Author Archives: Christof Teuscher

IMMEDIATE OPENING: GRA Position in Molecular Computing

The research group of Prof. Christof Teuscher has an immediate opening for a paid PhD or MS student in the area of molecular computing and novel computing paradigms.

Molecular computing is a promising computational paradigm, in which computational functions are evaluated at the nanoscale, with potential applications in smart molecular diagnostics and therapeutics. However, despite recent advances in the field, prospects for direct application of these techniques to solve real-world problems are limited by the lack of robust interfaces between molecular computers and biological and chemical systems. This project will address these limitations by targeting two application domains, wide-spectrum chemical sensing and cell surface analysis using molecular logic cascades. Drawing on a combination of experimental, theoretical, and computational tools, molecular computing systems will be developed for use in these application domains. Molecular circuit architectures that process sensor inputs from chemical sensors and cell-surface analysis reactions will be designed, modeled, and implemented in the laboratory, and computational modeling will be used to predict and optimize interactions between DNA circuit components and their binding targets. Furthermore, advanced molecular circuit architectures capable of adaptive, bio-inspired behavior, such as dynamic learning and adaptation, will be designed, with a view to future experimental implementations of these features.

The position is funded by the National Science Foundation. The project is a collaboration with teams from the University of New Mexico and Columbia University.

QUALIFICATIONS

  • The ideal candidate has experience in DNA and molecular computation, unconventional computation, computational intelligence, machine learning, neural networks, reservoir computing, and optimization techniques.
  • Must be enrolled in the ECE or CS MS or PhD program at PSU for the fall ’21, winter ’22, and spring ’22.
  • Excellent Python programming skills.
  • Interested in far-reaching cutting-edge interdisciplinary research.
  • Outstanding academic records.
  • Excellent written and verbal communication skills in English.
  • Highly motivated, responsible, independent, with outstanding work ethics.
  • Visionary, creative, outside-the-box thinker.

WHAT YOU GET

A place to invent, design, create, investigate, support and advice, an unconventional lab environment, free coffee, a GRA stipend, tuition, a foosball table, access to a powerful research compute server, a unique team, opportunities to collaborate with researchers from other fields.

WHAT WE DO

The mission of teuscher.:Lab is review the foundations of computer technology to help solve tomorrow’s technological and societal problems. We use a radical interdisciplinary approach and apply tools from computer science, computer engineering, physics, biology, complex systems science, and cognitive science to the study and the design of next generation computing models and architectures. Our research and education have global impact. We educate lifelong learners through academic excellence.

APPLICATION

Send application materials to teuscher@pdx.edu. Review will begin immediately. The position remains open until filled. Portland State University is an Equal Opportunity/Affirmative Action Employer.

NEW PAPER: Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks

Lilak S, Woods W, Scharnhorst K, Dunham C, Teuscher C, Stieg AZ and Gimzewski JK (2021) Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks. Frontiers in Nanotechnology, 3:675792. doi: 10.3389/fnano.2021.675792

Abstract: Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing. This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.

NEW PAPER: A golden age for computing frontiers, a dark age for computing education?

Paper: https://doi.org/10.1145/3457388.3458673 

Abstract: There is no doubt that the body of knowledge spanned by the computing disciplines has gone through an unprecedented expansion, both in depth and breadth, over the last century. In this position paper, we argue that this expansion has led to a crisis in computing education: quite literally the vast majority of the topics of interest of this conference are not taught at the undergraduate level and most graduate courses will only scratch the surface of a few selected topics. But alas, industry is increasingly expecting students to be familiar with emerging topics, such as neuromorphic, probabilistic, and quantum computing, AI, and deep learning. We provide evidence for the rapid growth of emerging topics, highlight the decline of traditional areas, muse about the failure of higher education to adapt quickly, and delineate possible ways to avert the crisis by looking at how the field of physics dealt with significant expansions over the last centuries.

Presentation: https://youtu.be/gjw9dRWaeNM

Citation: C. Teuscher, “A golden age for computing frontiers, a dark age for computing education?” In Proceedings of the 18th ACM International Conference on Computing Frontiers (CF ’21). Association for Computing Machinery, New York, NY, USA, 140–143, 2021. DOI: https://doi.org/10.1145/3457388.3458673

Paper acceptance rate: 25%

 

NEW PAPER: Computational Capacity of Complex Memcapacitive Networks

D. J. Tran and C. Teuscher. “Computational Capacity of Complex Memcapacitive Networks,” Journal of Emerging Technologies in Computing Systems, 17:2(17), 2021, doi: https://doi.org/10.1145/3445795