Linear Digressions

By Ben Jaffe and Katie Malone

What's Linear Digressions about?

Linear Digressions is a podcast about machine learning and data science. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago.

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Linear Digressions episodes:

07.26.2020
So long, and thanks for all the fish
All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing...
07.19.2020
A Reality Check on AI-Driven Medical Assistants
The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that...
07.13.2020
A Data Science Take on Open Policing Data
A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to...
07.06.2020
Procella: YouTube's super-system for analytics data storage
This is a re-release of an episode that originally ran in October 2019. If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each...
06.29.2020
The Data Science Open Source Ecosystem
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally...
06.21.2020
Rock the ROC Curve
This is a re-release of an episode that first ran on January 29, 2017. This week: everybody's favorite WWII-era classifier metric! But it's not just for winning wars, it's a fantastic go-to metric for all your classifier...
06.15.2020
Criminology and Data Science
This episode features Zach Drake, a working data scientist and PhD candidate in the Criminology, Law and Society program at George Mason University. Zach specializes in bringing data science methods to studies of criminal behavior, and got in touch after...
06.07.2020
Racism, the criminal justice system, and data science
As protests sweep across the United States in the wake of the killing of George Floyd by a Minneapolis police officer, we take a moment to dig into one of the ways that data science perpetuates and amplifies racism in...
06.05.2020
An interstitial word from Ben
A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.
05.31.2020
Convolutional Neural Networks
This is a re-release of an episode that originally aired on April 1, 2018 If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net. This episode is all about the architecture...
05.24.2020
Stein's Paradox
This is a re-release of an episode that was originally released on February 26, 2017. When you're estimating something about some object that's a member of a larger group of similar objects (say, the batting average of a baseball ...
05.18.2020
Protecting Individual-Level Census Data with Differential Privacy
The power of finely-grained, individual-level data comes with a drawback: it compromises the privacy of potentially anyone and everyone in the dataset. Even for de-identified datasets, there can be ways to re-identify the records or otherwise figure out sensitive personal...
05.11.2020
Causal Trees
What do you get when you combine the causal inference needs of econometrics with the data-driven methodology of machine learning? Usually these two don’t go well together (deriving causal conclusions from naive data methods leads to biased answers) but economists...
05.04.2020
The Grammar Of Graphics
You may not realize it consciously, but beautiful visualizations have rules. The rules are often implict and manifest themselves as expectations about how the data is summarized, presented, and annotated so you can quickly extract the information in the underlying...
04.27.2020
Gaussian Processes
It’s pretty common to fit a function to a dataset when you’re a data scientist. But in many cases, it’s not clear what kind of function might be most appropriate—linear? quadratic? sinusoidal? some combination of these, and perhaps others? Gaussian...
04.20.2020
Keeping ourselves honest when we work with observational healthcare data
The abundance of data in healthcare, and the value we could capture from structuring and analyzing that data, is a huge opportunity. It also presents huge challenges. One of the biggest challenges is how, exactly, to do that structuring and...
04.13.2020
Changing our formulation of AI to avoid runaway risks: Interview with Prof. Stuart Russell
AI is evolving incredibly quickly, and thinking now about where it might go next (and how we as a species and a society should be prepared) is critical. Professor Stuart Russell, an AI expert at UC Berkeley, has a formulation...
04.06.2020
Putting machine learning into a database
Most data scientists bounce back and forth regularly between doing analysis in databases using SQL and building and deploying machine learning pipelines in R or python. But if we think ahead a few years, a few visionary researchers are starting...
03.29.2020
The work-from-home episode
Many of us have the privilege of working from home right now, in an effort to keep ourselves and our family safe and slow the transmission of covid-19. But working from home is an adjustment for many of us, and...
03.23.2020
Understanding Covid-19 transmission: what the data suggests about how the disease spreads
Covid-19 is turning the world upside down right now. One thing that’s extremely important to understand, in order to fight it as effectively as possible, is how the virus spreads and especially how much of the spread of the disease...
03.15.2020
Network effects re-release: when the power of a public health measure lies in widespread adoption
This week’s episode is a re-release of a recent episode, which we don’t usually do but it seems important for understanding what we can all do to slow the spread of covid-19. In brief, public health measures for infectious diseases...
03.09.2020
Causal inference when you can't experiment: difference-in-differences and synthetic controls
When you need to untangle cause and effect, but you can’t run an experiment, it’s time to get creative. This episode covers difference in differences and synthetic controls, two observational causal inference techniques that researchers have used to understand causality...
03.02.2020
Better know a distribution: the Poisson distribution
This is a re-release of an episode that originally ran on October 21, 2018. The Poisson distribution is a probability distribution function used to for events that happen in time or space. It’s super handy because it’s pretty simple to use...
02.23.2020
The Lottery Ticket Hypothesis
Recent research into neural networks reveals that sometimes, not all parts of the neural net are equally responsible for the performance of the network overall. Instead, it seems like (in some neural nets, at least) there are smaller subnetworks...
02.17.2020
Interesting technical issues prompted by GDPR and data privacy concerns
Data privacy is a huge issue right now, after years of consumers and users gaining awareness of just how much of their personal data is out there and how companies are using it. Policies like GDPR are imposing more stringent...
02.10.2020
Thinking of data science initiatives as innovation initiatives
Put yourself in the shoes of an executive at a big legacy company for a moment, operating in virtually any market vertical: you’re constantly hearing that data science is revolutionizing the world and the firms that survive and thrive in...
02.02.2020
Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng
