Resoundingly Human

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.

Few topics generate more debate than elections. But long before voters head to the polls, important decisions have already been made about how communities are grouped into districts and how representation is structured. While redistricting is often discussed as a political issue, it's also a fascinating operations research problem – one involving competing objectives and complex constraints such as geography, population, representation, and legal requirements, as well as competing definitions of fairness. My guest today, Ian Ludden, Assistant Professor of Computer Science and Software Engineering at Rose-Hulman Institute of Technology, will share how computational methods and OR/MS are helping researchers evaluate district maps and better understand the choices that shape our democracy. We'll explore questions of fairness, representation, and how data-driven approaches can help us better understand one of the most consequential – and often controversial – processes in American democracy.

Today, we're talking about a topic that sits at the heart of the INFORMS mission: public engagement. While many people think of analytics and operations research as tools used in businesses, universities, or research labs, these disciplines also play an important role in informing public policy, improving government decision-making, and helping communities tackle difficult problems. Joining me today is someone who has spent his career not only advancing the field, but helping others understand its value and impact. Jim Cochran is a professor at the University of Alabama, an internationally recognized educator, researcher, and advocate for analytics and operations research. He is also the recipient of the 2025 INFORMS Advocacy and Governance Committee Award for Public Engagement.

Earlier this year, we kicked off 2026 with INFORMS President Mark Lewis, talking about his vision for the year ahead. Now, just after the INFORMS Analytics+ Conference – his first major event as president – we're checking back in to reflect on what we've seen so far, what stood out, and what's still ahead for the INFORMS community.

In this episode, we're diving into something every one of us experiences - but may not always think about strategically: customer relationships. Why do we stay loyal to some brands and leave others after one frustrating interaction? How are analytics and AI helping companies predict churn, personalize experiences, and build trust in a world where customers have more power than ever before? Helping tackle these questions is Ankit Agrawal, whose work sits at the intersection of analytics, customer experience, and business strategy. We'll talk about what companies are getting wrong about loyalty, how CRM has evolved, and why the future of customer relationships may depend as much on empathy as it does on algorithms.

In the final epiosde of our special series highlighting the finalists of the 2026 Franz Edelman Award, members of the team from Microsoft share special insight on their project, which is transforming how global cloud infrastructure is supplied and delivered. As demand for cloud services continues to grow, the logistics behind delivering the hardware that powers data centers have become increasingly complex. To meet this challenge, Microsoft developed a platform – the Intelligent Fulfillment Service – that combines machine learning, mathematical optimization, and generative and agentic AI to automate global shipment

This latest episode in the mini-series highlighting the 2026 Franz Edelman Award finalist featues members of the team from Google , whose project is advancing a new approach to managing energy and computing resources across the company's global data center network. Data centers power the digital services we rely on every day, but they also require enormous amounts of energy. To address this challenge, Google developed a first-of-its-kind system that intelligently shifts flexible computing workloads across both time and location to take advantage of cleaner energy sources. In this episode, we'll hear more about how this innovative system works and the impact it's having.

Each year, organizations from around the world demonstrate how advanced analytics and operations research are solving real problems at massive scale – and are recognized and celebrated by the INFORMS community with the Franz Edelman Award competition. Joining me today are members of the team from NVIDIA , a global leader in accelerated computing and artificial intelligence. As companies grow and their products evolve, the demand–supply networks that support them often become incredibly complex. To stay ahead of this complexity and remain responsive in fast-moving markets, organizations need smarter and more adaptive supply chain management systems. To tackle this challenge, NVIDIA developed an AI-powered planning platform designed to maintain stability, responsiveness, and agility across their global supply chain. The platform brings together a full stack of advanced technologies, all working together to support intelligent supply chain planning at scale. Today, we'll hear more about how this powerful combination of AI and optimization is transforming supply chain decision-making.

Each year, organizations from around the world demonstrate how advanced analytics and operations research are solving real problems at massive scale – from retail and logistics to healthcare systems, public services, and the complex networks that keep our world moving . Joining me today are members of the team from India's Department of Food and Public Distribution (DFPD), working in partnership with the United Nations World Food Programme in India and the Indian Institute of Technology Delhi. Together, they developed Anna Chakra, an operations research–based decision support system designed to strengthen India's Public Distribution System by optimizing the logistics that move food across the country. Today we'll hear how this ambitious project came together – and how operations research is helping transform food distribution at national scale.

