Roni Kobrosly Ph.D.'s Website

The great Benn Stancil wrote a blog post way back in the summer of 2022, and ever since I read it I can't get it out of my mind. As you can guess from the title of this blog post, the topic is: "Do data-driven companies actually win? Some gut-based judgements on the effectiveness of data." We take for granted that collecting data, mining it for insights, and building ML gives companies a competitive edge. Is that true though? Have we rigoriously assessed that?

Whenever I discuss causal thinking with data, people inevitably bring up the topic of automated causal discovery. I get it. It's sort of like the search for El Dorado and would change the field of data science to its core if it were possible. I talk about what it is and how this is unlikely to happen anytime soon.

My updated website and the joys of Terminal CSS and static site generators.

Anyone who has worked in tech in the data science / MLE / applied ML field knows that most ML projects will fail. Newcomers to the field have super high expectations about what they can do, and, sadly, this leads to a lot of them leaving the space altogether. ML project failure is an inevitable part of life in this field and it's happened to me many times at different stages of my career. In the last couple of years, I've begun to think about the building of ML projects as a sort of funnel process; ideas are cheap and many, but only a small amount of these ideas reach the end of the funnel and become productionized, solid, used, effective, and maintained ML applications.

One of the hottest ChatGPT takes I've heard from mostly non-technical, tech-enthusiast folks is that it, or one of it's near-term successors, will effectively put software engineers into the dustpan of history. The following is my attempt to collect my thoughts around this and present my case to a lay audience on why this will not be the case.

Fall is here and you really ought to buy some of those pumpkin pancake/waffle mixes at the store. I'm there for it. I've been thinking a lot recently about data-driven culture and who is responsible for building it and maintaining it in organizations.

I recently had the privilege of giving a talk and tutorial session at SciPy 2022 in Austin. Besides rediscovering how hot central Texas is in the summer (the sun is trying to kill you), I walked away with some useful insights from the audience...

I love Medium and Substack. They're so great that only a non-English word can truly describe them: fantastico. However, not all tech leadership, data, and engineering blogs will let you subscribe and receive email updates on new posts. And some of the best personal blogs out there only drop a new post a few times a year. You're then forced to periodically and manually check these websites for updates. I've got a list of 50+ of these non-subscribable blogs that I care about, and checking in on them sucks. So, I built a tool...

Like many data folk I'm subscribed to a bunch of weekly data analytics, data science, analytics engineering, and etc. email services. They're roundups of great blog posts, papers, and articles all over the web. Pretty regularly I come across gems in these roundups that give fantastic insights about data leadership, but usually a week later the email is buried under 6 feet of crap in my inbox. No more!

Obesity, as a public health problem, has an enormous amount of "causes": the types of farmed foods we tend to subsidize on a national level, our policies around public transit, the walkability of neighorhoods, the presence of food deserts, our social networks and their attitudes toward obesity, the media, etc etc. All of these complex, interconnected things make it really challenging to perform a causal analysis of potential solutions. I recently came across a great paper that takes a stab at addressing this problem through simulations, and I think the lessons from this are very much applicable to some problems we face in the data industry.