 Welcome everybody at the Starry City talk and what I would like to do today is to explain a little bit about the Zero Hunger Lab and the research we are doing. But before that I was my screens. So I was thinking about the audience and also have a meal explained in the beginning about, let's say what what do we try to achieve with these talks and I was thinking of Marie because we have one thing in common. In these weeks we are both moving to a new house and to avoid rumors we are not moving to the same house because Eric did not appreciate that. But I sent this picture to Marie and Eric last week because we are moving they are moving. And then I got the reaction back. Oh that's typical logistic modeling operations research approach. Okay, I thought, but how would Eric and Marie move. So, then I thought it must be something like this. So it's kind of artificial and so on the left is Eric and on in the front is Marie. But I think it is very good to bring together all these, let's say, these expertise, which we have at Tobit University. And that's also part of the reason why I said yes to Emile when he asked me for this talk. Because Emile explained about the ELSA labs. And as a zero hunger lab, we want to establish an ELSA lab in this year or next year or maybe year after. But what we find important is that it's very multidisciplinary. And that's also what the ties talks are about. And I hope is this talk that I can at least I'm going to explain a little bit what we are doing. But I hope that you find angles that you say hey, this relates to my research and then please refer to me. Then over to the more serious part hunger hunger is one of the most challenging problems we have at this moment. It's nearly 700 million people go to that hungry tonight. And as Marie already mentioned, it's raising at this moment. It's not decreasing. It has been decreasing for 15 years. And the last three years, it's climbing again. And the really real thing is that a lot of children dying. And so every 10 seconds, a child dies of hunger. So after my talk, three classrooms of children have died. So that's really, we need to really to do something about. And in the past 10 years, I've devoted my research really to work on applying data science and especially my background is operations research to do something for the world. And I was very happy with this sustainable development goals. And number two is on zero hunger. Number one is poverty. Number two is zero hunger. And what we do in the zero hunger lab is what we call bites for bites. And so we want to help. We are not going to ourselves to Africa, but we want to help. Let's say NGOs, the United Nations governments to to reduce hunger in the world. So let them work better. So what I would like to do today is briefly meet the zero hunger lab show you our research focus areas and in the end, a little bit on how you might help. So this is our team. We are rapidly expanding. What you see on the left, and you see the, the SAGs. So this is number two, and there are 17 in total. And this is what we are trying to help the young girl it's a Yazidi girl. We cannot help her personally, but we can help children like her. And what we try to do in the zero hunger lab and that's important we try to find the balance between science and impact. So our research, we wanted to be impactful and I'll show you later some of the, of the results you already achieved. So, what is about is the following so we have, let's say, data and algorithms. I don't need to explain in this group. We want to come to solutions and advice for our departments for the room we work. Most of the time NGOs, sometimes governments, and often also the UN, and we want to come to better decisions. And the better decisions might be on emergency relief. That's what you see on the left. That's when a disaster strikes and earthquake or flood and whatever. I think is more important is on the sustainable development. And we want also to help there. And we can do a lot there because the final solution of hunger in this world will be that societies can take care of themselves as and also climate resistance, and these kind of things. And so we want to help there. And so our main focus at this moment is data and algorithms, maybe in the future we will expand it in the Elzau way, also with law and ethics and behavioral science. But for now, this is what we are doing. We have four main themes, and I will show you a little bit, some examples in, I will not go into detail because time is short for that, but if you are interested please please come to me. We have emergency relief that's where we originated. We are working on detecting malnutrition. We are working on healthy diets for a healthy planet, and we are working on food system resilience. And to emergency supply chain optimization, this has much to do with the work which we have been doing over the past 10 years with the United Nations World Food Program, and they are based in Rome. And what we do there is we develop solutions to make relief a very efficient and effective. And we do that by mathematical optimizations, think of my boxes and moving to a house. You want to do that as efficient as possible. And the impact we try to achieve is saving lives by as many people we can help, or within the same funding. Now, some examples. Our work started with the World Food Program. We started some 10 years ago. It has been a lot of work. We have worked with five master thesis students from Tilburg University, one PhD, and we have worked for many years and WFP has invested a lot in it. They have built up their own group of analytical people over these 10 years. And, yeah, the nice thing is, from a science perspective, we want the front Edmund watch this year with his work, but from an impact, and that's the science impact balance from impact perspectives we were also very happy, because what