 So I'll be talking about computational gastronomy about what computational gastronomy entails. It's something that I'll explain before I get started off but before that I'm Ganesh Bagler and I come from an institute called Triple I.T. Delhi Interpress Institute of Information Technology and those of you who have done any amount of binary Number system would know that triple I.T. Delhi is 10 years old roughly. It's now 13 years running and it has been dwelling on different aspect of computer science and allied areas which includes computational biology, design, applied mathematics and similar areas which overlap with computer science. My lab complex systems laboratory looks at various complex entities which primarily come from biological domain and these entities are such as those of protein structures, cells which basically we look at diseases as cellular malfunctioning of cellular mechanisms, then brain as a network we look at that and finally we have been looking at food as a complex system. So these systems all of them are made up of interconnected entities first of all and they all exhibit an emergent property. Some of the parts is not the same as the whole. This is a telltale feature of all of these entities. Given the fact that today I'm known for computational gastronomy research you might wonder whether I have been a foodie all the time, but I must tell you that as a teenager I was an aspiring astronomer. So it has been a journey from being trained in physics to all the way up to in computational techniques to doing PhD in an interdisciplinary area called computational biology from Center for Cellular and Molecular Biology, Hyderabad to a couple of postdocs, one in NCBS, National Center for Biological Sciences, Bangalore to Max Planck Institution to all the way up to the discovery of this area or invention of this area called computational gastronomy. So it's been not so shortest path from astronomy to gastronomy. Having said that, what is computational gastronomy? My definition of computational gastronomy is that something that is a data science, which blames food with data and power of computation, so as to bring about data driven food innovations. So essentially my bet is that like it has happened in many other areas of our life, where a certain aspect of life gets transformed by virtue of bringing in data and computation. Think of Google Maps, think of railway reservation systems and social networks and whatnot. All of these areas have got dramatically changed because we have been able to compile a large amount of data on different attributes and have been able to apply various algorithms, computation on it to be able to come up with solutions. Food remains an artistic endeavor. Food remains personal. Food is like my mother's recipe. It has got a very personal touch. So question is, such a personal topic, can it be transformed into a science in the first place, and that to a data science, such that we can actually bring about innovations there? Well, all of my talk is going to be about that only, to pitch that indeed it is possible. And what is also not possible, I'll mention that in short run. When I look at food, I look at it as transformation of raw ingredients into delicious recipes. All of us are used to and we consume recipes on a day to day basis. So each of this recipe is a product of cultural evolution and has evolved over a period of a few hundred years or maybe more than that. And this food is magical, it's transformational. And incidentally, the ability of cooking is something rather unique to human beings, which is not found in any other species on the face of the earth. No other species on the face of the earth cooks its foods, relishes its foods in its cooked form in the delicious forms, right? And passes it on to from generation to generation. But human beings do that. And it has been argued that the disproportionately large brain sizes that we have is essentially is because one of the primary reason is because we got enough time from foraging because we could actually cook our food and obtain the necessary nourishment from the cooked food. To an extent, Richard Rangham argues in his book Catching Fire that cooking is the very essence of being human. Well, having said that if we have evolved to this extent from primitive species to what we are today homo sapiens, you know, ironically, we have reached a stage where we seem to be plagued with an epidemic of lifestyle disorders, lifestyle disorders such as obesity, type two diabetes, cardiovascular disorders and similar disorders. Why many of these disorders could be factored in after accounting for various other elements such as the genetics and social factors and lifestyle that of sedentary lifestyle, etc. Food is a key component behind the epidemic of lifestyle disorders. And food has changed a lot over a period of time. Notice that maybe around 12 to 15,000 years back, the earliest of the food might have been or the cooked food might have been only roasting of a meat or a food for that matter. Over time, invention of instruments, invention of refrigeration, agricultural biotechnology, breeding, and many other techniques have changed the shape of the food the way human beings have been seen for a long period of time. My the question that I'm asking standing today on 21st in 21st century is how is food going to change now, henceforth, having seen what we have seen so far. My major as I have explained to you a while back is that introduction of large amount of data associated with food, food attributes along with the power of