Each year, organizations from around the world compete in the INFORMS Franz Edelman Award competition, demonstrating how advanced analytics and O.R. are solving real problems at massive scale. In this episode, members of the team from Chewy , the largest online pet retailer and pharmacy in the United States, share how they tackled a challenge that sounds simple on the surface but becomes incredibly complex at scale: replenishment. Using a science-driven suite of models, supported by a custom engineering platform and continuous analytics monitoring, the team transformed how Chewy manages purchase orders, safety stock, and inventory placement – all while accounting for unreliable data, supply uncertainty, and vendor constraints. The result? Improved inventory placement, fewer split shipments, shorter shipping distances, and meaningful financial impact in a thin-margin industry.

Each year, finalists in the Franz Edelman Award Competition demonstrate how advanced analytics and operations research are solving real problems at massive scale – from the products we buy and the packages we receive to the healthcare we rely on and the policies that shape our communities. Joining me today are members of the team from ECCO , the globally recognized footwear brand known for its vertically integrated supply chain and retail operations. Managing inventory across hundreds of retail locations around the world is a massive logistical challenge. Historically, store replenishment decisions were made manually, which often led to inconsistent ordering, operational inefficiencies, and missed opportunities to optimize inventory placement. To address this, ECCO's data & AI team developed a powerful analytics system that uses large-scale stochastic mixed-integer programming to automate store replenishment decisions across their global retail network.

Our latest guest brings a voice – and I do mean a voice – that you don't often hear in the data world. He's bold, he's sharp, and he's not afraid to call out the fluff that can cloud today's analytics conversations. Scott Taylor , also known as The Data Whisperer , is a global advocate for strategic data management, the author of Telling Your Data Story , and one of the most honest, and most likeable, truth-tellers in the data space. Whether he's breaking down metadata metaphors or championing the case for master data as the foundation of all things analytics, Scott's mission is crystal clear: No matter how fancy your AI is, it's worthless without the right data underneath. Scott is a keynote speaker at the upcoming 2026 INFORMS Analytics+ Conference, where he'll be shaking things up with his signature style.

Even though we are are still navigating the AI boom and its growing impact, it's still important to also keepo looking ahead to what's next. In this episode, we're diving into a topic that sparks imagination, confusion, and maybe even a little fear: quantum computing. Joining me in this episode is Tam á s Terlaky, a longtime INFORMS member and a global leader in optimization, interior point methods, and – more recently – the future of quantum computing. Tam á s is a professor at Lehigh University and a passionate advocate for preparing the OR/MS community for the next technological wave.

The 2026 INFORMS President Mark Lewis brings not just technical brilliance to his role as president, but a deep care for the people who make this field so impactful. His vision reminds us that analytics isn't just about solving problems – it's about building communities, growing opportunity, and creating a better world through smarter decisions.

I'm so excited to be joined by someone whose name is deeply woven into the fabric of the INFORMS community – Dr. Jim Cochran, Professor of Applied Statistics and the Mike & Kathy Morone Research Chair at the University of Alabama's Culverhouse College of Business. Jim is a longtime INFORMS member, past recipient of the INFORMS Prize for the Teaching of OR/MS Practice and INFORMS Presidents Award, and a Fellow of INFORMS, the American Statistical Association, and the African Academy of Sciences, and a tireless advocate for analytics education, international collaboration, and community engagement. He's attended more INFORMS meetings than most of us can count – and today we're talking about what has changed, what has stayed the same, and why INFORMS remains such a meaningful home for so many members like Jim.

We close out another terrific year of Resoundingly Human podcasts with our 2025 INFORMS President Dave Hunt, for a look back over his time as president, milestones, and what activities he's looking forward to getting back to in 2026.

In this episode, we're continuing our special relaunch series by welcoming one of this year's INFORMS Annual Meeting plenary speakers – a pioneer in the fusion of machine learning and optimization, and a leader in advancing AI for real-world engineering challenges. Pascal Van Hentenryck is a Fellow of INFORMS and AAAI, and has made groundbreaking contributions to constraint programming and optimization systems that are still used in industry decades later. His current research focuses on integrating AI with mathematical optimization to solve incredibly complex and large-scale problems in energy, transportation, supply chains, and more. At the 2025 INFORMS Annual Meeting, he delivered a plenary address titled "Learning to Optimize: Foundations and Industrial Impact," exploring how machine learning and optimization can work hand-in-hand to meet real-time demands in today's dynamic systems.