Reed's Ruminations: A Blog by Dan Reed

Explaining Science and Engineering

N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog.

The human suffering caused by the recent earthquake, tsunami and nuclear reactor damage in Japan have saddened us all. As we watch the ongoing struggle to cope with the complexities of a multifactor crisis, we can offer technical and logistics assistance where appropriate, extend emotional and social support to our friends and colleagues, and draw insights, as we always do, for possible responses to future disasters. Each such challenge reminds us that the interplay among complex, dynamical systems is subtle and deep, and that chaos, not just in its popular definition, but in its deep mathematical sense, is ever present, lurking in the shadows, aided by its old friend entropy.

Public Communication

Beyond generating an outpouring of international concern and highlighting the interconnectedness of our society and our shared humanity, the events in Japan have also thrust a plethora of scientific and engineering terms into the international news media and public discourse: tectonic plates, subduction zones and fault planes in the lithosphere; tsunamis, fluid dynamics and wave shoaling; nuclear fission, criticality, neutron moderation and half-lives; atmospheric dispersion, the jet stream and diffusion processes.

As I have read the international newspapers, watched the television reports, listened to the radio interviews and scanned the web sites, I have been struck by the challenges each news organization has faced in explaining technical concepts in intuitive and readily accessible terms. Whether it be explaining logarithmic earthquake scales (e.g., the Richter scale), where a unitary delta corresponds to an order of magnitude difference in magnitude; radioactivity and half-lives, where exponential decay reduces the quantity of material; or the subtleties of dose equivalent radiation exposure measurements, Sieverts and food life cycles, successful explanations depend on both the knowledge of the reporters and of the audiences.

All too often, I have heard inaccurate descriptions of scientific processes or hyperbolic assessments of risks, neither grounded in facts or statistics. To be sure, explaining complex concepts is not easy. This is, perhaps, a teachable moment, where we can highlight the importance of scientific and engineering literacy across all of society, not just among a cadre of technical experts.

Scientific Literacy

Regardless of what we might hope, no engineering structure, whether a nuclear reactor, tsunami sea wall, or earthquake resilient building can be perfectly safe, fully effective or absolutely resistant. Nor can all possible outcomes be anticipated in a natural disaster. One can only plan and assess, then execute accordingly, drawing on the best practices and knowledge currently available, and then learning valuable lessons from each failure.

Our society depends deeply on scientific and engineering advances – including those in computing – for they are embedded in our communication systems, manufacturing processes and supply chains, food production and processing, logistics and transportation, energy production and environmental interactions, and economic mechanisms. Societal understanding of scientific processes and terminology, as well a shared appreciation for the engineering design balances among costs, functionality and risks are essential to informed debate and decision making.

In turn, that understanding rests on our continued investment and support for STEM (science, technology, engineering and mathematics) education, from advanced technical degrees to general awareness of science and engineering principles among all our citizens. We must continue to make STEM and computing exciting and accessible, for we live in a technological society, with all its incumbent implications.

Meanwhile, I continue to wish my friends and colleagues in Japan the very best. Their struggle is our struggle.

Works in Progress: MIA

N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog.

My colleague and CACM's editor-in-chief, Moshe Vardi, has written thoughtfully and persuasively about the changing nature of the academic publication culture, sentiments I also expressed in an earlier Computing Research News (CRN) column entitled Publishing Quarks: Considering Our Culture. Both of us have bemoaned the rising tide of conference publications at the expense of archival journals. (See Moshe's CACM editorial, Conferences vs. Journals in Computing Research for his perspective.)

In a more recent CACM editorial, Moshe returned to publications and culture, discussing the transformation of informal workshops into what might best be called small conferences, with all the program committees, paper reviewing and formality that implies. Like Moshe, I remember attending an earlier genre of workshops, when I was a graduate student and a young assistant professor.

Workshops: Real Discussion

These earlier workshop venues were an opportunity to meet senior researchers in settings where extended conversations and social interactions were both possible and desirable. The atmosphere was informal, the posted agenda were at best a rough guideline, and the discussions were lively, with no objective other than sharing experiences, trading insights and generating ideas.

There were no published proceedings, nor in many cases did the presentations even involve use of overhead transparencies. Real chalk was sometimes used, and the agenda might well have been mimeographed. I realize these descriptions of meeting technology alone date me, and perhaps label me as a technological Neanderthal. For the record, we did not walk five miles barefoot, in the snow, to the workshop venue, nor was it uphill both ways.

