Ben Lorica

Ben Lorica is the Chief Data Scientist and Director of Content Strategy for Data at O'Reilly Media, Inc. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services.

Topic models: Past, present, and future

The O'Reilly Data Show Podcast: David Blei, co-creator of one of the most popular tools in text mining and machine learning.


I don’t remember when I first came across topic models, but I do remember being an early proponent of them in industry. I came to appreciate how useful they were for exploring and navigating large amounts of unstructured text, and was able to use them, with some success, in consulting projects. When an MCMC algorithm came out, I even cooked up a Java program that I came to rely on (up until Mallet came along).

I recently sat down with David Blei, co-author of the seminal paper on topic models, and who remains one of the leading researchers in the field. We talked about the origins of topic models, their applications, improvements to the underlying algorithms, and his new role in training data scientists at Columbia University.

Generating features for other machine learning tasks

Blei frequently interacts with companies that use ideas from his group’s research projects. He noted that people in industry frequently use topic models for “feature generation.” The added bonus is that topic models produce features that are easy to explain and interpret:

“You might analyze a bunch of New York Times articles for example, and there’ll be an article about sports and business, and you get a representation of that article that says this is an article and it’s about sports and business. Of course, the ideas of sports and business were also discovered by the algorithm, but that representation, it turns out, is also useful for prediction. My understanding when I speak to people at different startup companies and other more established companies is that a lot of technology companies are using topic modeling to generate this representation of documents in terms of the discovered topics, and then using that representation in other algorithms for things like classification or other things.”

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Forecasting events, from disease outbreaks to sales to cancer research

The O'Reilly Data Show Podcast: Kira Radinsky on predicting events using machine learning, NLP, and semantic analysis.

Editor’s note: One of the more popular speakers at Strata + Hadoop World, Kira Radinsky was recently profiled in the new O’Reilly Radar report, Women in Data: Cutting-Edge Practitioners and Their Views on Critical Skills, Background, and Education.

When I first took over organizing Hardcore Data Science at Strata + Hadoop World, one of the first speakers I invited was Kira Radinsky. Radinsky had already garnered international recognition for her work forecasting real-world events (disease outbreak, riots, etc.). She’s currently the CTO and co-founder of SalesPredict, a start-up using predictive analytics to “understand who’s ready to buy, who may buy more, and who is likely to churn.”

I recently had a conversation with Radinsky, and she took me through the many techniques and subject domains from her past and present research projects. In grad school, she helped build a predictive system that combined newspaper articles, Wikipedia, and other open data sets. Through fine-tuned semantic analysis and NLP, Radinsky and her collaborators devised new metrics of similarity between events. The techniques she developed for that predictive software system are now the foundation of applications across many areas. Read more…


Network structure and dynamics in online social systems

Understanding information cascades, viral content, and significant relationships.


I rarely work with social network data, but I’m familiar with the standard problems confronting data scientists who work in this area. These include questions pertaining to network structure, viral content, and the dynamics of information cascades.

At last year’s Strata + Hadoop World NYC, Cornell Professor and Nevanlinna Prize Winner Jon Kleinberg walked the audience through a series of examples from social network analysis, looking at the content of shared photos and text, as well as the structures of the networks. It was a truly memorable presentation from one of the foremost experts in network analysis. Each of the problems he discussed would be of interest to marketing professionals, and the analytic techniques he described were accessible to many data scientists. What struck me is that while these topics are easy to describe, framing the right question requires quite a bit of experience with the underlying data.

Predicting whether an information cascade will double in size

Can you predict if a piece of information (say a photo) will be shared only a few times or hundreds (if not thousands) of times? Large cascades are very rare, making the task of predicting eventual size difficult. You either default to a pathological answer (after all most pieces of information are shared only once), or you create a balanced data set (comprised of an equal number of small and large cascades) and end up solving an artificial task.

Thinking of a social network as an information transport layer, Kleinberg and his colleagues instead set out to track the evolution of cascades. In the process, they framed an interesting balanced algorithmic prediction problem: given a cascade of size k, predict whether it will reach size 2k (it turns out 2k is roughly the median size of a cascade conditional on whether it reaches size k). Read more…


The evolution of GraphLab

The O'Reilly Data Show Podcast: Carlos Guestrin on the early days of GraphLab and the evolution of GraphLab Create.

Editor’s note: Carlos Guestrin will be part of the team teaching Large-scale Machine Learning Day at Strata + Hadoop World in San Jose. Visit the Strata + Hadoop World website for more information on the program.

I only really started playing around with GraphLab when the companion project GraphChi came onto the scene. By then I’d heard from many avid users and admired how their user conference instantly became a popular San Francisco Bay Area data science event. For this podcast episode, I sat down with Carlos Guestrin, co-founder/CEO of Dato, a start-up launched by the creators of GraphLab. We talked about the early days of GraphLab, the evolution of GraphLab Create, and what’s he’s learned from starting a company.

