Broad agenda-setting

During the transition period to the Obama administration, we had the opportunity to feed a number of “white papers” into the transition team’s planning process.  Thanks to the receptiveness of the incoming administration, these white papers had impact far beyond what we had dared to imagine.

Our approach was to focus on the fact that fundamental advances in computer science and computer engineering are essential to meeting the nation’s challenges and achieving the nation’s priorities.  America’s energy future, from transportation to the smart grid, depends essentially on fundamental advances in computer science and computer engineering.  Ditto for the transformation of health care.  Ditto for the future of education.  Ditto for 21st century data-driven discovery — “eScience” — which will be transformational, ubiquitous, and driven by fundamental advances in computer science and computer engineering.

This approach does not position our field a “tool” of other fields, because it is not about applying today’s technology.  Rather, it focuses on the fundamental advances in computer science and computer engineering that will be necessary to meet the nation’s challenges and achieve the nation’s priorities.

This work was done pro bono by a small number of people.  (Committees produce consensus; leaders produce visions.)  And it was carried out as what computer architects would call “speculative execution” — effort devoted in the belief that it might prove to be useful.  (If you wait until someone asks you for something, it’s too late — you need to have it ready!)

Focused agenda-setting

The CCC funds workshops initiated by members of sub-fields who want to chart a future direction.  Some of these have been hugely influential.

A great example is a robotics effort led by Henrik Christensen (Georgia Tech), Vijay Kumar (Penn), Matt Mason (CMU), and others.  This broad community effort, carried out over a period of 18 months, yielded a coherent direction for fundamental research in robotics, a set of “research roadmaps” for the field, and a white paper that is likely to result in a significant federal research initiative during the next fiscal year.

Computing Innovation Fellows

During the 2008-09 academic year it became clear that, due to the economic downturn, many extremely strong Ph.D. graduates would “exit the research game” due to lack of employment opportunities at universities and industrial research labs — sacrificing the nation’s investment in their education, and jeopardizing the nation’s future competitiveness.

Computer science had never had a broad-based coordinated postdoc program, but the Computing Community Consortium, working closely with NSF, was able to establish the Computing Innovation Fellows Project in remarkably short order — from concept to awards in less than six months.  It was NSF’s confidence in CCC as a “proxy” for the computing research community that made this possible.

The CIFellows Project had several unique aspects that we expect to have broad impact.  The first was the “max 2 rule” — at most two awardees were allowed to come from, or go to, any one institution.  (The goal was to establish persistent interactions between diverse institutions.)  The second was an ordering of the holistic quality assessment of candidates:  at each iteration (as the field was reduced from 500+ proposals to 60 awards), members of under-represented groups (women, minorities, particular research areas, etc.) were discussed first.  When the dust had settled, 42% of CIFellows awardees were women!  (To be clear:  gender only influenced the order of discussion!)

Summary

There’s lots more to say, but this is getting long for a blog post.  The bottom line is that a group of community-oriented research leaders can have a profound effect, given the endorsement (confidence and good will) of the research community, and the right environment in Washington.

There are many, many ways in which you can participate.  See the CCC web page for ideas!

Metagenomics is a relatively new field that seeks to understand the
structure and function of the shockingly large number of microorganisms on
our planet.  New technologies permit us to now sequence samples taken from
their environment rather than only those that are cultivated in the lab. For
example, Craig Ventner’s Global Ocean Sampling Expedition has collected water throughout the world’s oceans, captured organisms, and sequenced their DNA. In the initial pilot study alone, nearly 150 new bacteria were discovered through this process.

The science and computing challenges are huge. A single gram of soil
contains approximately one trillion base pairs of DNA. Scientists at the National Institutes of Health recently compared over 100,000 bacterial gene sequences on the human skin and discovered a far larger number of different bacteria living on human skin than had been previously known (Science, May 28, 2009). Sequencing and making sense of these data introduces new computational problems, not merely slight extensions of existing ones.

The potential impacts of understanding these data are huge as well. In the
case of soil, microbial communities have an impact on carbon sequestration
and understanding them may help us with cleaning toxic waste. In our bodies,
microbial cells are estimated to outnumber our human cells by a factor of
ten to one and are important in protecting our skin, digestion, and much
more. Understanding these large microbial communities is therefore likely to
have a positive impact on human health. The NIH has launched the Human
Microbiome Project to support work in this field.

Complete DNA sequences of thousands of organisms are piling up in databases
because of the efficiency of DNA sequencing technologies. Most of this
remains unanalyzed for several reasons. We don’t yet know the right
biological questions to ask. We don’t have all the clever programs that
would actually ask these questions of the computer. And there is now so much
data that many questions totally overwhelm even existing high performance
computers.

Among the computational challenges in this field are the design of new
algorithms and cloud computing technologies. In the National Academies of
Science publication “The New Science of Metagenomics: Revealing the Secrets
of our Microbial Planet”, the authors conclude “What then, will metagenomics
have become, in 20 years? We believe that it too will be a concept-driven
computational science… We can expect, in 20 years, enormous advances on
three fronts – technical, computational, and biological – as well as a host
of specific applications.”