analytics such as that of statistics, machine learning and artificial intelligence is going to change the way we look at food dramatically. That's my major. And accordingly, I have been spending a huge amount of time in my lab. In fact, practically, I'm completely devoted to developing the foundations of computational gastronomy for the last five years. And we have been developing algorithms on various aspects of food. And those aspects include those of traditional recipes, because let us not forget that regard whether we like it or not, all of us human beings, we have been locked into the cultural rubric of recipes and cuisines that we have been evolving over a period of time. So we need to understand that lock in, in terms of understanding the patterns that are present in traditional recipes, we need to look at the physical basis, the flavors, which is what makes them possible to be included in a recipe used or not used, and the nutrition as well as health aspect of food. All of these factors is something that we are building both in terms of algorithms and databases. All of this journey that I've been explaining to you actually started with a very simple question. And that question that we asked when I was still at IIT, Jodhpur was, why do we eat what we eat? Today morning, I ate Dhokla. So why is it that I ended up eating Dhokla? And what makes it possible? So the proximate answers are easy to be given here. That's because the ingredients were available to me and that is what I could think of eating today morning. But the ultimate answer in terms of what make made Dhokla to be available in the form it is today is something which is far more deeper. So we took a very simplistic stance and we said that let's assume that a recipe is characterized by the fact that it is a set of ingredients, as opposed to being individual ingredients. I know it's a very simplistic notion, but let's stick to it for the moment and I'll come back to this point again. So by considering the recipe as a combination of ingredients, this question changes into this one. Why do we combine ingredients the way we do in our recipes? Well, it turns out that one may have an intuitional answer to such a question. And one of them was coming from a chef called Heston Brumenthal from United Kingdom. He had this hypothesis of food pairing and he suggested that ingredient that tastes similar tend to go well with each other. And that's why in our traditional recipes, more frequently you end up finding ingredients that are tasting similar is what he proposed and in fact he used for creating such combinations, the white chocolate and caviar combination, chocolate and blue cheese combination, etc, which are very, very similar in terms of their taste and flavor molecules composition. You need to look at both the aspects, taste something which is perceived by the human beings when you consume it as well as the other aspect is that of the objective aspect, what flavor molecules are present in them. Now this is an interesting hypothesis and in fact this hypothesis was tasted in 2011 by looking at what can be called as western cuisines. I know that this is calling a cuisine as a western or a eastern or an Indian is a rather tricky matter. We need to look at the social factors, anthropological factors, etc, that have gone into the creation of a cuisine. But nonetheless, if I were to bundle all the recipes that are part of some of these countries and continents such as North America, Latin America, Southern and Eastern Europe, etc, then I can call them western cuisines. So this particular principle has been shown to be true in the western cuisine. What we did was to ask this question in the context of Indian cuisine which is diverse, culture rich and has health-centric dietary practices and this question wasn't dealt with in the context of Indian cuisine. So we started looking at the data of Indian cuisine. Now notice all of a sudden food, recipes, cuisines which were otherwise not so scientific in a scientific domain, they were more like an artistic endeavor. They are now turning up into a data endeavor. So we collected the data of recipes of Indian recipes from across various parts of India and this was the earliest data set that we came up with in 2014-15 and that included data coming from TherlaTheLa.com which had around 2500 odd recipes which were belonging to regional cuisines which were across length and the breadth of the country and each of there were around 200 odd ingredients to be specific 193 ingredients which belong to one of these food categories that of dairy, fish, fruit, herb, meat, pulse, vegetable, spices etc. Having got this data of recipes and the ingredients of which these recipes are composed of, the next thing that we did was to look at the flavor basis of these ingredients because the onions and the chilies and the tomatoes and the tamarinds would be chosen or not chosen to be a part of a recipe by virtue of what is their flavor, how do they taste, how do they smell etc. What is their aroma and incidentally flavor molecules which are part of these ingredients, the chemicals are the ones which when we consume this food or when we take it closer to the nose interact with the so-called olfactory and gastatory mechanisms in our body giving rise to a rich sense of the flavor of that particular ingredient be it onion or chili or tomato. Given that we compiled from a large body of literature the flavor molecules that are reported in each of the ingredient so essentially corresponding to onion we had a bunch of molecules which are known