QMW Testimonials

As a biology major, sometimes it seems hard to branch out into computational research and incorporate those tools into wet lab work. However, this workshop showed me many computational applications to biology and neuroscience, such as machine learning and single-cell sequencing data analysis. These multidisciplinary lessons allowed me to explore new ways of approaching and visualizing data. Meeting faculty and spending every day with a diverse group of students also expanded my understanding of the future of science.

Although I did not have any coding experience before the workshop, I did not feel overwhelmed. The lecturers and teaching assistants were patient and helped me understand programming languages like MATLAB. I also enjoyed interacting with the other participants and hearing about their interests in science. The skills I learned during the workshop will help me accurately identify patterns in my own results and communicate my findings to others in the future.

This was an incredible experience. The faculty and graduate students I met were from very different backgrounds, but they formed a community of people who deeply enjoy research. I had never considered how computer science related to biology, but the workshop made it evident that tools like MATLAB can be incredibly useful. I will apply what I learned to my own research to analyze data more efficiently. I also feel more confident in exploring other computational and quantitative methods, and applying them to my work.

I learned programming languages like MATLAB that have helped me in classes and allowed me to analyze data in a project I recently started. Tools like this will help me become a better engineer, scientist, and researcher. I treasured meeting grad students, undergraduates, and faculty from different backgrounds and sharing stories about overcoming obstacles. These experiences taught me that, with perseverance, I can achieve my goals.

I learned different ways to analyze and visualize large amounts of data, various statistical analysis methods, as well as basic deep learning to look for trends. My favorite part was meeting researchers currently working at MIT during the small group sessions. I got exposure to the kinds of research other people are working on, and had the opportunity to ask people who are where I want to be (in terms of career path) how they got there, so that I am more prepared for my future as a researcher.

I was already familiar with MATLAB as it applies to engineers, and during the workshop I learned that this programming tool can also be used to analyze and solve problems in biology. I left with valuable skills that I will use in my research, which will make my data analysis much easier and more organized. I also enjoyed networking with faculty. They come from interdisciplinary backgrounds, and talking to them helped me gain a sense of confidence and belonging, reaffirming my academic aspirations and showing me that they’re possible.

The new skills I gained in this workshop are invaluable. It showed me how computer science could be helpful, and in some cases absolutely necessary, to validate and facilitate many different fields of research in biology. Specifically, I learned how to use MATLAB, which complements my other programming languages and will help me in lab. My favorite part of the workshop was getting to meet the amazing faculty at MIT, and having one-on-one conversations with them. It was a truly memorable experience.

The Quantitative Methods Workshop was an incredible experience. I was able to design and teach a workshop on Python, and test strategies for communicating these ideas in a way that resonates with students from many different fields: computer science, chemistry, biology, etc. One student even emailed me after and said the workshop inspired them to enroll in an introductory programming course. These things make the experience so rewarding, and I look forward to teaching again at the 2018 workshop.

The workshop was an incredible experience. I was able to meet and network with amazingly bright and motivated students, and the intense, fast paced program ensured we all made the most of our time while fostering teamwork. I was exposed to the programming languages used in MatLab, ImageJ, and Python. During a fellowship that same year, I used my newfound skills to write a program and analyze dozens of z-stacks efficiently — allowing my team to draw important conclusions from the work I had been doing.