MATLAB for graphs

Guestrin remains a professor of computer science at the University of Washington, and GraphLab originated when he was still a faculty member at Carnegie Mellon. GraphLab was built by avid MATLAB users who needed to do large scale graphical computations to demonstrate their research results. Guestrin shared some of the backstory:

“I was a professor at Carnegie Mellon for about eight years before I moved to Seattle. A couple of my students, Joey Gonzales and Yucheng Low were working on large scale distributed machine learning algorithms specially with things called graphical models. We tried to implement them to show off the theorems that we had proven. We tried to run those things on top of Hadoop and it was really slow. We ended up writing those algorithms on top of MPI which is a high performance computing library and it was just a pain. It took a long time and it was hard to reproduce the results and the impact it had on us is that writing papers became a pain. We wanted a system for my lab that allowed us to write more papers more quickly. That was the goal. In other words so they could implement this machine learning algorithms more easily, more quickly specifically on graph data which is what we focused on.”

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Building and deploying large-scale machine learning pipelines

We need primitives; pipeline synthesis tools; and most importantly, error analysis and verification.

There are many algorithms with implementations that scale to large data sets (this list includes matrix factorization, SVM, logistic regression, LASSO, and many others). In fact, machine learning experts are fond of pointing out: if you can pose your problem as a simple optimization problem then you’re almost done.

Of course, in practice, most machine learning projects can’t be reduced to simple optimization problems. Data scientists have to manage and maintain complex data projects, and the analytic problems they need to tackle usually involve specialized machine learning pipelines. Decisions at one stage affect things that happen downstream, so interactions between parts of a pipeline are an area of active research.


Some common machine learning pipelines. Source: Ben Recht, used with permission.

In his Strata+Hadoop World New York presentation, UC Berkeley Professor Ben Recht described new UC Berkeley AMPLab projects for building and managing large-scale machine learning pipelines. Given AMPLab’s ties to the Spark community, some of the ideas from their projects are starting to appear in Apache Spark. Read more…

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A brief look at data science’s past and future

In this O'Reilly Data Show Podcast: DJ Patil weighs in on a wide range of topics in data science and big data.

Back in 2008, when we were working on what became one of the first papers on big data technologies, one of our first visits was to LinkedIn’s new “data” team. Many of the members of that team went on to build interesting tools and products, and team manager DJ Patil emerged as one of the best-known data scientists. I recently sat down with Patil to talk about his new ebook (written with Hilary Mason) and other topics in data science and big data.

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Here are a few of the topics we touched on:

Proliferation of programs for training and certifying data scientists

Patil and I are both ex-academics who learned learned “data science” in industry. In fact, up until a few years ago one acquired data science skills via “on-the-job training.” But a new job title that catches on usually leads to an explosion of programs (I was around when master’s programs in financial engineering took off). Are these programs the right way to acquire the necessary skills? Read more…


Lessons from next-generation data wrangling tools

Drawing inspiration from recent advances in data preparation.


One of the trends we’re following is the rise of applications that combine big data, algorithms, and efficient user interfaces. As I noted in an earlier post, our interest stems from both consumer apps as well as tools that democratize data analysis. It’s no surprise that one of the areas where “cognitive augmentation” is playing out is in data preparation and curation. Data scientists continue to spend a lot of their time on data wrangling, and the increasing number of (public and internal) data sources paves the way for tools that can increase productivity in this critical area.

At Strata + Hadoop World New York, NY, two presentations from academic spinoff start-ups — Mike Stonebraker of Tamr and Joe Hellerstein and Sean Kandel of Trifacta — focused on data preparation and curation. While data wrangling is just one component of a data science pipeline, and granted we’re still in the early days of productivity tools in data science, some of the lessons these companies have learned extend beyond data preparation.

Scalability ~ data variety and size

Not only are enterprises faced with many data stores and spreadsheets, data scientists have many more (public and internal) data sources they want to incorporate. The absence of a global data model means integrating data silos, and data sources requires tools for consolidating schemas.

Random samples are great for working through the initial phases, particularly while you’re still familiarizing yourself with a new data set. Trifacta lets users work with samples while they’re developing data wrangling “scripts” that can be used on full data sets.
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Apache Spark’s journey from academia to industry

In this O'Reilly Data Show Podcast: Ion Stoica talks about the rise of Apache Spark and Apache Mesos.

Three projects from UC Berkeley’s AMPLab have been keenly adopted by industry: Apache Mesos, Apache Spark, and Tachyon. As an early user, it’s been fun to watch Spark go from an academic lab to the most active open source project in big data. In my recent travels, I’ve met Spark users from companies of all sizes and and from many industries. I’ve also spoken with companies that came of age before Spark was available or mature enough, and many are replacing homegrown tools with Spark (Full disclosure: I’m an advisor to Databricks, a start-up commercializing Apache Spark..)

