Category Archives: Data Science

Preserving Content for Your Portfolio: Kudos to The Internet Archive

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Preserving Science

I’ve been publishing articles since the last century.

In fact, my first, legitimate publication was a letter to science journal Nature with my then thesis supervisor (Keith Aldridge) in 1987 … that’s 31 years ago . Armed with nothing more than Google Scholar, searching for “aldridge lumb nature 1987” yields access to the article via Nature’s website in fractions of a second. Moreover, since the introduction of Digital Object Identifiers (DOIs) around the turn of the last century (circa 2000), articles such as this one are uniquely identifiable and findable via a URL – e.g., the URL for our Nature letter is http://dx.doi.org/10.1038/325421a0.

In this letter to Nature, Keith and I cite an 1880 publication authored by Lord Kelvin – whom, it appears, is known for fluid dynamics in addition to the temperature scale that bears his name … and, of course, much more! Through this and other citations, Keith and I explicitly acknowledged how the contributions of others enabled us to produce our letter – in other words, we made it clear how we have been able to stand on the shoulders of giants.

In addition to assigning intellectual credit where it is due, this personal reflection underscores the importance of preserving contributions over the long haul – make that 138 years in the case of Kelvin’s 1880 paper. Preservation is a well-established practice in the case of scientific journals, for example, even though it may be necessary to draw upon analog renditions captured via print or microfiche rather than some digital representation.

In self-curating portfolios recently, it’s been made increasingly clear to me that content preservation has not been a focal point in the digital realm.

Digital Properties

Let’s make use of Grid Computing for the purpose of providing an illustrative example. In its heyday, a popular and reputable online magazine was GRIDtoday: “DAILY NEWS AND INFORMATION FOR THE GLOBAL GRID COMMUNITY”. Other than a passing reference in pioneering publisher Tom Tabor’s BIO (you can search for his BIO here), I expect you’ll be hard pressed to locate very much at all regarding this once-thriving online property. Like Grid Computing itself: GRIDtoday, gone tomorrow; RIP GRIDtoday. Of course, Grid Computing Planet (GCP) suffered a similar fate.

My purpose here is not to question those extremely reasonable business decisions that resulted in closing down operations on GCP or GRIDtoday – Tabor Communications, for example, boasts three, prized ‘properties’ as of this writing … one of which (HPCwire) predates the inception of GRIDtoday, and remains a go-to source for all things HPC.

Grid Computing remains an important chapter in my professional life – especially given my claims for genetic imprinting via Distributed Computing. However, based upon my desire to assemble a portfolio of my work that includes Grid Computing, the /dev/null redirection of those bits that collectively represented GRIDtoday and GCP is problematical. In particular, and even though I collaborated upon articles and book chapters that have been preserved in analog and/or digital representations, articles contributed to GRIDtoday and GCP still retain value to me personally – value that I’d like to incorporate into my Portfolio.

Enter The Internet Archive

Fortunately, also since close to the end of the last century, The Internet Archive has been:

… building a digital library of Internet sites and other cultural artifacts in digital form. Like a paper library, [they] provide free access to researchers, historians, scholars, the print disabled, and the general public. Our mission is to provide Universal Access to All Knowledge.

I’m not intending to imply that those items I was able to have published via GRIDtoday and GCP carry ‘a Kelvin of clout’ however, for more than purely sentimental reasons it’s truly wonderful that The Internet Archive has attempted to preserve those artifacts that collectively comprised these publications in their heyday. Although I haven’t yet attempted to locate an article I wrote for GCP, I was able to retrieve two articles from the archive for GRIDtoday:

  • Towards The Telecosmic Grid – Published originally in December 2002, in this article I ‘channeled’ George Gilder is asserting that: “Isolating and manipulating discrete wavelengths of visible light across intelligent optical transport media results in the grid – a specific instance of The Telecosmic Grid. Several examples serve as beacons of possibility.” More on this soon (I hope) in a separate post that revisits this possibility.
  • Open Grid Forum: Necessary … but Sufficient? – Published originally in June 2006, this may be the most-opinionated article I’ve ever had appear in any media format! It generated a decent amount of traffic for GRIDtoday, as well as an interesting accusation – an accusation ‘leaked’, incidentally, through a mailing list archive.

