Naji Gehchan: Hello, leaders of the world. Welcome to spread love in organizations, the podcast for purpose-driven healthcare leaders, striving to make life better around the world by leading their teams with genuine care, servant leadership, and love.

I am Naji, your host for this episode from our new series focused on us as leaders. I’m thrilled to be joined by an exceptional MIT professor of Management, Operations Research, Statistics and Operations Management: Georgia Perakis.

Georgia teaches courses and performs research in analytics, optimization, machine learning with applications in pricing, revenue management, supply chains, transportation, energy, and healthcare among others. I had the privilege to be her student the past semester, and not only I learned about data, models, and decisions, but I learned about leadership from her as much!

In her research, Georgia investigates the theory and practice of analytics and its role in operations problems.. She has received numerous Awards and have several prestigious publications. Currently, and Georgia serves as the codirector of the Operations Research Center and on the council for the College of Computing, and faculty director of the Executive MBA (EMBA) program at MIT Sloan. I’ll never be able to share all her incredible work and impact so I’ll stop here and listen to her!

Georgia Perakis: Hello, Naji. Thank you very much for inviting me. Nice to see you.

Naji Gehchan: Georgia, before going into the world of data and leadership, I really would love to learn more about your personal story and journey to becoming the great MIT professor you are today.

Georgia Perakis: So, uh, I was actually born in the south of Greece in the island of Crete. I came from a, what I would call a very academic family that starts from my father who, uh, arrived before the second world war started. He was in Switzerland, uh, doing his master’s degree in civil engineering and studying airplanes, unfortunately, because of the war he had to come back and he had to serve the army for five.

So the dream of he used to pursue graduate studies did not work out. Uh, but as a result, uh, both my brother and I, uh, he basically instilled in us the passion to basically study and be academics. Um, and so both my brother and I are professors in the us in different universities, myself in, uh, what I would call applied math in business or analytics and my brother and sister.

So I, I would say in short, I’m trying to say that this is all because of how our father inspired.

Naji Gehchan: Oh, wow. That’s a, that, that’s awesome. Uh, and I’ve, I’ve seen you, you’re talking about how you get into MIT, academics, uh, and well, all the amazing work you’ve done so far and still doing there. Uh, and, and before going, you know, more into the data, I I’ve seen you leading your team, the PhD team that you have during the, you know, the sessions that we had, uh, and the course, uh, and it was really amazing to watch you like truly how you led the team, how you value them, how you brought them in front of the class.

Uh, Yeah, for me, I really felt it’s practically a real team. It’s one, it’s a one team that has, that is doing this. So can you share a little bit your leadership beliefs and your leadership journey and how you ended up in academia and building really strong teams with your.

Georgia Perakis: So how I ended up in academia, I already mentioned, and I came to the U S from graduate school.

I felt that I was just going to finish my master’s and PhD and go back. But of course, many double digit, won’t say how many I am still here. Am I teasing and incredible? Uh, it’s, I call it like a huge candy store with grape candy that it’s hard to choose from. But what I think is extremely unique are the students that we have at MIT, whether there are people like you, the MBA students, or whether there are PhD students, like the ones in the operations research center, which, where you’re doing.

So I consider myself very, very lucky to be able to interact and basically be among people like them and you guys. And so basically my goal is what I would call enabling these people to bring back back, to bring out the best of them. And that’s kind of my core belief actually in everything I do. Bring in enable the best in other people.

And so I know I have the luck to be among incredibly talented people. So bringing out their skills and enabling them is what I believe in doing in everything.

Naji Gehchan: Well, this is such a powerful, uh, such. Powerful word, you know, for a leader is right. Enabling others and making sure the environment and tries to return a lot about what is the environment that you’re creating for us to enable, uh, enable people to be at their best.

Uh, you know, um, I’ve seen all the work and the word I’ve seen all the work you have done with your team, and it’s also part of leadership and how you pivoted, I think with your team. The round. COVID try. I know many don’t want to talk about it, but it’s important where it’s still somehow in a, uh, can you share a little bit with us?

How, how you took your team into modeling all that was happening in COVID and ultimately the impact that you are having on, on policies, uh, now with the data and model, I think it’s a beautiful example on how you can. Take data and do something actionable about it. And it’s a, it’s a great. Leadership lesson, I’m sure you had, as, as things were, uh, on feeding during the pandemic.

Georgia Perakis: So I would say basically that, uh, when the pandemic started and we all had to go very quickly virtually, uh, the first thing that I wanted to do is to create community actually with my. And we have been working on many different types of algorithms. We sub groups of my students. So I thought in order for them to not feel isolated and so forth, it would be great to create community and see how we can do something.