to be found flavor molecules which are known to be found in onions and the same was done for each of the ingredient that we had with us. Having created in this manner this is the broad statistics that most of them tend to have relatively small number of molecules you know anywhere between 0 to 25 but there are certain ingredients which do tend to have up to 50, 75 or maybe even 100 ingredients of 100 flavor molecules in them. So this is the statistics of that suggesting that some of the ingredients are pretty rich in terms of their flavor profile and having got the flavor profile most importantly for us we can do the study that was suggested by the food pairing principle and that is to look at what is the similarity of the flavor profile between any two ingredients like in this case of coconut and onion right so how do we do that in the context of a recipe. So we looked at the full cuisine the Indian cuisine itself starting with recipe number one, recipe number two all the way up to 2543 right and we broke down this caricature coconut chutney recipe I deliberately say it's caricature because obviously it's not real recipe where let us say it has got this coconut onion chili and curry leaf we can break it down into its constituent ingredients as shown in the bottom of the slide and further we can actually create pairwise combination of these ingredients like onion and coconut, coconut and chili, curry leaf and chili etc and for each of this pair we can ask this question about what is the number of common molecules between the two right if you do that then you will end up getting one number for each of this pair about how many molecules are common if there is any. Now when you take an average of all of these numbers that you get that comes out to be the food pairing index of that recipe for example it could be that the food pairing index of coconut chutney is let's say 5.31 which is an average number of flavor molecules shared across all pair of ingredients in its ingredients well the beautiful thing that we have ended up doing here that with so many sorry to interrupt over here in its recipe correct with regards to the curry leaf and the onions over here true can you explain a little bit more about what what kind of pairing is being done between something like say coconut and onion just so I have a clear idea yeah so I don't have the exact numbers with me but let us say the coconut and onion have approximately 15 flavor molecules shared between the two okay and similarly coconut and chili may have three molecules shared between the between the two these are molecules which eventually going to illicit taste and order when we consume them and when you take an average of that you will get a single number which is the average number of flavor molecules I hope it is clear and the higher the food pairing is the more complimentary the ingredients are to each other higher the food pairing is more similar are the two ingredients to each other that is true yeah please go on yeah so having got the food pairing index in such a manner for one recipe you can actually do the statistics for the whole cuisine so remember this was one particular recipe for which we did this computation of the food pairing index in terms of flavor molecules is sharing right and that number which comes out from the whole cuisine is the mean value of the food pairing across the whole cuisine and its standard deviation all right so this is done for the statistical purpose why are we doing this we want to ask a question that given the basket of same ingredients same vegetables same dairy products same chili same spices and herbs etc the question is if I were to randomly put together the ingredients what would be the food pairing index because even such a randomized bunch of ingredient will have some amount of sharing amongst them so compared to such random cuisine what would be the food pairing index of a real cuisine that would tell me how uniquely the cultural practices the geography climate etc have affected the cuisine to be what it to become what it is today so we ended up doing this experiment to find out what is the food pairing index of a randomly composed recipes ingredients being exactly the same that it they were in the beginning so when we do this experiment turns out that compared to the randomized cuisine the Indian cuisine the western cuisine tend to have a uniform food pairing which means they tend to have ingredient that are very very similar to each other in terms of their food pairing which would mean that there is a overlap is pretty much higher here in the ingredients on an average which are found in western recipes and that's what we have labeled it as uniform food pairing similar type of it compared to that when we looked at the Indian recipes that we had with us we figured out that the pairing is in fact of a contrasting nature meaning compared to that of a randomized bunch of it we tend to have lesser and lesser overlap lesser and lesser number of molecules that are shared between two ingredients in on an average in Indian recipes and that is what we named it as contrasting food pairing and this turned out to be an interesting observation in the context of recipes from western world again I must point out to the fact that we are stereotyping here but nonetheless I'm going to use the notion broadly speaking the western world versus that of the Indian recipes India itself is pretty diverse but we have taken all of these recipes together from across the length and the breadth of the country well one of the most interesting