Given that these two GRIDtoday articles are currently accessible via The Internet Archive means that I can include each of them directly in my Portfolio, and update my blog posts that make reference to them. Having laid intellectual claim (in 2002 I’ll have you know!!! 😉 to various possibilities telecosmic in nature, I’ll be able to soon revisit the same through the guise of hindsight. Whereas I fully appreciate that business decisions need to be made, and as consequence once-popular landing pages necessarily disappear, it’s truly fortunate that The Internet Archive has our collective backs on this. So, if this post has any key takeaways, it’s simply this:

Please donate to The Internet Archive.

Thanks Brewster!

Disclosures Regarding My Portfolios: Attributing the Contributions of Others

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‘Personal’ Achievement?

October 8, 2018 was an extremely memorable night for Drew Brees at the Mercedes-Benz Superdome in New Orleans. Under the intense scrutiny of Monday Night Football, the quarterback of the New Orleans Saints became the leading passer in the history of the National Football League. (For those not familiar with this sport, you can think of his 72,103-yard milestone as a lifetime-achievement accomplishment of ultramarathon’ic proportions.) The narrative on Brees’ contributions to ‘the game’ are anything but complete. In fact, the longer he plays, the more impressive this milestone becomes, as he continues to place distance between himself and every other NFL QB.

Of course the record books, and Brees’ inevitable induction into the Pro Football Hall of Fame, will all position this as an individual-achievement award. Whenever given the opportunity to reflect upon seemingly personal achievements such as the all-time passing leader, Brees is quick to acknowledge those who have enabled him to be so stunningly successful in such a high-profile, high-pressure role – from family and friends, to teammates, coaches, and more.

As I wrote about another NFL quarterback in a recent post, like Tom Brady, Brees remains a student-of-the-game. He is also known for his off-the-field work ethic that he practices with the utmost intensity in preparing for those moments when he takes the main stage along with his team. Therefore, when someone like Brees shares achievements with those around him, it’s clearly an act that is sincerely authentic.

Full Disclosure

At the very least, self-curating and sharing in public some collection of your work has more than the potential to come across as an act of blatant self-indulgence – and, of course, to some degree it is! At the very worst, however, is the potential for such an effort to come across as a purely individual contribution. Because contribution matters so much to me personally, I wanted to ensure that any portfolio I self-curate includes appropriate disclosures; disclosures that acknowledge the importance of collaboration, opportunity, support, and so on, from my family, friends and acquaintances, peers and co-workers, employers, customers and partners, sponsors, and more. In other words, and though in a very different context, like Brees I want to ensure that what comes across as ‘My Portfolio’ rightly acknowledges that this too is a team sport.

In the interests of generic disclosures then, the following is an attempt to ensure the efforts of others are known explicitly:

  • Articles, book chapters and posters – Based on authorships, affiliations and acknowledgements, portfolio artifacts such as articles, book chapters and posters make explicit collaborators, enablers and supporters/influencers, respectively. In this case, there’s almost no need for further disclosure.
  • Blog posts – Less formal than the written and oral forms of communication already alluded to above and below, it’s through the words themselves and/or hyperlinks introduced that the contributions of others are gratefully and willingly acknowledged. Fortunately, it is common practice for page-ranking algorithms to take into account the words and metadata that collectively comprise blog posts, and appropriately afford Web pages stronger rankings based upon these and other metrics.
  • Presentations – My intention here is to employ Presentations as a disclosure category for talks, webinars, workshops, courses, etc. – i.e., all kinds of oral communications that may or may not be recorded. With respect to this category, my experience is ‘varied’ – e.g., in not always allowing for full disclosure regarding collaborators, though less so regarding affiliations. Therefore, to make collaborators as well as supporters/influencers explicit, contribution attributions are typically included in the materials I’ve shared (e.g., the slides corresponding to my GTC17 presentation) and/or through the words I’ve spoken. Kudos are also warranted for the organizations I’ve represented in some of these cases as well, as it has been a byproduct of this representation that numerous opportunities have fallen into my lap – though often owing to a sponsorship fee, to be completely frank. Finally, sponsoring organizations are also deserving of recognition, as it is often their mandate (e.g., a lead-generation marketing program that requires a webinar, a call for papers/proposals) that inspires what ultimately manifests itself as some artifact in one of my portfolios; having been on the event-sponsor’s side more than a few times, I am only too well aware of the effort involved in creating the space for presentations … a contribution that cannot be ignored.

