I usually write about data science in my blogs, whether that's recent projects I've been working on, goals I've been attempting to reach, or facts I've learned along the way. Today, however, I want to share something a bit different. Throughout this year, I've been volunteering online every week for "Turks Without Borders," an organization that works to accelerate the English-learning process of students in Turkey by connecting them directly to American students. Each week, f

I recently wrote about how I started reading Introduction to Machine Learning with Python by Andreas C. M ü ller and Sarah Guido. So far, the guide has been quite helpful for learning the basics behind training a model––it offers digestible explanations, concise code, and references to every related source. There are 4 key pieces of information that I have gathered thus far: Many AI models are trained to make predictions for new data points based off of existing ones––often

The development of LLMs (Large Language Models) like ChatGPT and Claude has made it exponentially easier for large amounts of code to be generated in short amounts of time. What might've once taken a team of professional programmers hours can now be accomplished, by any average person, in less than ten minutes––and all you need is a simple prompt. Rightfully, many are nervous about these new AI systems. If one prompt can accomplish the same and more as hired employees, then w

It took me two months to identify a crucial design fail in my platform. ClassPulse, said platform, is a website I am developing that aims to narrow the gap between students and teachers in learning environments. ClassPulse began as an experimental project, where I sought to discover just how far curiosity and personal research could take me for a rather ambitious goal––after all, I am not so well-versed in programming that I could derive a website from a blank document. Howev

Recently, my most pressing issue with the development of AI model at MIT has been refining the data so that it doesn't reflect irrelevant variations. With every data set coming from multiple experiments or sources arrives one considerable issue: small differences in the recordings (typically due to varying biological practices) that can affect results in undesired ways. However, feeding imperfect data to an AI model, the issue I am currently focused on, is just one of many co

Entering the Koch Institute at MIT earlier this December, I was pleased to be greeted yet again by the light bustling of students and researchers, the fast-closing elevator doors, and the faint smell of rat enclosures––though last was definitely not as pleasing. My mission at MIT was simple––discuss the grounds for a new project involving AI for cancer research, specifically for the small intestine. Our team of five, including a computer science PhD, a cancer research profess

As we pass through October, many high schoolers have already started to prepare for what is arguably the most important standardized test in high school––the SAT. One data-related question that the SAT loves to bring up is about the box plot, a simple chart that depicts useful features of a data set like the median, interquartile region, and range. A simple box plot looks like the image below: Today, I will be clarifying how to recognize these features on the box plot chart t

The use of AI chatbots like ChatGPT has become the new hot topic for high schools like my own. Over the past few years, we students have seen many changes––often upsetting ones––in assignments, tests, and grading due to the blooming use of AI. My school, for instance, recently oversaw a significant transformation in essay writing, essentially replacing many out-of-school writing assignments with their in-class counterpart. Our education system seems to believe that it's bette

When I first arrived at the Koch Institute, a research facility at MIT, I expected to spend all my time analyzing tumor growth and dissecting mice. I falsely assumed that, as a cancer research lab, there wouldn't be much diversion from the purely biological elements of the study. I certainly wasn't expecting my computer science knowledge to become my most useful attribute. On my first day of the internship, I spent most of my time observing and catching up on their most recen

" What course are you taking? " That was probably the most commonly asked question at Brown this summer, usually spoken between two students who arrived from miles away, hoping to make new friends on campus for the duration of their two-week stay. Most people had easy answers: "Tropical Marine Biology. What about you?""I'm doing Cancer Informatics." They'd smile, nod, and start asking questions about each other's class. For me, it usually went a bit differently. " What course

Recently, while scrolling through articles on the New York Times , I came across a paper about the creator, Rhayid Ghani, of a nonprofit called "Data Science for Social Good." Curious to know more, I spent some time exploring the nonprofit's page and familiarizing myself with its mission. Data Science for Social Good (DSSG) is a platform where data and computer scientists can come together and form/build upon a project tackling a specific social concern. One particular projec