Blending computer science with science and life
By: Raghu Machiraju

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I believe that computing shall be even more pervasive and ubiquitous than it is today and will evolve into a high-impact, use-inspired basic science1.

Over the last seven decades, we have seen the beginning of a major technological revolution, the Information Revolution. Many of us who are middle-aged or part of the Baby Boomer generation have been witness to this growth. The last decade has especially seen computing emerge from its strongholds in engineering and the mathematical sciences and has diffused not only into all corners of academia (biology, social sciences, humanities, etc.) but also more impressively into quotidian life. It is the latter aspect that will drive computer science and provide even more growth opportunities. Witness the growth of companies like Google, which have made “search” and “to google” overloaded and essential terms. The social impact of computing is at an all-time high, and its ubiquity and pervasiveness will only grow. In my opinion, growth will be in democratized iComputing—computing for everybody and by everybody.

We are at the confluence of many perfect storms of opportunity. Processing and storage hardware is cheap and increasingly available in the cloud. Mobile and traditional communication systems and the Internet connect us and our embedded devices, our vehicles, our homes, and our offices into a global matrix. Yet, I believe we have seen only the tip of the iceberg.

Therefore, computer science will be better served if it considers itself to be a broad discipline with myriad interacting components and not just a slew of vertical silos wherein practitioners design and build compilers, operating systems, architectures, and graphics systems, while some propound algorithms and theory. It must also rely on disciplines other than mathematics (e.g., physics, statistics) for intellectual succor. I increasingly believe that computer science is a use-inspired science with a high-impact footprint where many sub-disciplines are applied to solve problems. Use and value will drive the discipline of computer science. Further, in the age of austerity and growth in the developing world, there will be an emphasis on use-based research and development. Still, there will be a place for fundamental and basic work. Consider this: Louis Pasteur, a founding father of modern microbiology, began his quest with the more basic need of preserving food2. I believe that computer science will also head this way—use-driven and use-inspired yet innate to the human experience.

In marked contrast to the early formative years of computer science, which overlapped with the ebb and flow of Cold War funding and patronages, the ecosystem of today is dramatically different. Computer science will not be just driven by the next new hammer that is invented at the behest of high priests or driven by plain curiosity. However, there will be incentives in finding nails everywhere around us, like geocachers, to construct new hammers or adopt existing hammers. Consider Google’s PageRank algorithm. It should be noted that the original and well-touted PageRank algorithm, or hammer, had a purported use. With the passage of a decade, the venerable algorithm must also act as an honest broker, thus bringing in additional constraints to search and a rethinking of the mathematical and algorithmic underpinnings.

I have followed this mantra in my own work. I embedded myself increasingly in the physical and biological sciences by learning the subtleties and nuances of the other sciences and realizing scalable and robust methods and workflows. The interpretable and actionable end-results—vortices in unsteady flow, distinction between dyscalculics from normal subjects, robust subtypes of triple negative breast cancer—are paramount rather than the singular method. Often new basic methods or foundational principles had to be discovered or re-invented within the constraints of the user-science. Robust engineering practices already achieve this two-step tango; computer science will increasingly adopt the same tango as it increasingly addresses the needs of the larger population.

Computational modeling and data analysis will play an important role in defining salient processes and associations that can be stored and processed in portable, scalable, and robust prototypes and systems. These systems will be deployed in a multitude of human-centric applications that in turn parlay human sensory perception and, more usefully, cognition. This is my utopian albeit utilitarian view of computer science.

The role of mathematics and statistics in the age of big data especially cannot be understated. Foundational work in theoretical regimes will continue; the increased emphasis on high-dimensional and probabilistic learning algorithms offers one example. Similarly, 3D printing and novel manufacturing processes could not have become a reality without the pioneering work of geometer Prof. Herbert Edelsbrunner and many others. New systems and programming languages will also be required. The growth of graphical processing units and probability processors (Lyric Labs) will only usher expedited growth in hitherto unexplored application areas. There will always be a need for compilers for all these specialized solutions; the demands of a probabilistic language where a variable carries the semantics of a distribution will require some deliberation. Human-computer interaction will be even more stressed and text/speech/image/video/ processing will be eventually woven into a single tapestry of a user interface.

Applications will form the inter-disciplinary bridges to others in social sciences, engineering, and medicine, and it is through applications that the user plays an important role. Personalized medicine and all the associated advancements in the physical and biological sciences will require a bigger role from computing software and infrastructure, while traditional consumers in enterprise computing, finance, weather prediction, etc., will continue to place even more demands on real-time services and storage.

In academia, there will be more branches and rivulets. Cybersecurity, systems biology, finance, fluid dynamics, medical imaging, neuroscience, etc., already vie for attention from academics in various departments of computer science. E-governance, social media, social innovation, and smart cities will follow, especially spurred by the availability of big data3. Each of these inter-disciplinary growth opportunities will lead to a reexamination of cross-disciplinary fault lines, and new flurries of research will ensue. The exact shape and form of these changes will depend on the local academic, business, and cultural environment.

The delivery of curriculum will also experience a sea change. There is certainly an interest in all things computing; we have seen this in burgeoning enrollments at both university and K-12 levels. Some of the new improvements will be of vocational nature. It is therefore not surprising that one speaks of imparting the three Rs and C to the uninitiated4. Similarly, there will be an increased emphasis on active learning in flipped classroom settings, where students actually engage in problem solving while being part of collaborative teams. This pedagogical approach is increasingly shown to be effective in more general settings5. In many settings, computer science is already taught in this manner and is likely to become the norm.

In closing, it is my opinion that computer science will adopt a utilitarian face and will make even greater forays into interdisciplinary ventures while strengthening all components in a cross-disciplinary fashion. By placing computer science squarely in Pasteur’s quadrant, practitioners can engage in a science that is at once rigorous and very relevant, and in essence enable computer science to play an even more important role in modern life and human civilization.


1 Marc Snir, Communications of the ACM, ViewPoints, Vol. 54, No. 3, March 2011.

2 Stuart Cantrill, Speaking Frankly: The allure of Pasteur’s quadrant, The Sceptical Chymist, July 7, 2013.

3 Data Science for Social Good, 20th ACM SIGKDD Conference,

4 Leah Hoffman, Computer Science and the Three Rs, Communications of the ACM, Vol. 55, No. 12, October 2012.

5 Freeman et al., Active learning increases student performance in science, engineering, and mathematics, PNAS, doi: 10.1073/pnas.1319030111

About The Author

Dr. Machiraju’s interests include visual analytics, modeling, and machine learning, especially as they apply to topics in biology, medicine, and engineering. Over the years he has been working increasingly on problems of computational biology and bioinformatics including the discovery of biomarkers and disease subtypes and the modeling and reconstruction of signal transduction networks.


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