From explicit to vague, disclosures regarding contribution are clearly to barely evident. Regardless, for those portfolios shared via my personal blog (Data Science Portfolio and Cloud Computing Portfolio), suffice it to say that there were always others involved. I’ve done my best to make those contributions clear, however I’m sure that unintentional omissions, errors and/or (mis)representations exist. Given that these portfolios are intentionally positioned and executed as works-in-progress, I look forward to addressing matters as they arise.

Ian Lumb’s Cloud Computing Portfolio

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When I first introduced it, it made sense to me (at the time, at least!) to divide my Data Science Portfolio into two parts; the latter part was “… intended to showcase those efforts that have enabled other Data Scientists” – in other words, my contributions as a Data Science Enabler.

As of today, most of what was originally placed in that latter part of my Data Science Portfolio has been transferred to a new portfolio – namely one that emphasizes Cloud computing. Thus my Cloud Computing Portfolio is a self-curated, online, multimedia effort intended to draw together into a cohesive whole my efforts in Cloud computing; specifically this new Portfolio is organized as follows:

  • Strictly Cloud – A compilation of contributions in which Cloud computing takes centerstage
  • Cloud-Related – A compilation of contributions drawn from clusters and grids to miscellany. Also drawn out in this section, however, are contributions relating to containerization.

As with my Data Science Portfolio, you’ll find in my Cloud Computing Portfolio everything from academic articles and book chapters, to blog posts, to webinars and conference presentations – in other words, this Portfolio also lives up to its multimedia billing!

Since this is intentionally a work-in-progress, like my Data Science Portfolio, feedback is always welcome as there will definitely be revisions applied !

Towards Tsunami Informatics: Applying Machine Learning to Data Extracted from Twitter

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2018 Sulawesi Earthquake & Tsunami

Even in 2018, our ability to provide accurate tsunami advisories and warnings is exceedingly challenged.

In best-case scenarios, advisories and warnings afford inhabitants of low-lying coastal areas minutes or (hopefully) longer to react.

In best-case scenarios, advisories and warnings are based upon in situ measurements via tsunameters – as ocean-bottom changes in seawater pressure serve as reliable precursors for impending tsunami arrival. (By way of analogy, tsunameters ‘see’ tsunamis as do radars ‘see’ precipitation. Based on ‘sight’ then, both offer a reasonable ability to ‘nowcast’.)

In typical scenarios, however, advisories and warnings can communicate mixed messages. In the case of the recent Sulawesi earthquake and tsunami for example, a nearby alert (for the Makassar Strait) was retracted after some 30 minutes, even though Palu, Indonesia experienced a ‘localized’ tsunami that resulted in significant losses – with current estimates placing the number of fatalities at more than 1200 people.

With ultimate regret stemming from significant loss of human life, the recent case for the residents of Palu is particularly painful, as alerting was not informed by tsunameter measurements owing to an ongoing dispute – an unresolved dispute that rendered the deployment of an array of tsunameters incomplete and inoperable. A dispute that, if resolved, could’ve provided this low-lying coastal area with accurate and potentially life-saving alerts.

Lessons from Past Events

It’s been only 5,025 days since the last tsunami affected Indonesia – the also devastating Boxing Day 2004 event in the Indian Ocean. All things considered, it’s truly wonderful that a strategic effort to deploy a network of tsunameters in this part the planet was in place; of course, it’s well beyond tragic that execution of the project was significantly hampered, and that almost 14 years later, inhabitants of this otherwise idyllic setting are left to suffer loss of such epic proportions.

I’m a huge proponent of tsunameters as last-resort, yet-accurate indicators for tsunami alerting. In their absence, the norm is for advisories and warnings that may deliver accurate alerts – “may” being the operative word here, as it often the case that alerts are issued only to be retracted at some future time … as was the case again for the recent Sulawesi event. Obviously, tsunami centers that ‘cry wolf’, run the risk of not being taken seriously – seriously, perhaps, in the case when they have correctly predicted an event of some significance.

It’s not that those scientific teams of geographers, geologists, geophysicists, oceanographers and more are in any way lax in attempting to do their jobs; it’s truly that the matter of tsunami prediction is exceedingly difficult. For example, unless you caught the January 2006 issue of Scientific American as I happened to, you’d likely be unaware that 4,933 days ago an earthquake affected (essentially) the same region as the Boxing Day 2004 event; regarded as a three-month-later aftershock, this event of similar earthquake magnitude and tectonic setting did not result in a tsunami.