observation that we did was actually the second one wherein we did a random shuffling of ingredients within a given category so we had bunch of recipes which constituted a cuisine in this case Indian cuisine what we did was to say that look what would happen if I were to take every vegetable in these recipes out and randomly replace it with some vegetable from the vegetable basket everything else being the same everything else is untouched absolutely when you do this experiment turns out that you will have some change in food pairing and that would indicate how sensitive is food pairing to such shuffling within vegetables right so that's the experiment we did for every category that we could manage to do based on the statistical numbers that we had with us and we figure out that compared to the original food pairing that the Indian cuisine had the contrasting one as you can see the shuffling within category for vegetables or fruits or dairy or plant etc was changing the food pairing marginally and still keeping it on the contrasting side whereas if I were to shuffle one category of ingredients randomly the food pairing index was dramatically getting changed and you can actually make a guess about what that category would be and most of you would be right about it the reason being that it's pretty intuitional I would say about which category ingredients of which category are critical in Indian recipes spices we found out that spices are the molecular fulcrum of Indian recipes meaning that shuffling them taking a clove out and putting cardamom or taking cardamom out and putting something else there is actually going to change the pattern of food pairing pattern and thereby probably taste and the aroma of the recipe in a manner that it changes the identity of the cuisine so we concluded by the end of this study I must also highlight that while the contrasting food pairing result is very interesting the fact that spices are the molecular fulcrum of Indian cuisine has remained a robust result despite the fact that we have collated a much larger number of recipes than we had earlier despite that the result has remained pretty robust so this is a very very strong result and something which doesn't change with change in data which has happened because of the very early stages of this particular area um professor Ganesh if I could just like go back to the the swapping out of specific ingredients yeah you had mentioned that when you swapped out vegetables there was much lesser change in the taste profile in the food pairing index how it reflects in taste profile I can't say that it's difficult yeah I don't know that okay but the food pairing index does denote the molecular factory and gustatory molecules that are actually going to play a role so we can extrapolate from it a little bit yeah it's a it's a complex function of molecular composition but yes that's a guess we can make I'm deliberately not making that guess because it would be a long you know hall would be making the jump from there okay and then you're stating that the biggest change that comes in the food pairing index which just throws all the numbers off is when you swap out spices instead of um ingredients like vegetables yeah maybe like a sub-z okay all right thank you yeah that's observation yeah all right so uh what we did is to notice that all of these observations of patterns in the recipes data are very myopic they look at simplistic patterns such as pair wise ingredients and uh they're rather naive so what we did is to create a larger repository of patterns that can be measured in a given recipe or in a given cuisine not recipe in a given cuisine to look at which category of ingredients are dominant which ingredients are far more frequent what are the pair wise combinations which are more frequent which are the triads three ingredients put together which are more frequent and higher orders of combinations etc all of these things put together we labeled it as a culinary fingerprint like a dna fingerprint which uniquely identifies a human based on the patterns in the dna in a similar manner by looking at a large number of recipes that are known to characterize a cuisine one can actually pull out these patterns by looking at their recipes ingredients and the flavor molecules to form culinary fingerprints that's what we ended up observing and finding it out and publish it later going forward this culinary fingerprints actually tell us about the role of a particular category of ingredient like spices vegetables dairy dairy products etc how do they contribute towards the food pairing index that is what we measured and this is just an illustration of a South Indian cuisine and how does it look all of this research that we did in 2015 got a lot of press and also academic recognition it was identified as best of 2015 and also highlighted as an emerging technology by MIT technology review and also many people such as Veer Sangvi and many other outlets across more than 200 out media outlets highlighted our work as the future of the food and importantly industry was not far behind in recognizing the fact that bringing data and computation together with food is going to revolutionize food in a big way although it will take its own sweet time to make it happen so they identified it as the sweet spot of the food what I'm going to do now in the next 15-20 minutes is to tell you about what is the future that we are looking at what is the future that we are building in our lab when it comes to computational gastronomy and while saying so I must also highlight