As demand for data scientists grows, and it remains as relevant as ever that practicing data scientists have a solid methodological and technical foundation for their work, higher education institutions are coming to terms with what’s required to educate the...
01.27.2020
Running experiments when there are network effects
Traditional A/B tests assume that whether or not one person got a treatment has no effect on the experiment outcome for another person. But that’s not a safe assumption, especially when there are network effects (like in almost any social...
01.20.2020
Zeroing in on what makes adversarial examples possible
Adversarial examples are really, really weird: pictures of penguins that get classified with high certainty by machine learning algorithms as drumsets, or random noise labeled as pandas, or any one of an infinite number of mistakes in labeling data that...
01.13.2020
Unsupervised Dimensionality Reduction: UMAP vs t-SNE
Dimensionality reduction redux: this episode covers UMAP, an unsupervised algorithm designed to make high-dimensional data easier to visualize, cluster, etc. It’s similar to t-SNE but has some advantages. This episode gives a quick recap of t-SNE, especially the connection it...
01.05.2020
Data scientists: beware of simple metrics
Picking a metric for a problem means defining how you’ll measure success in solving that problem. Which sounds important, because it is, but oftentimes new data scientists only get experience with a few kinds of metrics when they’re learning and...
12.30.2019
Communicating data science, from academia to industry
For something as multifaceted and ill-defined as data science, communication and sharing best practices across the field can be extremely valuable but also extremely, well, multifaceted and ill-defined. That doesn’t bother our guest today, Prof. Xiao-Li Meng of the Harvard...
12.23.2019
Optimizing for the short-term vs. the long-term
When data scientists run experiments, like A/B tests, it’s really easy to plan on a period of a few days to a few weeks for collecting data. The thing is, the change that’s being evaluated might have effects that last...
12.16.2019
Interview with Prof. Andrew Lo, on using data science to inform complex business decisions
This episode features Prof. Andrew Lo, the author of a paper that we discussed recently on Linear Digressions, in which Prof. Lo uses data to predict whether a medicine in the development pipeline will eventually go on to win FDA...
12.08.2019
Using machine learning to predict drug approvals
One of the hottest areas in data science and machine learning right now is healthcare: the size of the healthcare industry, the amount of data it generates, and the myriad improvements possible in the healthcare system lay the groundwork for...
12.02.2019
Facial recognition, society, and the law
Facial recognition being used in everyday life seemed far-off not too long ago. Increasingly, it’s being used and advanced widely and with increasing speed, which means that our technical capabilities are starting to outpace (if they haven’t already) our consensus...
11.25.2019
Lessons learned from doing data science, at scale, in industry
If you’ve taken a machine learning class, or read up on A/B tests, you likely have a decent grounding in the theoretical pillars of data science. But if you’re in a position to have actually built lots of models or...
11.18.2019
Varsity A/B Testing
When you want to understand if doing something causes something else to happen, like if a change to a website causes and dip or rise in downstream conversions, the gold standard analysis method is to use randomized controlled trials. Once...
11.11.2019
The Care and Feeding of Data Scientists: Growing Careers
In the third and final installment of a conversation with Michelangelo D’Agostino, VP of Data Science and Engineering at Shoprunner, about growing and mentoring data scientists on your team. Some of our topics of conversation include how to institute hack...
11.04.2019
The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists
This week’s episode is the second in a three-part interview series with Michelangelo D’Agostino, VP of Data Science at Shoprunner. This discussion centers on building a team, which means recruiting, interviewing and hiring data scientists. Since data science talent is...
10.28.2019
The Care and Feeding of Data Scientists: Becoming a Data Science Manager
Data science management isn’t easy, and many data scientists are finding themselves learning on the job how to manage data science teams as they get promoted into more formal leadership roles. O’Reilly recently release a report, written by yours truly...
10.21.2019
Procella: YouTube's super-system for analytics data storage
If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that...
10.13.2019
Kalman Runners
The Kalman Filter is an algorithm for taking noisy measurements of dynamic systems and using them to get a better idea of the underlying dynamics than you could get from a simple extrapolation. If you've ever run a marathon, or...
10.06.2019
What's *really* so hard about feature engineering?
Feature engineering is ubiquitous but gets surprisingly difficult surprisingly fast. What could be so complicated about just keeping track of what data you have, and how you made it? A lot, as it turns out—most data science platforms at this...
09.30.2019
Data storage for analytics: stars and snowflakes
If you’re a data scientist or data engineer thinking about how to store data for analytics uses, one of the early choices you’ll have to make (or live with, if someone else made it) is how to lay out the...
09.23.2019
Data storage: transactions vs. analytics
Data scientists and software engineers both work with databases, but they use them for different purposes. So if you’re a data scientist thinking about the best way to store and access data for your analytics, you’ll likely come up with...
09.16.2019
GROVER: an algorithm for making, and detecting, fake news
There are a few things that seem to be very popular in discussions of machine learning algorithms these days. First is the role that algorithms play now, or might play in the future, when it comes to manipulating public opinion,...
09.09.2019
Data science teams as innovation initiatives
When a big, established company is thinking about their data science strategy, chances are good that whatever they come up with, it’ll be somewhat at odds with the company’s current structure and processes. Which makes sense, right? If you’re a...
09.01.2019
Can Fancy Running Shoes Cause You To Run Faster?
This is a re-release of an episode that originally aired on July 29, 2018. The stars aligned for me (Katie) this past weekend: I raced my first half-marathon in a long time and got to read a great article from the...
08.25.2019
Organizational Models for Data Scientists
When data science is hard, sometimes it’s because the algorithms aren’t converging or the data is messy, and sometimes it’s because of organizational or business issues: the data scientists aren’t positioned correctly to bring value to their organization. Maybe they...