Good. Everything we were doing that where, for example, for the retail space or somewhere for basically other healthcare applications, and that’s how this journey actually got started. Um, we, we tried to basically find data sources and. And actually there was an MBA student at the time, at the time, uh, that also was involved in the team because he talk to me and she told me, Hey, uh, I would like to be involved as well.

Um, so that’s how the discussions got started. The next step it happened. And since we were doing this, uh, and we then got contacted by MIT quest for intelligence, which is sort of the air over my. That sits, you know, it brings together many parts of MIT as it comes to AI. And they notice that we were starting to do this work.

And so they said, well, maybe you can help us because we are trying to understand how cases, how deaths and so forth will evolve. And that will also help us to reopen them. So now, you know, what was just about community building and about let’s make our work mean something, it became something that was sort of more tangible.

It was like, okay, let’s see how we can have on it. Understand how the evolution of cases, the evolution of deaths in different states and counties in the United States and in particular, Massachusetts. Because if we understand what’s happening in Massachusetts and my ticket now make decisions about different measures they could impose in order to make things work.

Because as you know, MIT believed that we in-person experience is something very important. Everybody knew that this is not going to be perfect, but it’s better than just being all virtual with no in-person component. And so we thought it would be great to basically help them do. It was a lot of hard work.

And again, I give all the credit to the students because they work tirelessly and very excited. They had ownership of this. This was their baby, and I was lucky to be part of that conversation. And then we decided, well, we actually maybe can help a little bit more at the national level. So, um, Contacted Fox from the CB CBC.

And then we started actually submitting our models. We are still doing this every week, uh, for predicting for the next week, two weeks, three weeks, four weeks ahead, how cases and deaths are going to evolve. So that’s kind of the summary of a long journey.

Naji Gehchan: Yeah, well, wow. And again, like it’s, uh, you, you always told us data is better than nothing.

And then the ultimate objective is making decisions, right? So it’s, it’s really impressive. And w before going to data machine learning, I want to ask a question on this, but before that, I really want to summarize, you said things that are super powerful. You, you started by, uh, creating a company. Doing something for good.

Right? And then you go into ownership, empowerment of your team, enabling them to be at their best. And honestly, what a humble leader you are, because again, you say it’s your team, but I’m sure without her leadership, you wouldn’t have, they wouldn’t have achieved what you guys have achieved. So, yeah. Thanks for giving all those dealerships tips for all of us.

Georgia Perakis: I think of my team, some amazing students. I honestly believe that at all. And I consider myself very lucky to be part of that environment. Uh, and so I believe success of people is by understanding the abilities of others and helping bringing them out. So, and then we all look good. They do. And actually, so do I find it a bit selfish?

Naji Gehchan: Yeah. I so agree with this. Um, well you mentioned words, right? Data models, machine learning, it’s such trendy words, right? So I would love to get your take on this. I had the opportunity to hear you about it for many hours that I enjoyed. I would love to hear your take on those and the most important advice you would give us for leaders who either are talking about it or dealing with it and our organizations.

What are your, what is your take about all

Georgia Perakis: those strategies? Right. So, first of all, understanding what data we each have in our organization, how we can use them in a good way, how we can use them to have a competitive edge. Or realizing the limitations of our data, maybe what we do not have and we could have, uh, and how, what we don’t have, we can actually collect and use it to do better things.

That’s sort of the first step, basically, uh, understand what is your data environment? Where can you get it and what can you not get? But you could potentially. The second, also an important thing is to understand what you could do with it, potentially what are some areas where analytics and in analytics, I put machine learning, AI optimization, how analytics could actually improve the way you are doing things in your organization and recognizing those opportunities.

And recognizing areas that my lead picks can have a first order effect as I call it. It’s not just a small parentheses. Uh, and so, you know, recognizing what are these areas also extremely important. And then flowed that I wanted to do with the class that I was teaching is to make sure. And I’m not sure if I should use this word, but, and maybe I have not used it, but that nobody can, uh, talk another person.

Nobody can be as you.

And that, you know, and so I’m proud whenever I hear, uh, people, uh, who took my class, that they go in meetings and they recognize, and they ask questions and, you know, people, don’t not trying to pull a fast one over them and that’s extremely satisfying.

Naji Gehchan: Yeah. Yeah, you definitely, you definitely helped us see this for your, you know, for those of us who deal with data constantly and those who don’t, but it’s important to kind of think of it that

Georgia Perakis: way.