We encourage our community to explore and engage in this and other emerging
fields at the crossroads of biology and computation. This is one of the
exciting areas for 21st century computing.

Contributed by Bill Feiereisen with assistance from Ran Libeskind-Hadas

Could prizes be useful to the broader computing community in advancing research?  The Clay Mathematics Institute established the Millenium Prizes in 2000, offering $1 million for the solutions to each of seven famous open problems, including the question of whether P=NP.  It’s hard to imagine that many researchers have decided to shape their research agendas based on the existence of this prize.  On the other hand, Wolfram Research sponsored a $25,000 prize, with a blue ribbon prize committee, to determine if a specific small (2 states and 3 symbols) Turing Machine is universal. The problem was solved (in the affirmative) in 2007 by a 20-year-old from Birmingham, England.

There is a rich history of prizes for technical innovation.  In the early 18th century, the British Parliament offered the Longitude Prize for a practical method of precisely determining a ship’s longitude, with different monetary amounts depending on the accuracy of the instrument.  The rules were changed during the course of the competition and the prize was never awarded.

More recently, there have been numerous technical prizes such as the $10 million Ansari X PRIZE for carrying three people to 100 kilometers above the earth’s surface.  Following on the success of the Ansari Prize, The X PRIZE Foundation has established several other major prizes for specific achievements that have “the potential to benefit humanity”.

Are there some major problems in computer science that could be incentivized by prizes – financial or otherwise?  What are the potential benefits and risks of this approach?  We’re eager to hear your thoughts.

Some good additional readings include the following:

• Two articles at Slate Magazine, one on the Netflix prize and one on the use of prizes for innovation in the pharmaceutical industry.
• A scholarly paper on the subject by Stephen Maurer and Suzanne Scotchmer.
• A report from the National Research Council on Innovation Inducement Prizes at the National Science Foundation.

The third meeting will likely be scheduled for late October, though draft vision/consensus documents are being crafted before this next meeting.  The meeting will held at IBM in Austin, Texas, and will engage leaders from funding agencies as part of the program.

It’s certainly exciting to see core computer science issues featured so prominently in the press! Indeed, this article has generated quite a bit of discussion in the research community. For example, while acknowledging that a new network architecture would certainly play an important role in improving security, Purdue’s Gene Spafford writes on his CERIAS blog, “Do we need a new Internet? Short answer: Almost certainly, no.” (Gene tells me that he will be interviewed on this topic on C-SPAN’s Washington Journal, airing at 9:30am on Saturday, February 21.) UCSD’s Stefan Savage is largely in agreement, saying that “the network is by and large the smallest part of the security problem” and that “at a technical level the security problem is really an end-host issue, coupled with an interface issue — lots of power given to lots of different pieces of software whose couplings present opportunities to bad guys that aren’t anticipated, at a social level its a human factors issue.” The bottom line is that, outside of resource management (that is, controlling DDoS) and attribution/accountability, the main sources of security risk are at the end points — a key point missed in the NY Times article. Peter Freeman perhaps puts it most plainly:

To be succinct, although technical improvements are clearly needed, a large part of the security issue comes back to people, not technology. If we could figure out how to educate people so they don’t respond to pleas from Nigerians who need to transfer money or they don’t leave their passwords on post-its or never install the frequent security patches that are issued, we could make huge improvements immediately.

That’s not to say, however, that reinventing some aspects of networking isn’t an important research goal. Peter Freeman, while he was the director of NSF’s computer science (CISE) division, was instrumental in helping to launch the GENI Project in 2004, with the goal of developing an experimental platform for exploring truly reliable and higher capacity networks. For Freeman and others, new approaches to networking were deemed an important area for government investment because of the basic nature of the research problems involved.

Mounting a global-scale effort such as GENI has been a major challenge for the computing research community, perhaps similar to what the astronomy community goes through when it decides to develop large telescopes. But the initiative has already had several ripple effects. Guru Parulkar, who was the NSF program manager for GENI at the start, went to work with Nick McKeown and helped start the Clean Slate Project mentioned in the NY Times article. The GENI effort also put Princeton’s Larry Peterson in the middle of things, as the PlanetLab Consortium was one of the most influential early inspirations for GENI. And now, a much broader visioning effort in Network Science and Engineering, or NetSE, supported by the Computing Community Consortium (CCC), is defining the critical research questions in a wide range of network-related areas.

As for GENI itself, significant progress on development of a prototype has been made, coordinated by a GENI Project Office (GPO) and involving a large number of academic researchers. BBN’s Chip Elliott says that a version of the testbed will be available for early testing in a matter of months, “which will allow researchers to investigate many core networking research questions, some of which are the thorniest questions for Network Science and Engineering, upon the earliest end-to-end prototype of GENI.” Ellen Zegura, Georgia Tech professor and NetSE Council Chair, cites the importance of this development, saying “For me, the deep technical issues of security and privacy are at the heart of the GENI effort, and one of the main reasons for developing it.”

