Resoundingly Human

Artificial intelligence has become one of the defining technologies of our time. But what if the way we're thinking about AI – starting with the very idea of "intelligence" – is too narrow?
Welcome to Resoundingly Human: The INFORMS Podcast about how operations research, analytics, data science, and AI are helping us better understand – and improve – the world around us.
Today's guest has spent decades helping shape our understanding of machine learning and its connections to statistics, computation, cognition and decision-making.
Michael Jordan is a researcher at Inria Paris and Professor Emeritus at the University of California, Berkeley and will be joining us in San Francisco as the opening plenary speaker at the 2026 INFORMS Annual Meeting, where he'll present "A Collectivist, Economic Perspective on AI."

If you've ever visited a hospital emergency department, chances are you've experienced one of its most persistent challenges: waiting.
For hospitals, reducing those wait times can seem like a capacity problem. More patients require more beds, more staff or more space, right?
But what if one of the solutions is simply making better decisions about how the resources already available are used?
Joining me today is Agni Orfanoudaki, Oxford University, to share new research on a data-driven approach to determining which patients actually need a traditional emergency department bed and who can be treated in alternate locations. Ultimately, this reduced emergency room stays by 11 minutes, all without adding staff, beds or technology, and without compromising patient safety.

Organizations have more data, more dashboards, and more sophisticated analytics tools than ever before. And yet, many still struggle to make confident decisions quickly.
Sometimes the problem isn't the data itself. It's what happens between the insight and the action: unclear ownership, competing versions of the truth, slow review cycles, and analytics that sit beside the business instead of helping shape it.
In today's episode of Resoundingly Human: The INFORMS Podcast, I'm joined by Ritish Chugh, Senior Analytics Engineer with Airbnb, to talk about what it takes to move from producing analytics to building an organization that actually knows how to use it.

Joining me today is Katalia Chen, recipient of INFORMS Doing Good with Good OR Award, which recognizes outstanding work using operations research to create meaningful social impact.
Katalia's research tackles a question that affects hundreds of thousands of animals every year: how can we help animal shelters make better decisions when resources like kennel space, staff time, and foster homes are limited?
By combining operations research, optimization, survival analysis, and causal inference, her work demonstrates that smarter allocation of these scarce resources can dramatically increase the number of lives saved – without building new facilities or increasing capacity.

Reaching 100,000 downloads isn't just a milestone – it's a reflection that the stories featured on Resoundingly Human matter. It means there are thousands of people who are curious about how analytics can solve meaningful problems and who want to hear directly from the people making that happen.
To celebrate, let's take a look back at our top five favorite conversations from throughout the years. These aren't necessarily the most downloaded episodes – they're simply five conversations that challenged our thinking, taught us something unexpected, or perfectly captured what Resoundingly Human is all about.