COVID-19 Information

Rahul Sarpeshkar headshot

Rahul Sarpeshkar

Professor of Engineering

Thomas E. Kurtz Professor
Professor of Microbiology & Immunology
Professor of Physics
Professor of Molecular & Systems Biology
Chair, Neukom Cluster of Computational Science

​Office: Vail 507A, Geisel School of Medicine

Overview

Professor Sarpeshkar's interdisciplinary research uses analog circuits and analog computation as a universal language to design advanced quantum, bio-molecular, and nano-electronic circuits and systems, from atom to living cell. These systems are experimentally implemented in living synthetic microbial DNA-RNA-protein circuits in his wet lab, and in nano-electronic supercomputing chips that emulate or are inspired by biological and quantum computation in his dry lab. His fundamental work has been applied to implantable medical devices, synthetic biology, systems biology, neural prosthetics, bio-inspired, and ultra-energy-efficient systems. His research group members have originated from a wide variety of disciplines including physics, bioengineering, microbiology, computer science, and analog circuit engineering.

Professor Sarpeshkar is Dartmouth's inaugural Thomas E. Kurtz Professor and Chair of the Neukom Computational Science Cluster. He is a Professor of Engineering, Physics, Microbiology & Immunology, and Molecular & Systems Biology. He has published over 140 research articles, holds 43 patents, and has authored a leading textbook on analog circuits and bioelectronic systems. Prior to his joining Dartmouth, he was a tenured and award-winning professor at MIT.

Research & Job Opportunities

  • Research Associate, Analog or RF Circuit Designers
    • The Sarpeshkar Lab at Dartmouth has multiple immediately-available openings for talented analog or RF circuit designers at the PhD and Postdoctoral levels. The group has originated several first or best innovations in analog, RF, biomedical, bio-inspired, and biological chips and systems over two decades at MIT and at Dartmouth. Those interested in a PhD or a postdoc should send a CV and email to Prof. Sarpeshkar with their Skype and email contact information.
    • Postdoctoral applicants should send their CVs, and have at least 3 references email letters to christine.l.collins@dartmouth.edu.
    • PhD applicants should also concurrently apply at the Thayer School of Engineering PhD Graduate Admissions website. Dartmouth’s efficiency can enable speedy admission for qualified applicants.
  • Synthetic Biology Openings for Undergraduates
    • This part-time position will work closely under the tutelage of a Post-doctoral researcher to work on sophisticated synthetic biological circuits in living cells which have numerous applications in biotechnology and medicine. Candidates with basic bioengineering or microbiology skills such as PCR, DNA Assembly, and molecular cloning will be preferred. For more information and to apply, email rahul.sarpeshkar@dartmouth.edu.

Education

  • BS, Electrical Engineering and Physics, MIT
  • PhD, Computation and Neural Systems, California Institute of Technology

Research Interests

Analog synthetic biology; biological and bio-inspired super-computing chip design; quantum circuit design, quantum computation, and hybrid quantum-classical computation; feedback control systems; medical devices; ultra-low-power, fault tolerant, and ultra-energy-efficient systems; engineering systems that operate at the fundamental limits of physics

Selected Publications

  • J.K. Medley, J. Teo. S.S. Woo, J. Hellerstein, R. Sarpeshkar, H.M. Sauro, "A compiler for biological networks on silicon chips," PLoS Comput Biol 16(9): 2020. https://doi.org/10.1371/journal.pcbi.1008063
  • J. Zeng, J. Teo, A. Banerjee, T.W. Chapman, J. Kim, R. Sarpeshkar, “A Synthetic Microbial Operational Amplifier,” ACS Synth Biol. 2018 Sept 21; 7(9):2007-2013. Doi:10.1021/acssynbio.8b0013
  • S.S. Woo, J. Kim, and R. Sarpeshkar, "A Digitally Programmable Cytomorphic Chip for Simulation of Arbitrary Biochemical Reaction Networks," IEEE Transactions on Biomedical Circuits and Systems, Vol. 12, No. 2, February 2018.
  • J. Kim, S.S. Woo, and R. Sarpeshkar, "Fast and Precise Emulation of Stochastic Biochemical Reaction Networks With Amplified Thermal Noise in Silicon Chips," IEEE Transactions on Biomedical Circuits and Systems, Vol. 12, No. 2, February 2018.
  • A. Banerjee, I. Weaver, T. Thorson, R. Sarpeshkar. Bioelectronic measurement and feedback control of molecules in living cells. 2017. Scientific Reports. 7.12511. Doi:10.1038/s41598-017-12655-2
  • S. Mandal and R. Sarpeshkar, “A Simple Model for the Thermal Noise of Saturated MOSFETs at All Inversion Levels," IEEE Journal of the Electron Devices Society, Vol. 5, No. 6, pp. 458–465, Nov. 2017.
  • J. Teo, S. Woo, and R. Sarpeshkar. “Synthetic Biology: A Unifying View and Review Using Analog Circuits,” IEEE Transactions on Biomedical Circuits and Systems, Special Issue in Synthetic Biology, Vol. 9, No. 4, August 2015.
  • S. Woo and R. Sarpeshkar, “A Cytomorphic Chip for Quantitative Modeling of Fundamental Bio-molecular Circuits,” IEEE Transactions on Biomedical Circuits and Systems, Special Issue in Synthetic Biology, Vol. 9, No. 4, August 2015.
  • R. Sarpeshkar, “Analog Synthetic Biology,” Philosophical Transactions of the Royal Society A, 372: 20130110, 2014.
  • R. Daniel, J. R. Rubens, R. Sarpeshkar, and T. K. Lu, “Synthetic Analog Computation in Living Cells,” NATURE, Vol. 497:7451, pp. 619-623, 2013; doi:10.1038/nature12148
  • S.S. Woo and R. Sarpeshkar, “A Spiking-Neuron Collective Analog Adder with Scalable Precision,” Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1620-1623, Beijing, China, May 2013.
  • Benjamin I. Rapoport, Jakub T. Kedzierski, Rahul Sarpeshkar, “A Glucose Fuel Cell for Implantable Brain-Machine Interfaces,” PLoS ONE, Vol. 7, No. 6, e384386, 2012.

Awards

Courses

  • ENGS 162: Basic Biological Circuit Engineering
  • ENGS 262: Advanced Biological Circuit Engineering
  • ENGS 59: Basic Biological Circuit Engineering

Patents

  • Emulation of quantum and quantum-inspired dynamical systems with classical transconductor-capacitor circuits | 10,769,338
  • Emulation of quantum and quantum-inspired discrete-state systems with classical transconductor-capacitor circuits | 10,275,556
  • A quantum cochlea for efficient spectrum analysis | 10,248,748
  • Emulation of quantum and quantum-inspired spectrum analysis and superposition with classical transconductor-capacitor circuits | 10,204,199

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