Writing in this January 2006 issue of Scientific American, Geist et al. compared the two Indian Ocean events side-by-side – using one of those diagrams that this magazine is lauded for. The similarities between the two events are compelling. The seemingly subtle differences, however, are much more than compelling – as the tsunami-producing earlier of the two events bears testimony.

As a student of theoretical, global geophysics, but not specifically oceanography, seismology, tectonophysics or the like, I was unaware of the ‘shocking differences’ between these two events. However, my interest was captivated instantaneously!

Towards Tsunami Informatics

Graph Analytics?

It would take, however, some 3,000 days for my captivated interest to be transformed into a scientific communication. On the heels of successfully developing a framework and platform for knowledge representation with long-time friend and collaborator Jim Freemantle and others, our initial idea was to apply graph analytics to data extracted from Twitter – thus acknowledging that Twitter has the potential to serve as a source of data that might be of value in the context of tsunami alerting.

In hindsight, it’s fortunate that Jim and I did not spend a lot of time on the graph-analytics approach. In fact, arguably the most-valuable outcome from the poster we presented at a computer-science conference in June 2014 (HPCS, Halifax, Nova Scotia), was Jim’s Perl script (see, e.g., Listing 1 of our subsequent unpublished paper, or Listing 1.1 of our soon-to-be published book chapter) that extracted keyword-specified data (e.g., “#earthquake”) from Twitter streams.

Machine Learning: Classification

About two years later, stemming from conversations at the March 2016 Rice University Oil & Gas Conference in Houston, our efforts began to emphasize Machine Learning over graph analytics. Driving for results to present at a May 2016 Big Data event at Prairie View A&M University (PVAMU, also in the Houston area), a textbook example (literally!) taken from the pages of an O’Reilly book on Learning Spark showed some promise in allowing Jim and I to classify tweets – with hammy tweets encapsulating something deemed geophysically interesting, whereas spammy ones not so much. ‘Not so much’ was determined through supervised learning – in other words, results reported were achieved after a manual classification of tweets for the purpose of training the Machine Learning models. The need for manual training, and absence of semantics struck the two of us as ‘lacking’ from the outset; more specifically, each tokenized word of each tweet was represented as a feature vector – stated differently, data and metadata (e.g., Twitter handles, URLs) were all represented with the same (lacking) degree of semantic expression. Based upon our experience with knowledge-representation frameworks, we immediately sought a semantically richer solution.

Machine Learning: Natural Language Processing

It wasn’t until after I’d made a presentation at GTC 2017 in Silicon Valley the following year that the idea of representing words as embedded vectors would register with me. Working with Jim, two unconventional choices were made – namely, GloVe over word2vec and PyTorch over TensorFlow. Whereas academic articles justified our choice of Stanford’s GloVe, the case for PyTorch was made on less-rigorous grounds – grounds expounded in my GTC presentation and our soon-to-be published book chapter.

Our uptake of GloVe and PyTorch addressed our scientific imperative, as results were obtained for the 2017 instantiation of the same HPCS conference where this idea of tsunami alerting (based upon data extracted from Twitter) was originally hatched. In employing Natural Language Processing (NLP), via embedded word vectors, Jim and I were able to quantitatively explore tweets as word-based time series based upon their co-occurrences – stated differently, this word-vector quantification is based upon ‘the company’ (usage associations) that words ‘keep’. By referencing the predigested corpora available from the GloVe project, we were able to explore “earthquake” and “tsunami” in terms of distances, analogies and various kinds of similarities (e.g., cosine similarity).

Event-Reanalysis Examples

Our NLP approach appeared promising enough that we closed out 2017 with a presentation of our findings to date during an interdisciplinary session on tsunami science at the Fall Meeting of the American Geophysical Union held in New Orleans. To emphasize the scientific applicability of our approach, Jim and I focused on reanalyzing two-pairs of events (see Slide 10 here). Like the pair identified years previously in the 2006 Scientific American article, the more-recent event pairs we chose included earthquake-only plus tsunamigenic events originating in close geographic proximity, with similar oceanic and tectonic settings.

The most-promising results we reported (see slides 11 and 12 here and below) involved those cosine similarities obtained for earthquake-only versus tsunamigenic events; evident via clustering, the approach appears able to discriminate between the two classes of events based upon data extracted from Twitter. Even in our own estimation however, the clustering is weakly discriminating at best, and we expect to apply more-advanced approaches for NLP to further separate classes of events.

