Portland State computer engineer Christof Teuscher inspired by biology, keen on collaboration across disciplines

Inspiration for new technologies often can be found in biological systems. A classic example is how plant burrs sticking to an engineer’s dog led to the creation of Velcro.

Following in such traditions is Christof Teuscher, an associate professor in Portland State University’s electrical and computer engineering department.

Christof Teuscher (Photo courtesy of Portland State University)

Christof Teuscher (Photo courtesy of Portland State University)

Teuscher directs the eponymous Teuscher lab, which investigates nontraditional computing architectures that are inspired by biological and other systems (nanoscale and nonlinear dynamic ones).

He also recently was named the Maseeh Professor of Electrical and Computer Engineering in PSU’s Maseeh College of Engineering and Computer Science, a posting that will last for five years. The Maseeh professorship was created in 2004 to “recognize distinguished scholarship in the Maseeh College and to provide resources to further academic excellence,” according to PSU staff.

Science In Portland caught up with Teuscher and pitched him seven questions for our “Profiles in Science” Q&A. Here’s what he had to say about his background, his research and his interests in engineering:

Susannah L. Bodman1. Your field of STEM involves electrical and computer engineering, with interests in biological computing. How did you find your way into those fields and into science and engineering in general?

I’ve always had a keen interest in inter- and multidisciplinary research, mainly out of necessity. I realized as early as during my master’s thesis that the problems that I was interested to solve could not be solved by a single discipline and by myself alone.

Collaborating and going across disciplines is also great for learning to think differently, to think outside of the box. For example, when you work with folks in other disciplines, you learn to appreciate their own view of the challenges, their own terminology and their own way of thinking. It can be an eye-opening experience.

The best collaborations are the ones where there is trust and a mutual interest in each others’ knowledge, expertise and thinking. There are the situations where a team can be much bigger than the sum of its parts.

2. What is the focus of your research, and what new projects are you working on right now?

The current research focus, in a nutshell, is to “reboot” computing and redefine its frontiers. What all our major projects have in common is that we redefine the foundations of how we build and program computers. Nothing looks and is similar to the traditional silicon-based CMOS computers that are all based on the so-called von Neumann architecture, an architecture that goes back to 1945.

For example, instead of transistors, biochemical components are used that serve as information carriers and computational primitives. Such building blocks can only be used once before they dissolve. So one specific challenge consists in building a reliable computer out of unreliable components that dissolve as you use them.

We have a portfolio of very exciting research projects that all have the underlying goal to redefine the frontiers of computing.

Most projects are collaborative by nature because solving big problems today requires expertise from not just one but many areas. For example, we collaborate with teams from the University of Michigan that fabricate actual memristive crossbars. Only a few teams worldwide can currently do that successfully.

We also collaborate with teams from Columbia University and the University of New Mexico in the area of biomolecular computation. They have wet labs that allow them to fabricate some of the systems we designed and simulated on our computational grid.

3. In your recent research endeavors, what have you learned so far  that’s excited you? What have you found most challenging in the work?

It’s a long list of things that we learned, and we learn new things every day. That’s the beauty of science. Sometimes we are right about what we think we may find out, but almost always we are wrong.

Science provides us with a perfect tool to revise our worldview and to learn more about what is true and what is not, what is possible and what is not.

The part about science that is both frustrating and exciting is that you are never done. There are always new questions and open problems. The saying that the more you know, the more you know that you don’t know, is definitely true. I think that applies particularly to biological systems, the brain, consciousness, etc.

Also, you have to be extremely frustration-tolerant. Knowing that most of your work will not actually work, that most of your papers will be rejected and that most of your grant applications will not be funded can be hard to accept. But in general, the joy of discovering something new, to redefine existing boundaries, to push the limits that one thought were not flexible, outweighs the drawbacks.

4. In the area of biological computing, you look to biological systems to push the boundaries of traditional computing systems, but biological systems, being probabilistic and prone to exceptions, can at times be frustrating to work with (as anyone who’s ever tried to get competent bacteria to take up a gene insert with high efficiency would know). On the other hand, biological systems also can be fascinating to study or work with, too. What frustrates and fascinates you with these systems and trying to develop reliable computing systems based of them?

The fascinating part is that you have to think very much outside of the traditional box of designing computers. For example, it turns out that many of the not-so-desirable properties of a biological system, such as the probabilistic behavior, the proneness to errors, etc., can actually be harnessed to perform computations differently, and often even better.

There are computing paradigms that rely on the probabilistic and nonderterministic behavior of a system. Up to a certain level, the more the better. Sometimes you have to give up control in order to gain control.

I think the biggest challenge is to forget about what you knew about computing and to pretty much start from scratch. That’s also a very exciting endeavor obviously.

The frustrating part is that things take longer with these cutting-edge research projects. We often don’t have the right tools, so we have to spend a lot of time and effort on developing our own tools.

5. How do you go about overcoming any of the unique challenges you find in biological computing?

I think the entire biocomputing area is still very much in its infancy. It’s extremely challenging to engineer larger scale systems with more than a few hundreds of logical functions. That tells me that we are either doing something wrong, or this is not the best path forwards.

Maybe, instead of engineering these systems in a top-down way, as we do with traditional silicon-CMOS technology, we should more look into bottom-up fabrication, as nature uses it.

Another example are machines that can adapt and learn as opposed to highly specialized machines that you build for performing a single task. Engineering such a biological computer may take years, but if you can instead build a biological machine that learns and adapts to different tasks, you’ve gained a lot.

That was the purpose of one of our recent projects. We proposed a biological learning system that can be reprogrammed and trained. The work also was featured by renowned science writer Philip Ball.

6. Describe any public exposure you’ve had for your work. What has been the public or media reception or perceptions of your research? How has that thrilled or frustrated you? Any misconceptions you found that you’d like to clear up?

I love to give talks to a general audience or to students. For example, I’ve given several presentations to high school students over the last years.

What I noticed is that people are generally very skeptical, if not scared, of research that deals with biological components. People think about Frankenstein and playing god rather sooner than later. But most of the fears are rather irrational and based on simple ignorance.

Whether you want it or not, mutations, which can be good or bad, happen all the time in biological organisms. And yes, in organic food, too. Evolution hasn’t stopped.

As opposed to evolution, science brings a much more controlled and directed method to table. That’s generally a good thing.

Also, it’s hard for me to see why people are against genetic engineering, but they forget that their very own dog has been selectively bred for hundreds of years. How is genetic engineering “playing god” but not breeding? It’s just a different tool.

Or people may be against genetic engineering and stem cells, but they would be the first to ask for gene therapy when some terminal illness strikes them. How is that ethically compatible?

So overall, it’s important to clarify some of these misconceptions and to emphasize that bio-inspired and genetic engineering have enormous potential for the future of mankind.

7. How do you like to connect to the science community around Portland? (“Community” can include peers in your field, other scientists and science fans in the general public.)

Portland has a number of talk series, such as the OMSI Science Pubs, TEDx, which attract a very broad and engaging public. But science is more often global than local. While giving talks to the local community is an important part, having a solid global online presence is key in today’s world. It’s also equally important to travel to international conferences and workshops to learn about new things, share the research and connect with people.


For more about Teuscher and his work, see PSU’s “Beyond Moore’s Law” Q&A profile.

If you’d like to be featured in one our “Profiles in Science” posts, email your responses to the questions we posted earlier on this blog, along with a photo of yourself, to sciwhat@gmail.com, and we may choose to feature you on this blog.

Susannah L. Bodman
Twitter: @Sciwhat
Facebook: Sciwhat.Science

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