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Faculty Profile

Alyosha Molnar

Department: ECE

Title: Assistant Professor

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Degrees earned:
 BS Swarthmore College 1997
 MSEE University of California, Berkeley 2003
 PhD University of California, Berkeley 2007

Address:
Office:
 402 Phillips Hall
 Ithaca, NY, 14853-5401

Office Phone: 254-8257

Biography:

Alyosha Molnar received a BS in engineering with highest honors from Swarthmore College in 1997. After working as a deckhand on a fishing boat, he joined Conexant Systems Inc in Newport Beach CA in 1998. At Conexant, he worked as a RFIC design engineer and co-led the design of their first generation direct conversion GSM transceiver, which has sold more than 20 million parts to date. He entered graduate school at UC Berkeley in 2001 and received his MSEE in 2003 for his design of an ultra-low power RF transceiver for “Smart Dust” working with Professor Kris Pister. He then joined Frank Werblin’s neurobiology lab where he completed his doctoral work (still in electrical engineering), focusing on dissecting the neuronal circuitry of the rabbit retina using a combination of electrophysiology, pharmacology and anatomy. After receiving his PhD in May 2007, Alyosha joined the ECE department at Cornell as an assistant professor where he will continue is interdisciplinary research in integrated circuits and neurobiology.

Research interests:

I am interested in nanoscale circuitry of all sorts, including transistor circuits manufactured in silicon and biological circuits of the nervous system. In silicon, I am especially interested in RF and mixed-signal integrated circuits, especially focusing on highly integrated, low-power system design. On the biological side, I am presently focusing primarily on understanding the neuronal code of the mammalian retina and uncovering the neural circuitry that underlies that code. I plan to bring these interests together in several ways. One is to work on developing the circuits and systems for improving the acquisition and subsequent handling of large quantities of data from massive multielectrode arrays. This could be combined with low power wireless design to build chronic wireless implants handling data from 100s of electrodes. At the same time, understanding the workings of neuronal circuits can inspire new silicon circuit ideas.