 Next, we have Nishadi Kiriel from the A New College of Engineering and Computer Science and the title of Nishadi's three minute thesis is, linking data for a better tomorrow. If I tell you I've been to mountains that whisper secrets of the past and the future, what would you imagine? An enchanted forest in a Disney movie? Not really. I'm a data science researcher and these mountains are made up of lots and lots of data. Today, we have 7.9 billion people on this earth. That is almost 8 billion birth certificates. If we include our ancestors, this is way more than 10 billion and we are adding more every day. Do you know these mountains of data have secrets that could change the lives of people? For instance, there are some cancers that we can inherit from our parents through mutated genes. One in every 10 breast cancer is due to a strong family history. If we can identify such cancers early from their family history, we can potentially save lives. I am working on building family trees such as this one here. For instance, to identify people with a family history of cancer. We have databases of birth, marriage and death certificates of our population. My birth certificate has my parents' details. Their birth certificates have their parents' details. Brilliant. We can link them together and build this tree. What? You are doing research just to link a couple of certificates? Just use the medican numbers and link them. I've heard that many times. Unfortunately, in the past, no systems such as Medicare existed. We are left with the names, addresses and dates of births and deaths. Names are often written differently. They can contain errors and they change over time. The same name can be shared by many people. In my research, I developed machine learning algorithms to solve these challenges and link millions of certificates going back to the 1850s. Instead of checking if two names are exactly the same, I calculated a score to check how similar the name of a baby to the name of a bride on her marriage certificate. In this similarity score, I capture the ambiguity of names, spelling variations in them, the characteristics of the relationships and so on. Using these similarities scores, my algorithms automatically link a baby to a bride, then a bride to a mother, until the whole population is reconstructed. With that, my goal is to identify that one person among every 10-plus cancer patients much earlier and more accurately than is possible today. Maybe then, the secrets behind the mountains of data will help us save many lives. Thank you.