prof. Kaushik Sengupta and Prof.Hua Wang (an assistant professor in the Department of Electrical Engineering at Georgia Institute of Technology) is being published by Elsevier publishing.  It is titled  “RF and mm-Wave Power Generation in Silicon”.

Lee’s research on cybersecurity mentioned on Princeton's main webpage

Prof. Ruby Lee’s research on enhancing cybersecurity using computer hardware rather than software only is highlighted on Princeton main webpage.

EE Department Welcomes Two New Assistant Professors

The EE Department is growing!

Sengupta wins funding from IP Accelerator Fund

Prof. Kaushik Sengupta has won funding from the Intellectual Property Accelerator Fund for his proposal entitled, "Towards Moore’s law in diagnostics: Battery powered, fully integrated CMOS ICs for massively multiplexed point‐of‐Care biosensors".

Prucnal group’s research mentioned in IEEE Spectrum

Research done by Prof. Paul Prucnal’s group is mentioned in IEEE Spectrum.  The group is working on developing so-called neuromorphic chips consisting of networks of transistors that interact the way neurons do, allowing them to process analog input, such as visual information, more quickly and accurately than traditional chips can. 

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