And I would say that this past month I was contacted by two former students of mine that graduated one maybe three years ago. And the other two years ago, Sort of talking to me about problems that they had in their organizations and recognizing how some particular tools that I taught them was useful.

And so that happened to me twice in the last month. And, um, even asking me to, to remind them of what software had I recommended back then and so forth, and that’s extremely satisfying, then I feel okay. Yeah, I did something.

Naji Gehchan: Oh yeah, no, definitely. And there are many who don’t come back to what are actually doing it.

So that is for sure. What is the last question on this? Uh, since you’ve worked a lot in academia, but you obviously work it out with companies, right? You mentioned retail and healthcare and many others. Uh, what is the most challenging? When we touch those, you know, machine learning, AI, all those concepts and organization, what is for you?

The most challenging piece a leader should start with if we’re launching this type of projects or transformation.

Georgia Perakis: So I would say first understanding what are the problems that the organization have? What are the right questions and what are the limitations also that they have? What are they capable of?

And then they towel. So because, you know, everybody talks about big data and I say a far more important thing is what happens when I have little data. When I have missing data, actually I would say this is hard there. I’ll talk about big data. Let’s talk first about little data missing data. So I would say those three and in that order first.

Okay. What is that? I problem for me, is it. But maybe by using ethics, I could make a difference for somebody.

Naji Gehchan: Yeah. I will always take this. As you mentioned that many times, right? What is the problem you’re trying to fix? And this is usually the toughest question to ask because yeah, we can have data, but you know, what are we trying to fix and which type of data we need, and then try to deal with this.

So I, I would, uh, I would be moving into giving you one word and I would love to have your reaction to this word, uh, in a sentence or more, right? So the first one is leadership.

Georgia Perakis: So, um, my action is empowering others actions.

Oh, my God. The extremely important having diversity in an organization is what brings power to the organization.

Naji Gehchan: Okay. Can you share it a little bit more because I know you’re passionate about it, then you’ve done a lot of work within MIT on DNI. Can you share with us a little bit more, you know, from beta to auctions and thoughts on,

Georgia Perakis: right.

So a couple of things, the first is that by recognizing that if you bring a lot of different voices, You know, my analogy to data would be if you have different and diverse sources of data and even methods that you can buy, you will actually have a much more powerful outcome. In fact, what we did in our algorithms to predict was exactly the principle of diversity.

We basically developed a lot of different and diverse. Algorithms that capture different aspects. If you like, we brought different voices and by ensembling them together, we created a much more powerful. So it’s almost like taking the model of diversity and putting it in an algorithmic setting. And that’s one example, but I believe it in everything I do and I see it in my group and I see that MIT and I know now.

And in general, MIT takes this very seriously. We can do much better, but you know, recognizing this is the first step to success, I would say.

Naji Gehchan: Yeah, no, I love it. Well, recognizing the problem right

Georgia Perakis: then,

Naji Gehchan: uh, I have to ask this many of the listeners won’t know where it is.

Georgia Perakis: Data models and decisions, how you go from data to recommending good decisions that will bring value and value. Doesn’t have to be just revenue profit. It can be improving the world in some way.

Naji Gehchan: Awesome. This is one of the best courses in this ever programs.

Uh, the last one I’d love your reaction on is spread love and organizations.

Georgia Perakis: So do you need one word or Western, whatever

Naji Gehchan: you want,

Georgia Perakis: you don’t spend? I would say basically feeling compassionate and trying to understand. Other people in your organization so that you can embrace others and you can embrace the diversity or the lack thereof of an organization, and then bring diversity to the organization.

And by sort of having that empathy and compassion with others and spreading it is what would make the organization successful. So, as I said before, in a way it’s kind of understanding and enabling and embracing others.

Naji Gehchan: Awesome. Uh, Georgia, any final word of wisdom for all the leaders around the world.

Georgia Perakis: So I would say one thing, which is extremely important to me and I will put it in mathematical terms, what I call the law of large numbers and what I mean by that is persist and look at the long-term goal.

That’s what I learned in my career. And, you know, things didn’t just come on my plate as I went along in my career, but, you know, uh, I try to focus on the end. I’d rather have a look at the failures that I had right in front of me. And I call that the law of large numbers that if you persist and look at the future, long-term it will work out eventually

Naji Gehchan: the “Love of large number.” I love it. Thank you so much for your time today.

Naji Gehchan: Thank you all for listening to spread love and organization’s podcast. Drop us a review on your preferred podcast platform

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