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ANALYZE THIS!: Mathematical modeling gives way to interesting analytics topics

Author comes face-to-face with an uncomfortable but undeniable fact: There is probably not a whole lot more complex technical work in his future.

Vijay MehrotraBy Vijay Mehrotra

One sunny Sunday morning this past October, I realized that it had been exactly 25 years since the day I completed and turned in my dissertation, a not-very-interesting treatise on closed multiclass non-Markovian queueing networks. Impulsively pulling the hardbound volume off of my bookshelf for the first time in years, I found myself thumbing through its pages, struggling to make sense of the notation and equations and proofs. Though parts of the dusty old tome seemed vaguely familiar, on the whole it still seemed to have been written by a complete stranger, someone far more intelligent, disciplined, motivated and passionate about analytical work than the middle-aged man who was trying his best to slog through it.

As it happened, a few days later I found myself attending Homecoming Weekend at Stanford University, where I had been a graduate student. This campus visit provided me with an opportunity to catch up with Dr. Chin Fang, an old friend from graduate school days. Chin is the founder and CEO of Zettar, a start-up company focused on creating high-performance data transfer tools to address what Chin calls “hyperscale data distribution.”

Though I had previously never given this kind of thing too much thought, the problem seemed obvious once Chin had described it to me: When a large amount of data is captured in one location and needs to be stored somewhere at another site, there is a fundamental challenge associated with getting these records – usually distributed across multiple files in various types of data structures – from one place to the next, often across large physical distances. The particular use case that we spent most of our time discussing was a series of experiments in which Zettar and a team of scientists at the Stanford Linear Accelerator (SLAC) used Zettar’s ZX software to transfer petabytes of data across existing network infrastructure at unprecedented speed [1].

But beyond complex field experiments (for example, think about oceanic oil and gas exploration projects, in which data is captured in the field before needing to be quickly transferred to remote on-shore locations for storage and analysis), one can also imagine many similar applications in the increasingly data-intensive and geographically dispersed world of corporate computing. Indeed, the rapid growth in network traffic from both cloud-based software and data-intensive analytics applications makes the hyperscale data distribution problem an increasingly important one. In addition, because of the substantial cost of network capacity (roughly $400,000 per month for a 100-gigabyte connection), making efficient use of this capacity is also a business imperative.

How does Zettar manage to move data at faster speeds while also more effectively utilizing available network bandwidth? From what I could glean from my one-hour meeting with Chin, Zettar’s solution features a modern systems architecture (clustered nodes, new data transfer protocols) that leverages a variety of smart algorithms and design principles. Moreover, even as Chin sought to keep our technical conversation at a high level, it quickly became clear to me that Zettar’s solution has evolved over many long years of experimenting, iterating and learning, the same way my dissertation had.

My meeting with Chin seemed to exacerbate something that had been nagging at me throughout my recent sabbatical from my faculty job. We live in the golden age of the applied mathematician/statistician/data scientist/analyst, who are developing and utilizing tools to create sophisticated technical solutions that are changing the world at an incredibly rapid rate. Because I live and work in the San Francisco Bay Area, at the very heart of the analytics revolution, there is always a certain implicit pressure to be pushing the technical envelope in some way. To stay current, both in research and in industry, there is a never-ending stream of new topics to learn about, including programming languages and packages, modern machine learning methods and advanced optimization algorithms.

Given all of this, I harbored some abstract sense that I should spend my time away on sabbatical “getting more technical.” But that’s not exactly how it worked out. Along the way, I did manage to improve my R and Python skills and get a better handle on basic machine learning concepts, all things that I hadn’t had the chance to learn as a student. But more importantly, I also came face-to-face with an uncomfortable but undeniable fact: There is probably not a whole lot more complex technical work in my future.

Why? Well, to be blunt, it has become increasingly clear that I lack the passion to continue to pursue the types of technical challenges that friends of mine like Chin love to tackle. This is a somewhat painful realization, in large part because my years at Stanford and in Silicon Valley have left me with an implicit value system in which technical work is worthwhile/significant, and that the kind of professional activities that I enjoy – like teaching data literacy and fundamental (read: simple) technical tools and data-driven storytelling skills to non-technical business students – are trivial/frivolous. As such, marching to the beat of my own drummer leaves me feeling increasingly out of step with the noisy parade around me.

Paradoxically, though each passing year finds me less and less technical than I once was, I am also more and more appreciative of my graduate school experience. In part, this is because of the many bright and motivated people like Dr. Chin Fang that I got a chance to meet while I was a student. In addition, in the process of finding a meaty mathematical modeling topic and tackling it in depth, I somehow developed a knack for unearthing and exploring a wide variety of interesting topics within the world of analytics. These are the kinds of stories that I try to share in these columns – and I am somewhat stunned to realize that I have now been doing so for a full 20 years.

Whether you just started reading this column recently or have been faithfully following along since the OR/MS Today days, thanks for joining me on this ride. And who knows what the future holds? To paraphrase Buzz Lightyear [2] from “Toy Story,” “To hyperscale data distribution – and beyond!”

Vijay Mehrotra ( is a professor in the Department of Business Analytics and Information Systems at the University of San Francisco’s School of Management and a longtime member of INFORMS.


  1. For more details, see the recent SLAC technical report at

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