Agile Sprints - Events - 2017 AGU Fall Meeting - Twitter Tsunami - December 8, 2017

Discussion

Ultimately, the ability to further validate and operationally deploy this alerting mechanism would require the data from Twitter be streamed and processed in real time – a challenge that some containerized implementation of Apache Spark would seem ideally suited to, for example. (Aspects of this Future Work are outlined in the final section of our HPCS 2017 book chapter.)

When it comes to tsunamis, alerting remains a challenge – especially in those parts of the planet under-serviced by networks of tsunameters … and even seismometers, tide gauges, etc. Thus prospects for enhancing the alerting capabilities remain valuable and warranted. Even though inherently fraught with subjectivity, data extracted from streamed Twitter data in real time appears to hold some promise for providing a data source that compliments the objective output from scientific instrumentation. Our approach, based upon Machine Learning via NLP, has demonstrated promising-enough early signs of success that ‘further research is required’. Given that this initiative has already benefited from useful discussions at conferences, suggestions are welcome, as it’s clear that even NLP has a lot more to offer beyond embedded word vectors.

Pencasting with a Wacom tablet: Time to revisit this option

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Around the start of the Fall term in September 2014, I found myself in a bit of a bind: My level of frustration with Livescribe pencasting had peaked, and was I desperately seeking alternatives. To be clear, it was changes to the Livescribe platform that were the source of this frustration, rather than pencasting as a means for visual communication. In fact, if anything, a positive aspect of the Livescribe experience was that I was indeed SOLD on pencasting as an extremely effective means for communicating visually – an approach that delivered significant value in instructional settings such as the large classes I was teaching at the university level.

In an attempt to make use of an alternative to the Livescribe platform then, I discovered and acquired a small Wacom tablet. Whereas I rapidly became proficient in use of the Livescribe Echo smartpen, because it was truly like making use of a regular pen, my own learning curve with the Wacom solution was considerably steeper.

To be concrete, you can view on Youtube a relatively early attempt. As one viewer commented:

Probably should practice the lecture. Too many pauses um er ah.

Honestly, that was more a reflection of my grasp of the Wacom platform than my expertise with the content I was attempting to convey through this real-time screen capture. In other words, my comfort level with this technology was so low that I was distracted by it. Given that many, many thousands of visual (art) professionals make use of this or similar solutions from Wacom, I’m more that willing to admit that this one was ‘on me’ – I wasn’t ‘a natural’.

With the Wacom solution, you need to train your eyes to be fixed on your screen, while your hand writes/draws/etc. on the tablet. Not exactly known for my hand-eye coordination in general, it’s evident that I struggled with this technology. As I look at the results some four years later, I’m not quite as dismayed as I expected to be. My penmanship isn’t all that bad – even though I still find writing and drawing with this tablet to be a taxing exercise in humility. In hindsight, I’m also fairly pleased with the Wacom tablet’s ability to permit use of colour, as well as lines of different thicknesses. This flexibility, completely out of scope in the solution from Livescribe, introduces a whole next level of prospects for visual communication.

Knowing that others have mastered the Wacom platform, and having some personal indication of its potential to produce useful results, I’m left with the idea of giving this approach another try – soon. I’ll let you know how it goes.

Data Scientist: Believe. Behave. Become.

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A Litmus Test

When do you legitimately get to call yourself a Data Scientist?

How about a litmus test? You’re at a gathering of some type, and someone asks you:

So, what do you do?

At which point can you (or me, or anyone) respond with confidence:

I’m a Data Scientist.

I think the responding-with-confidence part is key here for any of us with a modicum of humility, education, experience, etc. I don’t know about you, but I’m certainly not interested in this declaration being greeted by judgmental guffaws, coughing spasms, involuntary eye motion, etc. Instead of all this overt ‘body language’, I’m sure we’d all prefer to receive an inquiring response along the lines of:

Oh, just what the [expletive deleted] is that?

Or, at least:

Dude, seriously, did you like, just make that up?

Responses to this very-legitimate, potentially disarming question, will need to be saved for another time – though I’m sure a quick Google search will reveal a just-what-the-[expletive deleted]-is-Data-Scientist elevator pitch.

To return to the question intended for this post however, let’s focus for a moment on how a best-selling author ‘became’ a writer.