What's Linear Digressions about?

Download our free app to listen on your phone

Linear Digressions episodes:

So long, and thanks for all the fish

A Reality Check on AI-Driven Medical Assistants

A Data Science Take on Open Policing Data

Procella: YouTube's super-system for analytics data storage

The Data Science Open Source Ecosystem

Rock the ROC Curve

Criminology and Data Science

Racism, the criminal justice system, and data science

An interstitial word from Ben

Convolutional Neural Networks

Stein's Paradox

Protecting Individual-Level Census Data with Differential Privacy

Causal Trees

The Grammar Of Graphics

Gaussian Processes

Keeping ourselves honest when we work with observational healthcare data

Changing our formulation of AI to avoid runaway risks: Interview with Prof. Stuart Russell

Putting machine learning into a database

The work-from-home episode

Understanding Covid-19 transmission: what the data suggests about how the disease spreads

Network effects re-release: when the power of a public health measure lies in widespread adoption

Causal inference when you can't experiment: difference-in-differences and synthetic controls

Better know a distribution: the Poisson distribution

The Lottery Ticket Hypothesis

Interesting technical issues prompted by GDPR and data privacy concerns

Thinking of data science initiatives as innovation initiatives

Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng

Running experiments when there are network effects

Zeroing in on what makes adversarial examples possible

Unsupervised Dimensionality Reduction: UMAP vs t-SNE

Data scientists: beware of simple metrics

Communicating data science, from academia to industry

Optimizing for the short-term vs. the long-term

Interview with Prof. Andrew Lo, on using data science to inform complex business decisions

Using machine learning to predict drug approvals

Facial recognition, society, and the law

Lessons learned from doing data science, at scale, in industry

Varsity A/B Testing

The Care and Feeding of Data Scientists: Growing Careers

The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists

The Care and Feeding of Data Scientists: Becoming a Data Science Manager

Procella: YouTube's super-system for analytics data storage

Kalman Runners

What's *really* so hard about feature engineering?

Data storage for analytics: stars and snowflakes

Data storage: transactions vs. analytics

GROVER: an algorithm for making, and detecting, fake news

Data science teams as innovation initiatives

Can Fancy Running Shoes Cause You To Run Faster?

Organizational Models for Data Scientists

What's really so hard about feature engineering?