Through His Research at Yale University, Instructor Mark Torres Uses Natural Language Processing To Study How Ideas and Information Spread Around the World
Mark Torres recently graduated from undergrad at Yale University this past May (class of 2020) with a degree in Statistics & Data Science. For his research and thesis, Mark used NLP to study how information (and misinformation) is spread online. He is particularly interested in how NLP can be used to learn more about how people communicate and spread information and, more broadly, studying how things spread (e.g., information, ideas, diseases, etc.). Mark is currently doing ML research for a bioinformatics lab at Yale University and is considering doing a Ph.D. in the near future.
How did you choose what you studied in college? How did you end up studying AI?
“I actually came into college wanting to study philosophy and medicine. I thought that I would read a lot of books, dissect a lot of animals, and eventually end up in medical school. I first pivoted with I wanted to do psychology research, since I wanted to learn about how the mind works. This gradually slipped into AI as I began wondering how we could create computer programs to think as a human thinks. I eventually decided to study AI officially, majoring in statistics, because I was able to use AI to work on interesting questions, such as how people come up with decisions and how information spreads over platforms like Twitter.”
What research/industry work within AI have you been focused on? What excites you about the future of AI?
“I've worked on a variety of AI-related projects. Much of my exposure to AI comes from using it in the context of social sciences. For example, I worked in a lab that uses AI to track how fake news spreads online. More recently, I've been using AI in the context of genomics, studying how we can aggregate information from commonly-available data sources to predict people's genomic profiles. I'm really excited about the potential of AI to complement our daily lives. We already see things such as Siri, Alexa, Google Maps, and other applications becoming a fundamental part of our lives. I'm interested in seeing how we can further integrate AI into other aspects of our daily lives.”
What has been the highlight of your teaching experience with Inspirit AI?
“Seeing the "aha" moments in the students' eyes, as they see how we can seemingly miraculously use computer programs to do complex things such as make self-driving cars or detect the presence of cancer cells or create applications such as Siri.”
What advice would you give to young people who are interested in studying AI or pursuing a career in AI in the future?
“Get really good at math and CS! At its core, AI is a combination of high-level math and programming, so having a strong foundation in these fields is "the cost" that you pay in order to be making these cool, interesting machine learning and AI programs (though, in reality, the math and programming end up being interesting enough that the journey, though it'll be a lot of hard work, will be hopefully fun along the way!).”