“I’m a Writer”

I was recently listening to best-selling author Jeff Goins being interviewed by podcast host Srini Rao on an episode of the Unmistakable Creative. Although the entire episode (and the podcast in general, frankly) is well worth the listen, my purpose here is to extract the discussion relating to Goins’ own process of becoming a writer. In this episode of the podcast, Goins recalls the moment when he believed he was a writer. He then set about behaving as a writer – essentially, the hard work of showing up every single day just to write. Goins continues by explaining how based upon his belief (“I am writer”) and his behavior (i.e., the practice of writing on a daily basis), he ultimately realized his belief through his actions (behavior) and became a writer. With five, best selling books to his credit, plus a high-traffic-blog property, and I’m sure much more, it’s difficult now to dispute Goins’ claim of being a writer.

Believe. Behave. Become. Sounds like a simple enough algorithm, so in the final section of this post, I’ll apply it to the question posed at the outset – namely:

When do you legitimately get to call yourself a Data Scientist?

I’m a Data Scientist?

I suppose, then, that by direct application of Goins’ algorithm, you can start the process merely by believing you’re a Data Scientist. Of course, I think we all know that that’ll only get you so far, and probably not even to a first interview. More likely, I think that most would agree that we need to have some Data Science chops before we would even entertain such an affirmation – especially in public.

And this is where my Data Science Portfolio enters the picture – in part, allowing me to self-validate, to legitimize whether or not I can call myself a Data Scientist in public without the laughing, choking or winking. What’s interesting though is that in order to work through Goins’ algorithm, engaging in active curation of a Data Science portfolio is causing me to work backwards – making use of hindsight to validate that I have ‘arrived’ as a Data Scientist:

  • Become – Whereas I don’t have best sellers or even a high-traffic blog site to draw upon, I have been able to assemble a variety of relevant artifacts into a Portfolio. Included in the Portfolio are peer-reviewed articles that have appeared in published journals with respectable impact factors. This, for a Data Scientist, is arguably a most-stringent validation of an original contribution to the field. However, chapters in books, presentations at academic and industry events, and so on, also serve as valuable demonstrations of having become a Data Scientist. Though it doesn’t apply to me (yet?), the contribution of code would also serve as a resounding example – with frameworks such as Apache Hadoop, Apache Spark, PyTorch, and TensorFlow serving as canonical and compelling examples.
  • Behave – Not since the time I was a graduate student have I been able to show up every day. However, recognizing the importance of deliberate practice, there have been extended periods during which I have shown up every day (even if only for 15 minutes) to advance some Data Science project. In my own case, this was most often the consequence of holding down a full-time job at the same time – though in some cases, as is evident in the Portfolio, I have been able to work on such projects as a part of my job. Such win-win propositions can be especially advantageous for the aspiring Data Scientist and the organization s/he represents.
  • Believe – Perhaps the most important outcome of engaging in the deliberate act of putting together my Data Science Portfolio, is that I’m already in a much more informed position, and able to make a serious ‘gut check’ on whether or not I can legitimately declare myself a Data Scientist right here and right now.

The seemingly self-indulgent pursuit of developing my own Data Science Portfolio, an engagement of active self-curation, has (quite honestly) both surprised and delighted me; I clearly have been directly involved in the production of a number of artifacts that can be used to legitimately represent myself as ‘active’ in the area of Data Science. The part-time nature of this pursuit, especially since the completion of grad school (though with a few notable exceptions), has produced a number of outcomes that can be diplomatically described as works (still) in progress … and in some cases, that is unfortunate.

Net-net, there is some evidence to support a self-declaration as a Data Scientist – based upon artifacts produced, and implied (though inconsistent) behaviors. However, when asked the question “What do you do?”, I am more likely to respond that:

I am a demonstrably engaged and passionate student of Data Science – an aspiring Data Scientist, per se … one who’s actively working on becoming, behaving and ultimately believing he’s a Data Scientist.

Based on my biases, that’s what I currently feel owing to the very nature of Data Science itself.

Data Science: Identifying My Professional Bias

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Data Science: Identifying My Professional Bias

In the Summer of 1984, I arrived at Toronto’s York University as a graduate student in Physics & Astronomy. (Although my grad programme was Physics & Astronomy, my research emphasized the application of fluid dynamics to Earth’s deep interior.) Some time after that, I ran my first non-interactive computation on a cluster of VAX computers. I’m not sure if this was my first exposure to Distributed Computing or not not; I am, however, fairly certain that this was the first time it (Distributed Computing) registered with me as something exceedingly cool, and exceedingly powerful.