In the spirit of fostering off the record conversations on new research directions, I once helped organize and host a Gordon research conference. It was an experience unlike any other I have had as either a program committee or conference general chair, one I heartily recommend we embrace more often in computing.

If you are unfamiliar with Gordon conferences, most are held at small colleges, with attendees housed in student dormitories. The conferences follow a strict format, with morning sessions, free afternoons for informal discussion, followed by evening sessions and (typically) a social event with liquid refreshment. The conference rules also prohibit publication, quotation or attribution of ideas exchanged at the meeting, and attendees are chosen from applicants to ensure diverse technical perspectives.

Technical Reports: Computing History

In that same spirit, I bemoan the seeming demise of the technical report, long a fixture in academic research circles. The technical report, or TR as it was often called, was a mechanism to record details that were too voluminous for a journal publication. Such details ranged from extended descriptions of the experimental infrastructure used to capture data to detailed derivations of the lemmas and theorems behind a key result. Yes, it may be "obvious to the reader" but it is certainly much clearer if the proofs of the preceding nine lemmas and three theorems are explained in depth. Likewise, an extended description of the research infrastructure increases the odds the experiment might be repeatable, an all too rare event in computing.

The TR was also often used to document the progress and technical context of an extended research project. Reading a series of TRs often introduced one to project rationales, approaches and milestones, as well as the then current technical state of the art. For seminal projects, these TRs now serve as an intellectual archeological dig, documenting the early days of our field.

Like the informal workshop, the TR has gone the way of the passenger pigeon. It is time we extracted a sample of cultural DNA from computing's history and engineered a new generation of contemplative, informal workshops. Perhaps we could even ban wireless devices to ensure participation. After gestation, ideas conceived at those workshops might even find their way into technical reports. Imagine that.

America COMPETES Passage

My Microsoft colleague, Elizabeth Grossman, has posted a thoughtful essay, entitled Investment in Federal Research Enables Innovation. In the essay, she reflects on today's passage of America Creating Opportunities to Meaningfully Promote Excellence in Technology, Education, and Science (COMPETES) Reauthorization Act of 2010 by the U.S. Congress.

The AMERICA Competes Act outlines target funding levels and priorities for the research and education programs at several federal agencies, including the National Science Foundation and the Department of Energy. As Elizabeth notes, "Robust federal support for breakthrough research conducted throughout the U.S. is critical to fueling the ecosystems of government, industry and universities, allowing the U.S. to make discoveries and turn them into products that improve our nation's ability to compete globally."

After initial crafting, the bill faced a long and arduous process that involved many negotiations and compromises. Hence, final passage of AMERICA Competes is a holiday event worthy of celebration.

Technology Transfer: A Contact Sport

It is fall and football season in the United States. We are still playing soccer too – football everywhere else – but that is another story. American football has long been called a contact sport, meaning the players intentionally collide to move the ball toward the goal line. However, it remains a far cry from rugby, which seems indistinguishable from unarmed combat to my untrained eye.

How, one may wonder, does football relate to technology transfer? For that matter, what do I mean by the phrase technology transfer itself? There are broader definitions, but for the sake of discussion, let's define technology transfer as the mechanism via which research ideas are communicated to and adopted by companies and their product groups.

The success or failure of technology transfer depends on many factors, from the personalities and skills of the people involved, through the timing and appropriateness of the offering, to the risks and costs associated with the new idea. No single mechanism is guaranteed to succeed, though there are many mechanisms that are likely to fail.

Sharing the Love

Opening the technology transfer conversation with a product group by disparaging the current product or questioning the intelligence of the developers is unlikely to create a receptive audience for one's brilliant research result, no matter how scintillating and erudite the subsequent description may be. Nor will woeful obliviousness to the history that led to the current product design, for it is important to understand context and competition. Finally, neither will bad timing. Reporting a better way to engineer a product just before it is finished will accomplish nothing other than depressing the product team, though it may provide guidance for the next product generation.

One needs to build a long-term, productive and positive relationship between research and product development. It is about creating mutual benefit, sharing the love.

On the positive side, I have observed a continuum of mechanisms that facilitate the transfer of ideas. None is universally successful in isolation, and even in aggregate they may fail. However, the set is far more powerful than the elements, and all can benefit from true contact sport engagement.

Papers. These are the stock and trade of researchers, whether in academia or in government or corporate research laboratories. Yes, members of many product groups read research papers, but an abstract idea, even with supporting experimental data, is rarely the catalyst for uptake of a new idea. A well-written paper does, however, provide the technical details needed when an idea is ultimately adopted.