what are the shortcomings of the work that we are doing as of now notice that computational gastronomy is where physics was maybe in 18th century it's a very early stage food is a very very subjective matter very personal matter it has got variety of associated factors you know geographical climatic personal interpersonal relationship and whatnot apart from the physiological basis of food that it may have so therefore untangling all of these parts to bring out food in its full glory when it comes to computational gastronomy is going to take its own sweet time and this is the story of a spherical cow though those of you who have not heard about it physicists and mathematicians are known to make this mistake of when given a challenge of modeling a cow they will start with the assumption that let's assume that the cow is spherical well turns out that there such assumptions are useful at times but at the same time they would not be representing the reality in the best possible manner therefore the kind of studies that we have done while they are useful interesting give us insight into a social anthropological factor such as food at the same time it doesn't actually let us to the depth of food at the level at which we would like to go and therefore let me tell you what are the assumptions that we made we said that recipe is an unordered list of ingredients of course not recipe is an ordered list of ingredients and quantity matters the process which goes into cooking also matters similarly we also assume that an ingredient such as tomato or a chili is nothing but a bunch of flavor molecules practically speaking but in reality that is not the case the concentration or the quantity of each of the flavor molecule that goes into the ingredient matters or how that molecule is interacting with the taste and like order receptors matters a lot right so there are many such factors which have gone ignored and I must tell you that we are trying to build upon that whatever is level possible and it's a new niche that has been created therefore the number of people who are working and the number of people who are willing to spend their time dedicate their time in building the foundations are relatively few and I'm proud of it that our lab is trying to do this job of putting things together of this piece of puzzle what we are trying to do in our lab is to bring about data-driven food innovations by using this data centric and AI centric platform and there are three dimensions practically mainly speaking that we are looking at although there are other dimensions that I'm not going to talk about today one is that of the recipes that how can we dramatically change the way we look at recipes second is that of nutrition and health how can we make food more tasty more nutritious more healthy and how can we change food at the level of molecules is these are the three different angles that I'll be telling you in the next 15 minutes or so before I can conclude so the first angle was that recipes had been looked at in a very simplistic manner so what we have done going ahead is to compile the recipes not only in a couple of thousand but in hundred thousands so we have now this repository of 118 thousand plus recipes which is called recipe DB now getting published in a journal called database wherein we have compiled the recipes from across the world that includes 24 world regions and 75 countries in a computable format unlike a traditional recipe resource where you can go and search by name of a recipe here you can search by complex queries show me all recipes which are having tomato chili but doesn't have turmeric from Algeria Algerian cuisine and such questions can be asked which can potentially be answered by recipe DB provided the data is available there and we have of course a lot of attributes the type of recipes it is the type of cooking processes which are being used in the recipes such as frying boiling sauteing so we have such 160 odd processes which going to it starting with very simple such as add stir to all the way up to more complicated ones and importantly we have broken down the recipe to the level of its quantity unit being used such as teaspoon spoon and similar and the kind of temperature state and size at which they are cooked all of these have been also mapped to their nutritional data from United State Department of Agriculture nutritional data to estimate their nutritional profile the agenda here was to come up with a system where given a recipe in a human readable format it should be able to extract all relevant information and tell us about especially its nutritional value both micronutrients as well as micronutrients that was the objective with which we worked on it and this is not a job done completely this is the step one the V1 is what we have created and we hope to build this further to make it far more applicable we have gone further and looked at these recipes from various parts of the world and have done culinary fingerprinting of these world cuisines remember we had done it for Indian recipes Indian regional cuisines and we have gone further and done it for the world cuisines to be able to map what is what are the patterns in these cuisines across the world one of the interesting question that we have been asking him in our lab is that like languages have their own way of similarity in them the phonemes that we use them the letters which are used in written in language based on that we can construe the similarity of the languages we know for example how Punjabi Urdu and Hindi are similar and that