Even back in those days, armed with nothing more than a VT100 terminal ultimately connected to a serial interface on one of the VAXes, I could be logged in and able to submit a computational job that might run on some other VAX participating in the cluster. The implied connectedness, the innate ability to make use of compute cycles on some ‘remote’ system was intellectually intoxicating – and I wasn’t even doing any parallel computing (yet)!

More than a decade later, while serving in a staff role as a computer coordinator, I became involved in procuring a modest supercomputer for those members of York’s Faculty of Pure & Applied Science who made High Performance Computing (HPC) a critical component of their research. If memory serves me correctly, this exercise resulted in the purchase of a NUMA-architecture system from SGI powered by MIPS CPUs. Though isolated initially, and as a component of the overall solution, Platform LSF was included to manage the computational workloads that would soon consume the resources of this SGI system.

The more I learned about Platform LSF, the more I was smitten by the promise and reality of Distributed Computing – a capability to be leveraged from a resource-centric perspective with this Load Sharing Facility (LSF). [Expletive deleted], Platform founder Songnian Zhou expressed the ramifications of his technical vision for this software as Utopia in a 1993 publication. Although buying the company wasn’t an option, I did manage to be hired by Platform, and work there in various roles for about seven-and-a-half years.

Between my time at Platform (now an IBM company) and much-more recently Univa, over a decade of my professional experience has been spent focused on managing workloads in Distributed Computing environments. From a small handful of VAXes, to core counts that have reached 7 figures, these environments have included clusters, grids and clouds.

My professional bias towards Distributed Computing was further enhanced through the experience of being employed by two software vendors who emphasized the management of clusters – namely Scali (Scali Manage) and subsequently Bright Computing (Bright Cluster Manager). Along with Univa (Project Tortuga and Navops Launch), Bright extended their reach to the management of HPC resources in various cloud configurations.

If it wasn’t for a technical role at Allinea (subsequently acquired by ARM), I might have ended up ‘stuck in the middle’ of the computational stack – as workload and cluster management is regarded by the HPC community (at least) as middleware … software that exists between the operating environment (i.e., the compute node and its operating system) and the toolchain (e.g., binaries, libraries) that ultimately support applications and end users (e.g., Figure 5 here).

Allinea’s focuses on tools to enable HPC developers. Although they were in the process of broadening their product portfolio to include a profiling capability around the time of my departure, in my tenure there the emphasis was on a debugger – a debugger capable of handling code targeted for (you guessed it) Distributed Computing environments.

Things always seemed so much bigger when we were children. Whereas Kid Ian was impressed by a three-node VAX cluster, and later ‘blown away’ by a modest NUMA-architecture ‘supercomputer’, Adult Ian had the express privilege of running Allinea DDT on some of the largest supercomputers on the planet (at the time) – tracking down a bug that only showed up when more than 20K cores were used in parallel on one of Argonne’s Blue Genes, and demonstrating scalable, parallel debugging during a tutorial on some 700K cores of NCSA’s Blue Waters supercomputer. In hindsight, I can’t help but feel humbled by this impressive capability of Allinea DDT to scale to these extremes. Because HPC’s appetite for scale has extended beyond tera and petascale capabilities, and is eyeing seriously the demand to perform at the exascale, software like Allinea DDT needs also to match this penchant for extremely extreme scale.

At this point, suffice it to say that scalable Distributed Computing has been firmly encoded into my professional DNA. As with my scientifically based academic bias, it’s difficult not to frame my predisposition towards Distributed Computing in a positive light within the current context of Data Science. Briefly, it’s a common experience for the transition from prototype-to-production to include the introduction of Distributed Computing – if not only to merely execute applications and/or their workflows on more powerful computers, but perhaps to simultaneously scale these in parallel.

I anticipate the need to return to this disclosure regarding the professional bias I bring to Data Science. For now though, calling out the highly influential impact Distributed Computing has had on my personal trajectory, appears warranted within the context of my Data Science Portfolio.

Data Science: Celebrating Academic Personal Bias

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Data Science: Celebrating My Academic Bias

In a recent post, I introduced my Data Science Portfolio. After describing the high-level organization of the Portfolio, I noted:

At the end, and for now, there is a section on my academic background – a background that has shaped so much of those intersections between science and technology that have been captured in the preceding sections of the portfolio.