Demonstrations. These are the "science fair" instantiations of ideas, the early instances that are little more than mockups of the idea. They work on limited data, are constructed using laboratory rather than product components, and they are unreliable at best. However, their virtue is in allowing non-researchers to see and experience the idea in concrete form. Often, seeing really is believing, and a prototype can be convincing in ways that a paper is not.

Prototypes. Prototypes are the next stage, where the demonstration is sufficiently robust that it can be used routinely by individuals and groups other than the creators. The robustness allows product groups and developers to test the idea in more realistic conditions and contexts, and see how it fares relative to existing technologies.

Near Products. Near products are products in all but name, suitable for shipment with little more than cosmetic changes. Creating a near product can be effective if the product team is interested and willing but understaffed, or if they lack the specific technical skills needed to create a product quality instance of the idea.

Contact Engagement

Papers, demonstrations, prototypes and near products help, and they may sometimes be sufficient to transfer an idea into a product. However, they are pale shadows of the true contact sports, either direct embedding with the product team or long-term engagement and partnership. In the embedding model, researchers become members of the product team, working side by side on the product in the same environment as the product developers. Because the utility of so many new ideas depend on culture and context – things rarely written down – as well as personal trust and connections, contact engagement is often the most successful approach.

Remember, game plans, no matter how elaborate, rarely work without modification. The players on the field innovate and adapt. Technology transfer is a contact sport. It can be contentious at times, but the rewards for successful transfers are enormous, both in a sense of personal accomplishment and pride and in the long-term success of the companies involved.

In Vivo, In Vitro, In Silico

N.B. This essay also appeared on the Communications of the ACM web blog.

On a recent business trip, I reached into my reading bag for the July 2, 2010 issue of the journal, Science. The issue contained a fascinating article by Gibson et al, "Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome," summarizing the work by Craig Venter's team to create an artificial life form. (I had read about the result in the popular press, but I was anxious to read the original work.) The team created a synthetic DNA sequence, assembling it from digitized genome sequence data for Mycoplasma mycoides and transplanted the modified sequence into Mycoplama capricolum, where it drove reproduction of new cells with the modified genetic code.

The process is a fascinating example of the interplay of biology, sequencing technology, algorithms and high-performance computing. Yet as the authors of the Science paper note, it also highlights how little we still understand. No single cellular system has all of its genes understood in terms of their biological roles. Simply put, we do not yet fully understand the function of all the genetic material, nor do we understand the complex interdependencies of differential gene expression during an organism's lifecycle.

Systems Biology Writ Large

This set me to thinking about systems biology and the multidisciplinary challenges inherent in constructing predictive computational models of biological systems, from cellular processes through organism lifecycle to evolution and population dynamics. These "grand challenges" encompass almost every aspect of modern computing, from numerical and symbolic methods through data management and analytics to extraordinarily high-performance computing platforms. The wide dynamic range of temporal and spatial scales of such models, from picosecond first principles molecular dynamics to the geological timescales of environmental shifts, is a full employment act for computational scientists.

In Silico Challenges

For many of us in high-performance computing, the temptation is to lapse into reverie about trans-exascale computing platforms, for predictive, multilevel biological models will be prodigious consumers of computational resources. Yet I would humbly suggest that is the wrong dreamy mediation for systems biology. Instead, the algorithmic, software and educational challenges are paramount.

There are enormous numerical and algorithmic challenges in fusing disparate models, from ab inito quantum chemistry, molecular dynamics, electrostatic continuum models, finite element models (FEM), computational fluid dynamics (CFD) models, and discrete automata models, among others. The software engineering challenges associated with building and maintaining such multidisciplinary codes are also complex, particularly when one realizes they are supported by generations of students, post-doctoral researchers, faculty members and software professionals.

Natural Philosophy Redux

In systems biology, the algorithms and the model couplings are subtle, the intellectual communities are diverse and often disjoint, and the model approximations are often domain specific. (Models are, after all, approximations of reality.) Perhaps most importantly, the educational structures and the social processes needed to inform, educate, integrate and fund biological researchers, engineers, computer scientists, and software developers to address such complex problems all challenge our existing approaches. Yet such integration harkens back to the origins of science as natural philosophy when all aspects of scientific inquiry were coupled.

In many ways, we are attempting to come full circle, from in vivo observations to in vitro experiments to holistic, in silico computational models. The challenges of systems biology – modeling, integration, computational resources and education – and the opportunities – for fundamental understanding of biological processes and the application to develop more efficacious drugs and improved health care are one of the great possibilities of the 21^st century. These are exciting times.