way Telugu and Kannada are very similar both in spoken as well as in written language and thereby we can actually build a tree which would have a Dravidian branch versus that of an Indo-Arabic branch etc so the question is can we also similarly learn from the patterns of the cuisines without going into much of the details and come up with a tree of cuisines that was the idea behind it imagining almost as if cuisines have been evolving over a period of time although there is a far more subtlety that goes into this notion because we are borrowing from biological evolution but nonetheless the primary idea is acceptable and we have built such lang cuisine tree similar to language tree and this is the tree that I was showing to you one of the question that we are asking in our lab is theoretically how many recipes are possible and the number turns out to be astronomically large the average number of a ingredient in a recipe is around 10 and the number of ingredients that are available to be put in these slots are around 1000 with a at a very very conservative estimate in reality the number is in tens of thousands but I'm taking a very small number which is definitely agreeable and with that you get a number which is 10 to the power 30 which is astronomical astronomically large so the question here is why is it that the cultures have evolved recipes which are far less in number at any given estimate the number is far less than 10 to the power 30 right very very very very less minister so question is can we actually come up with strategies by which we can learn from the recipes that have been already evolved and build new recipes well that's the question we are asking in our lab our inspiration is also coming from a movie called ratatouille I'm sure many of you would have seen this movie called ratatouille where chef Gustav has this catchphrase where he says that anyone can cook so the question here is that can the intuition intelligence of a chef can be built into a program an algorithm which can spew out recipes which are new recipes remember chefs and culinary enthusiasts are known to tweak do try an error and identify which recipes are good and come up with new recipes probably over a period of time in fact many people don't argue with the fact that you can build a knowledge repository of cuisines across the world like we have tried to build in recipe DB but how will you build in the intuition that's a big factor and we are trying to do that using the deep learning algorithms that are in vogue right now and they have been trying to replicate the style of writing of Shakespeare the style of music synthesis of Beethoven and we believe that we should be able to regenerate expert chefs intuition with the help of large number of recipes and in a structured manner they are not just bunch of words but where we know this is an ingredient this is a quantity this is a unit and thereby can we build new recipes is the question that we are asking so it's a very very challenging and interesting dimension that we are working towards coming to the second dimension that of flavor molecules and the molecular space wherein I would like to tell you that until now as I said it's a new area therefore there was no existing repertoire of flavor molecules found in natural ingredients those which are found in tomato ones which are found in chilies and etc it was not available until now we have built this repertoire called flavor DB which he was also made available as a flip app it has been taken down now for upgradation purpose but this repertoire provides the most exhaustive collection of flavor molecules that are reported in natural ingredients and the idea is also to come up with applications out of it very soon while the agreement is still to be done but very soon we may deal with a big company which brings you into a voice answering system the flavor DB app where you can do food pairing with the help of flavor DB one of the applications of such a compilation of flavor molecules wherein molecules and their tastes are put together is that of predicting taste can you predict given a molecule and its molecular structure what taste it would be of would it be bitter sweet or tasteless we have built algorithms which have been now published state-of-the-art algorithms which can predict taste and you would wonder what are the possible applications of this well finding out no well molecules which are sweet but at the same time don't add calorific value is one of the most challenging problem in this diabetes ridden world and that's the challenge that hopefully our algorithm will help addressing towards eventually we would like to build a food maker's guide to the galaxy synthetic molecules are nothing new to food and flavors and beverages industry fragrances industry for that matter what we are trying to suggest is this that the number of organic compounds using carbon hydrogen nitrogen oxygen and some stray elements that go into organic compounds can be built is also infinitely large but can you do it in a manner that you can recreate a particular taste order aroma can you do that well that is what we are trying to build as part of the food maker's guide to the galaxy the last angle that I would like to touch upon is the space of health apart from dealing with the recipe nutrition and flavor molecules part well always we come across contradictory assertions about how a particular food is good but at the same time also bad for you through newspaper articles which are writing their report based on some scientific research published somewhere