we achieved there in this 10 year is that they saved, let's say so many money that they can feed 2 million people for an entire year. That was the same funding they have today. That's really, and I think, and the nice thing is when you hear these managers talking at the United Nations, they say this is only the beginning. And then I say, I think by myself, yes, these people understand even more than some of the Western companies, what data science, what artificial intelligence is about. And we not only work for Africa, we also work for the Dutch food banks, because we also want to do something nearby. And we are working on several aspects. So, for example, to forecast how many people are eligible for food banks, and we are also of course also looking to their logistics and how they could invest optimally in spending their money also best. And the final one in this category is, we have last year we have worked on the optimal response depots. And when maybe you know that worldwide there are six humanitarian response depot. So whenever a disaster strikes, so take for example Haiti a few weeks ago, then immediately these depots are opened and immediately the flow starts of all kinds of goods, tents, water, food. And we have looked at the position of these depots and we could mathematically show that they are not at the right location. So you can be much faster at a disaster if there are positioned differently. Of course, there's also a lot of politics behind. I won't bother you with that. But these are the things which we do in emergency relief. And further we are also working on detecting malnutrition, and that is also work which we do together with the faculty of PhD and also with this Eric Posma. And so we develop artificial intelligence solutions to quickly determine malnutrition. I can really imagine that in Africa, or wherever in South America, that the mother with her children and often are very many children, they have to go go to a medical post. Then his children are weighted and they are measured and all kinds of things to detect the malnutrition. So it is a lot of effort for all these families, but it's also a huge claim on the medical sector. A lot of doctors and nurses I needed for that. Well, it's a big German organization in helping to reduce hunger. They asked us to help on, let's say, they had an idea of making a kind of picture or a small movie of a child. And that from that movie with artificial intelligence neural networks, these kind of things we can immediately detect whether a child is malnutrition. And then already separate 80 90% that would already help the tremendous. And this, this is also one that could have a tremendous effect in the world, because there are very many organizations looking for this also UN but many NGOs, they are also facing and also medicines without frontiers and all these kind of organizations are looking for these kind of solutions. The research line is the healthy diet for healthy planet. In a lot of situations, you can use artificial intelligence techniques to compose a very healthy diet, but we are also looking to make that. Sometimes you can calculate that but then the diet is not affordable, or the diet is very environmentally unfriendly. So we need to really find these these diets and compositions of diets to have people that say, let them have a regional good way of feeding themselves. We have here as partners again the world food program kept jam and I and john Hopkins, where we work together. And this should impact many of the policymakers and NGOs, and we are looking for some software model to help them in, and especially in low and mid income countries, but also we can use this in, let's say in many neighborhoods in in the west, where we know that the diet patterns are in fact very bad. And the fourth and final line is food system resilience and that's what I explained in the beginning hunger will in the end be reduced when we can develop resilient food systems. And the funny thing is when you look from a research perspective and algorithmic perspective perspective, let's say the solutions and algorithms really resemble the ones which we use in emergency aid. And because you don't have to procure food you have to grow food and so there's a kind of agricultural aspect into it. And this is one we are currently setting up and working out this one is not as far as the other three, but this will be an important one for the future. So we have worked on the sustainable palm oil is solid that we have looked at the effect of activity of cash programs, because more and more relief organizations are giving cash to people instead of food or items. So I found some very interesting results because so I had a very nice database of a lot of households, and we were able to analyze that and draw interesting conclusions. And the final one is from where we work with oxfam on the so called digital diversity real, and there we advise farmers via mobile app in Zimbabwe, what kind of crops they should grow. We know we should grow maize, but then there are 15 types of maize, which one is now the best that grows in their area, given the amount of, given the soil given the amount of rain give the amount of sun and these kind of things. I know it's a rush. A lot of things. I hope you have found some connections or something that you say hey that's interesting that might relate to my research. We cannot eliminate, eliminate hunger on our own, and we need to do that there's a lot of people a lot of research and a lot of organizations. And so I think we need all your expertise. I already mentioned it is machine learning ethics behavioral sciences law. And many of these things also mentioned by Emil, we have it at our university. And we want to expand our lab to include these domains of expertise. So I hope to hear from you in the future. Thank you very much for your attention.