Reed's Ruminations: A Blog by Dan Reed

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.

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.

Lessons from the Gulf of Mexico

N.B. My apologies for the absence of recent posts. It's been a bit busy here in HPCDan's world.

What can we learn from the oil well leak in the Gulf of Mexico? That deep water drilling is more common than most people realized? Yes, because much of the near surface oil has already been extracted. That a complex engineering tasks, such as capping an oil well one mile below the ocean's surface, are difficult? Yes, that is why we call them complex. That all engineering projects involve risks? Yes, for all designs involve tradeoffs and error margins. That our civilization is overly dependent on petrolem as an energy source? Yes, but we will forget that lesson again. That nature is not infinitely resilient to human folly? Yes, though we still struggle to believe it. That local events have global consequences? Yes, but we should know that already.

Coupled Models

Though all of these rethorical questions have answers in the affirmative, I suspect all of us in computational science looked at the disaster in the Gulf and saw something else – a devilishly complex multidisciplinary system with emergent behaviors across a wide range of temporal and spatial scales. We saw a modeling problem whose complete solution lies beyond our current reach at a time when we all hoped to be of more help.

Let's begin with the reservior models of subsurface attributes (e.g., porosity, permeability and water mobility) that guide selection of drilling sites. Obtaining accurate numerical solutions from the resulting discretizations remains a challenging parallel computation. In some larger sense, however, this is the easy, relatively well understood problem.

Next up are the computational fluid dynamics (CFD) models of oil dispersion by wind and waves, and their interactions with shallow water features and barrier islands. Turbulence and mixing, viscosity, dispersants, complex geometries and multiple time scales expose the theoretical and practical limits of our parallel, numerical solutions to the Navier-Stokes equations. But we are just getting started.

Next come biophysics models and interactions among surface illumination, bacterial breakdown of oil droplets, and the effects of effulent driven ocean deoxygenation. These coupled models must be embedded in the CFD models to produce time varying estimates of microbiological effects.

Now it is time to get serious.

Next up are the species-specific models of petroleum ingestion and interaction, the effects on organism behavior and longevity, individual population dynamics and their interplay in the food chain. These are followed by long-term ecological models of wetlands and marine estuaries and statistical models of the possible benefits and adverse effects of human remediation. Finally, we reach models of human physiology and public health. These are followed by a host of societal and economic models, ranging from fishing and tourism through the global energy markets to the broader economic markets.

Solution Methodologies

Even the most cursory examination of the models and problems enumerated above exposes three very sobering truths. First, our ability to couple discrete and continuous models, each with their own approximations and limitations, across such enormous temporal and spatial ranges pales before the magnitude and complexity of this problem. We can only solve pieces of it. Second, even given the numerical tools, we lack the software engineering and programming methodologies to assemble, test and verify an integrated solution. Third, even given the first two, the computational demands of an integrated, fully multidisciplinary, parametric simulation study of the oil spill and its effects would make accurate climate modeling seem like child's play on an abacus by comparison.

Let us be humbled by what we cannot do and challenged by how far we have to go. We have much work to do. These are computational science problems of the very highest order.

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.

Research Summers

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

I was a young assistant professor, attending an extended family event where one meets cousins, nieces, nephews, wives, husbands and people whose familial relationship you only vaguely understand. It was one of those times when one relaxes, enjoys family and makes small talk with people one sees only at such events. It was wedding or a holiday – I no longer recall the location or the details. One memorable aspect of the event remains, however.

A member of my extended family asked if I would be teaching during the upcoming summer. "No, I do not teach classes during the summer," I replied. A perplexed expression then played across the questioner's face, with the implied successor question very clear. What, pray tell, does a professor do in the summertime?

Lest I be viewed as an ivory tower dilettante with a questionable work ethic, I hastened to explain that I spent the summer devoting all my time to research. In turn, this prompted another question, "What does that mean?"

Realizing that I should have anticipated that response, I paused to consider my reply, with images of university life flashing through my mind. Did I explain grant proposal preparation and annual reports? Should I describe meetings with graduate students and reviewing thesis drafts? Or were descriptions of research conferences and hallway discussions more apt? Might recourse to memories of secondary school lectures on the scientific method and experiments do the trick? No, I decided. A more personal answer was required.

"In the summer, the government pays me to think. If I believe I have a good idea, I am supposed to write it down," I finally responded. It was the truth, encapsulating the fact that I had active research grants and that one of the tangible outputs of research is scientific publication. Yet, it was also a bit flippant, for it failed to capture all the nuance and complexity that academic research truly entails: the thrill of discovery, the camaraderie of a research team, the friendly intellectual competition, and the long hours of hard work.

After a moment's reflection, the family member smiled and asked jokingly, "How do I get one of those jobs?" "It's harder than it sounds," I replied.

Indeed it is. I am older and (I hope) a bit wiser now, and I would have given a much more nuanced answer. Research challenges us to pose new questions, choose research avenues carefully, and be frugal and wise stewards of the financial trust invested in us. It means nurturing each new generation of students in the ways and methods of discovery, shaping national and international research agendas, and serving society by transferring promising ideas. Finally, it means explaining why research is important.

And yes, it means thinking clearly about the future.