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A few months ago, I spoke with UC Berkeley Professor and Databricks CEO Ion Stoica about the early days of Spark and the Berkeley Data Analytics Stack. Ion noted that by the time his students began work on Spark and Mesos, his experience at his other start-up Conviva had already informed some of the design choices:

“Actually, this story started back in 2009, and it started with a different project, Mesos. So, this was a class project in a class I taught in the spring of 2009. And that was to build a cluster management system, to be able to support multiple cluster computing frameworks like Hadoop, at that time, MPI and others. To share the same cluster as the data in the cluster. Pretty soon after that, we thought about what to build on top of Mesos, and that was Spark. Initially, we wanted to demonstrate that it was actually easier to build a new framework from scratch on top of Mesos, and of course we wanted it to be also special. So, we targeted workloads for which Hadoop at that time was not good enough. Hadoop was targeting batch computation. So, we targeted interactive queries and iterative computation, like machine learning. Read more…


Clustering bitcoin accounts using heuristics

In this O'Reilly Data Show Podcast: Sarah Meiklejohn on analytic applications for blockchain and cryptocurrency technology.

Editor’s note: we’ll explore present and future applications of cryptocurrency and blockchain technologies at our upcoming Radar Summit: Bitcoin & the Blockchain on Jan. 27, 2015, in San Francisco.

A few data scientists are starting to play around with cryptocurrency data, and as bitcoin and related technologies start gaining traction, I expect more to wade in. As the space matures, there will be many interesting applications based on analytics over the transaction data produced by these technologies. The blockchain — the distributed ledger that contains all bitcoin transactions — is publicly available, and the underlying data set is of modest size. Data scientists can work with this data once it’s loaded into familiar data structures, but producing insights requires some domain knowledge and expertise.

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I recently spoke with Sarah Meiklejohn, a lecturer at UCL, and an expert on computer security and cryptocurrencies. She was part of an academic research team that studied pseudo-anonymity (“pseudonymity”) in bitcoin. In particular, they used transaction data to compare “potential” anonymity to the “actual” anonymity achieved by users. A bitcoin user can use many different public keys, but careful research led to a few heuristics that allowed them to cluster addresses belonging to the same user:

“In theory, a user can go by many different pseudonyms. If that user is careful and keeps the activity of those different pseudonyms separate, completely distinct from one another, then they can really maintain a level of, maybe not anonymity, but again, cryptographically it’s called pseudo-anonymity. So, if they are a legitimate businessman on the one hand, they can use a certain set of pseudonyms for that activity, and then if they are dealing drugs on Silk Road, they might use a completely different set of pseudonyms for that, and you wouldn’t be able to tell that that’s the same user.

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Regulation and decentralization: Defending the blockchain

Andreas Antonopoulos urges the Canadian Senate to resist the temptation to centralize bitcoin.

Editor’s note: our O’Reilly Radar Summit: Bitcoin & the Blockchain will take place on January 27, 2015, at Fort Mason in San Francisco. Andreas Antonopoulos, Vitalik Buterin, Naval Ravikant, and Bill Janeway are but a few of the confirmed speakers for the event. Learn more about the event and reserve your ticket here.

We recently announced a Radar summit on present and future applications of cryptocurrencies and blockchain technologies. In a webcast presentation one of our program chairs, Kieren James-Lubin, observed that we’re very much in the early days of these technologies. He also noted that the technologies are complex enough that most users will rely on service providers (like wallets) to securely store, transfer, and receive cryptocurrencies.

As some of these service providers reach a certain scale, they will start coming under the scrutiny of regulators. Certain tenets are likely to remain: currencies require continuous liquidity and large financial institutions need access to the lender of last resort.

There are also cultural norms that take time to change. Take the example of notaries, whose services seem amenable to being replaced by blockchain technologies. Such a wholesale change would entail adjusting rules and norms across localities, which means going up against the lobbying efforts of established incumbents.

One way to sway regulators and skeptics is to point out that the decentralized nature of the (bitcoin) blockchain can unlock innovation in financial services and other industries. Mastering Bitcoin author Andreas Antonopoulos did a masterful job highlighting this in his recent testimony before the Canadian Senate:

“Traditional models for financial payment networks and banking rely on centralized control in order to provide security. The architecture of a traditional financial network is built around a central authority, such as a clearinghouse. As a result, security and authority have to be vested in that central actor. The resulting security model looks like a series of concentric circles with very limited access to the center and increasing access as we move farther away from the center. However, even the most outermost circle cannot afford open access.

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