Even in this earliest of drafts, I knew that I was somewhat uncomfortable with a section dedicated to academics in my Portfolio. After all shouldn’t a portfolio place more emphasis on how my knowledge and skills, academic or otherwise, have been applied to produce some tangible artifact?

Upon further reflection, I currently believe what’s material in the context of a portfolio is some indication of the bias inherent in the resulting curated showcase of one’s work. Of course to some degree the works presented, and the curation process itself, will make self-evident such personal bias.

Whereas it may make sense for an artist not to overtly disclose any bias with respect to their craft, or a curated collection their work, I currently perceive absolutely no downside in sharing my personal bias – a bias that in my own case, I believe reflects only in positive ways on the Portfolio as well as the individual items included in it.

To this end, and in the spirit of such a positive self-disclosure, my personal bias reflects my formative years in science – a background to which I well recall significant contributions from high school, that were subsequently broadened and deepened as an undergraduate and then graduate student. Even more specifically in terms of personal bias was my emphasis on the physical sciences; a bias that remains active today.

As I’ve started to share, through such posts as the one on the mathematical credentials I bring to Data Science, my choice to pursue the physical sciences was an excellent one – even through the self-critical lens of personal hindsight. An excellent choice, but albeit a biased one.

The very nature of Data Science is such that each of us carries with us our own, wonderfully unique personal bias. As we necessarily collaborate in team, project and organizational settings, I believe it’s important to not only ensure each of us preserves their personal bias, but that we leverage this perspective as fully and appropriately as possible. As a consequence it is much more likely that everyone we work with, and everything we work on, will derive maximal value.

Ian Lumb’s Data Science Portfolio

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Ian Lumb’s Data Science Portfolio

I had the very fortunate opportunity to present some of my research at GTC 2017 in Silicon Valley. Even after 3 months, I found GTC to be of lasting impact. However, my immediate response to the event was to reflect upon my mathematical credentials – credentials that would allow me to pursue Deep Learning with the increased breadth and depth demanded by my research project. I crystallized this quantitative reflection into a very simple question: Do I need to go back to school? (That is, back to school to enhance my mathematical credentials.)

There were a number of outcomes from this reflection upon my math creds for Deep Learning. Although the primary outcome was a mathematical ‘gap analysis’, a related outcome is this Data Science Portfolio that I’ve just started to develop. You see, after I reflected upon my mathematical credentials, it was difficult not to broaden and deepen that reflection; so, in a sense, this Data Science Portfolio is an outcome of that more-focused reflection.

As with the purely mathematical reflection, the effort I’m putting into self-curating my Data Science Portfolio allows me to showcase existing contributions (the easy part), but simultaneously raises interesting challenges and opportunities for future efforts (the difficult part). More on the future as it develops …

For now, the portfolio is organization into two broad categories:

  • Data Science Practitioner – intended to showcase my own contributions towards the practice of Data Science
  • Data Science Enabler – intended to showcase those efforts that have enabled other Data Scientists

At the end, and for now, there is a section on my academic background – a background that has shaped so much of those intersections between science and technology that have been captured in the preceding sections of the portfolio.

Although I expect there’ll be more to share as this portfolio develops, I did want to share one observation immediately: When placed in the context of a portfolio, immune to the chronological tyranny of time, it is fascinating to me to see themes that form an arc through seemingly unrelated efforts. One fine example is the matter of semantics. In representing knowledge, for example, semantics were critical to the models I built using self-expressive data (i.e., data successively encapsulated via XML, RDF and ultimately OWL). And then again, in processing data extracted from Twitter via Natural Language Processing (NLP), I’m continually faced with the challenge of ‘retaining’ a modicum of semantics in approaches based upon Machine Learning. I did not plan this thematic arc of semantics; it is therefore fascinating to see such themes exposed – exposed particularly well by the undertaking of portfolio curation.

There’s no shortage of Data Science portfolios to view. However one thing that’s certain, is that these portfolios are likely to be every bit as diverse and varied as Data Science itself, compounded by the uniqueness of the individuals involved. And that, of course, is a wonderful thing.

Thank you for taking the time to be a traveller at the outset of this journey with me. If you have any feedback whatsoever, please don’t hesitate to reach out via a comment and/or email to ian [DOT] lumb [AT] gmail [DOT] com. Bon voyage!