well I believe that part of the reason why these assertions are made in a contradictory manner is because of the way the food interacts complex in a complex manner with the body giving rise to health implications right so this interaction is not so straightforward or simple as shown in this particular picture and therefore there is a need to integrate all the data that is available about food ingredients and their health or disease associations that's what we have done we have dug out the data of pub med and med line medical the scientific repertoires where in scientific research is published about a particular ingredient being good or bad for you and have put together a repertoire of 2200 or food ingredients and their disease associations going further we have also integrated these data with that of chemicals which are found in the food genes which are known to be present in the body which interact with the chemicals food chemicals etc so barring barring the food and gene interaction every other data is empirically available and we have created this repository called diatrics and we are in the process of analyzing it to find out major signatures that come out of it one of the utility of such a repose repertoire was to ask the question about why do we use spices in our food so without getting into much of the detail very briefly the proximate answer to this question is because of the fragrance and flavors in 1998 back to back papers Sherman and billing had actually pointed out that spices and herbs culinary herbs and spices tend to have antimicrobial properties beyond their fragrance and flavor that we are we know them for and therefore they may be used for that purpose what we have shown in 2018 paper after 20 years of their work is that beyond microbial properties the culinary herbs and spices also tend to have broad spectrum of benefits across cardiovascular disorders immune system disorders intestinal disorders etc they have beneficial effect and these conclusions have been drawn from a large number of research articles there are 38,000 odd research articles that we have in our repertoire well where is it all moving towards to conclude in my opinion all of this research of computational gastronomy blending food and data with computation is taking us towards a world where food would be personalized in the world of covid that we live today we know how much important it is for us to be having a strong immune system which helps us cope up with viruses such as covid 19 so the question that one was asking in this particular paper that I'm showing you from Weisman institution was can you build a machine learning algorithm a program which by measuring a variety of body parameters would be able to club it with a particular food or a diet that a person will consume and will be able to predict finally what is the post-prandial glucose level the increase in glucose level in the body of that individual well turns out that this particular paper is one of the landmark papers in the area of food and nutrition and it actually showed us that indeed it is possible for us to come up with personalized nutritional predictors and I believe personally that we are moving into that world where personalized nutritional prediction predictors would be would be routine and would be using them all the time going beyond the broad predictions that we tend to make today so in my lab we have been building applications of various sorts including food beverage pairing prediction of taste and order fingerprinting cuisines with culinary fingerprints dietary intervention creations beverage and food design as well as sustainable food innovations using the computational resources and repositories that we have created not to mention the algorithms that we have been building all the while and this is also leading towards a startup called gastronomic ascetic which blends food with artificial intelligence and tries to come up with new inventions when it comes to food innovations so I'm going to end this story to tell you what I told you in the beginning that I was an aspiring astronomer while I could not become an astronomer I ended up becoming a gastronomer a computational gastronomer at that and I remember this quote from Ayukha inter-university center for astronomy and astrophysics where I was working as a master's thesis student that the discovery of a new dish confers more happiness and humanity than the discovery of a new star so while I have not been able to discover new stars by being an astronomer I believe I hope I wish I should be able to discover new dishes with the endeavors that I am right now doing with the computational gastronomy work so with that I would like to thank you for listening patiently and I'm open for question and answers just give me one minute when I could actually thank a variety of people who have helped in me in this endeavor wherein we are trying to put knowledge data and applications together in my lab a large number of PhD students m-tech students b-tech students youngsters who have helped me who have been my collaborators who have contributed to different aspect of the work that I've done without which most of it would not have been possible only ideas would have been possible but implementations would have wouldn't have been possible right so I should thank them all of them and many of them have been summer interns so we have a very active summer internship in my lab which goes on every year right now also it is going on in online mode so with that thank you note I get back to you for question and answers