The demand for better security grows with the public’s dependence on computing and networking. As Chip Elliott states:

Would our lives improve if all aspects of the Internet were firmly bound to real-world personal and organizational identities? Might total public transparency reduce crime and misbehavior – in short, might less privacy lead directly to more security? Is privacy already a vanishing concern, fated to disappear in a few years without widespread regret?

Careful thinking will illuminate these issues — particularly if coupled to a vigorous program of experimentation.

This, in a nutshell, is what the NetSE and GENI initiatives aim to address.

– Peter Lee

For example, LIGO (Laser Interferometer Gravitational-wave Observatory) is designed to detect ripples in space-time caused by changes in very large masses (e.g., a star exploding). Such observations, if made successfully, would finally confirm Einstein’s prediction of the existence of gravitational waves. LIGO has a construction cost of about $300M and annual operating costs of more than $30M. Justifying the construction of such an instrument requires an exceptionally compelling science case and a disciplined approach to construction management. Just as important is the need for the scientific research community (astrophysics, in the case of LIGO) to show deep understanding and broad support for the investment.

Is the computing research community in need of such large-scale instrumentation? For the CCC, this question has been a major topic of discussion, instigated initially by the development of the GENI (Global Environment for Network Innovations) concept. The current concept of GENI involves a global experimental network that would support Internet-scale experimentation with new transport technologies, networking protocols, and security mechanisms. GENI, if successful, would not only answer fundamental scientific questions about the behavior of global-scale networks, but also provide design guidance for the future Internet.

Time will tell whether GENI or other computing research concepts will develop into viable MREFC candidate projects. But in the mean time, it is instructive to study how other research communities develop the broad support that is needed in order to make a case to the NSF and the National Science Board.

One of the cornerstones of the whole process is a document that lays out the science case, or science requirements, for the instrument. It is instructive, then, to take some time to study such documents. One of the most recent successful MREFC proposals is for the LSST (Large Synoptic Survey Telescope), a new telescope projected to have a construction cost between $250M and $350M and scheduled to become operational in 2014. In my view, for anyone in the computing research community, it is well worth the time to study the LSST Science Requirements Document. It provides a window into the kind of audacious yet focused investigation that is used to justify such huge science investments. LSST also involves significant data management and computing problems which may be of strong relevance to computing research.

A brief excerpt from one of the founding papers on LSST explains the goal of the instrument as follows:

We describe the most ambitious survey currently planned in the visible band, the Large Synoptic Survey Telescope (LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a large, wide-field ground-based system designed to obtain multiple images covering the sky that is visible from Cerro Pachon in Northern Chile. The current baseline design, with an 8.4m (6.5m effective) primary mirror, a 9.6 sq. deg. field of view, and a 3.2 Gigapixel camera, will allow about 10,000 sq.deg. of sky to be covered using pairs of 15-second exposures in two photometric bands every three nights on average, with typical 5-sigma depth for point sources of r=24.5. The system is designed to yield high image quality as well as superb astrometric and photometric accuracy. … These data will result in databases including 10 billion galaxies and a similar number of stars, and will serve the majority of science programs.

The document lays out the science case and from this derives the requirements on the instrument. The science case is built around four themes. The first theme, on dark matter and dark energy, directly addresses what the National Academies recently identified as one of the “most important scientific questions of our time.” Two other themes, to “explore the transient optical sky” and to “map the Milky Way”, speak to general facilities needs of the astronomy research community. And the fourth theme, on “taking an inventory of the Solar System”, addresses the practical problem of keeping track of asteroids that might “ultimately strike the Earth’s surface.”

The small number of themes and their clear, concise explanation (each one is described in about a single page of text) makes it possible to derive a clear set of requirements on the telescope. Importantly, it also gives a clear basis for disseminating the concepts to the research community, thereby encouraging more informed debate and consensus-building.

In the LSST Science Requirements Document, the high-level requirements are given in terms of a series of design specifications, with both minimum and stretch goals in each case. The level of specificity, particularly in a rather short (about 30 pages) document, is impressive.

Interestingly, one area where the document falls short is in the final section on “Data Processing and Management Requirements”. This is left essentially as a stub, for a yet-to-be-published separate document. The LSST is projected to produce about 100TB/week of image data, and the design requirement is for “snapshots” of the data to be fixed and published annually, to support repeatability of experiments. Yet to be specified is the manner in which up-to-the-minute data is disseminated, organized, and accessed. Certainly these are issues that computer scientists will be interested in and are likely to be well-equipped to answer. We should all look forward to contributing to this part of the LSST effort.

So what does this all say about efforts such as GENI or other future computing-related instrumentation proposals? For one thing, it is probably important to have a set of crisply stated science questions. What is GENI’s analogue to “constraining dark energy and dark matter”? Writing the science case with a level of focus and simplicity that the entire computing research community can understand and accept is also crucial. And, finally, it must be possible to derive, fairly directly, at least a high-level set of design requirements from the statement of the science case.

Computing research is both important and wonderful because it combines fundamental science, hard-core engineering, and practically useful technology all together to an extent that is unique in academic research today. Whether we will find compelling needs for MREFC-scale instrumentation is still an open question, but I have no doubt that if/when we do, that a successful case can be made to fund it.