Participants from the micro-internship keep journals as they progress through the two-week program. Use the drop-down menus below to read about their experiences!
Claire Camacho 
Claire Camacho is a sophomore at MIT studying Computer Science and Neuroscience. She is interested in the intersection of data, software, and neural engineering, and its applications to social justice. Claire spent the summer of 2022 interning for a cybersecurity startup in Tel Aviv and teaching Python to a binational group of Israeli and Palestinian high schoolers. She is passionate about improving equitable access to technology and STEM education, which guides her work with Harvard’s Women in Technology conference, SWE, and the Women of Color Professional Initiative at MIT. In the future, Claire hopes to continue her exploration of AI and grow her interdisciplinary skills in technology through undergraduate research and summer internships.
Orientation + Meeting 1; Choosing our project
Hannah Kim 
Hello! My name is Hannah, a sophomore at Harvard studying CS and Statistics. During the Try AI program, I explored the subfield of XAI, which is the field that studies ways to make Artificial Intelligence more interpretable and understandable. In the future, I hope to be working in the intersection of technology and social impact, whether that is through research, industry, or business -- I am honestly still figuring it out. Some social impact areas that I am passionate about right now are education, government, and climate. Here is my LinkedIn! Always happy to chat with more people.
Meeting #1: Orientation and kickoff
Alex Wong 
In our first meeting, Jessie and I had a discussion on how expansive our project should be, in addition to learning more about each other. For the project, the first topic was the research that Liz had sent to me over break, in addition to some papers that Jessie had sent. We tried to narrow down the scope of our project, and eventually concluded that setting up a game that simulated illegal fishing/overfishing would be doable within the two weeks of the micro-internship. It also helped that Jessie had lots of prior experience with games, and I learned that her first foray into academia as an undergraduate was studying how to reach socially efficient outcomes in games.
One point of interest that we discussed was subsidies when I mentioned that according to the paper, a lot of fishing would be unprofitable without subsidies. We realized this could be a good avenue of further research, so my task for our next meeting was to dig deeper into the impact of subsidies on fishing.
First, we reviewed the fishing subsidy literature and the main takeaways that I had found. I found through a few studies that large percentages of fishing would be unprofitable without subsidies, and we decided that this could be the center of our project. My main homework for the next meeting was to try to find any public documents from Taiwan or China about their subsidy policies. Specifically, if I could find an algorithm that they used, or take any public data to create an algorithm, we could have a good start for a maximization problem.
We briefly discussed what this optimization game might look like. Jessie gave the example of choosing between fishing for tuna or another fish based on expected profits, and if we could know what the subsidies are, we could more easily set up the game. On the other hand, we decided that If we couldn’t find any information, we’d use a rough approximation instead.
The next topic of the meeting was how to structure a game. I also had the following questions: How does AI come into it? What model would a game use? That’s when Jessie discussed gradient descent, which was about following the regularized leader. In a game, verifying Nash equilibrium is easy, but finding it is hard. There’s a lot of guess and check involved, but when you use gradient descent and libraries that do the optimization for you, it becomes much easier to do these calculations.
We had scheduled our next meeting for Thursday, but unfortunately, I got sick and it made it pretty hard to talk, so we decided to discuss my findings over email. My email mainly talked about the subsidy algorithm I had found in a highly detailed paper by Oceana. It listed out all of China’s fishing subsidy policies and included how to calculate many of them. I gave a report about the most important ones: tax breaks, fuel subsidies, and vessel subsidies, as well as the algorithms involved in each.
In response, Jessie helped to further define the maximization problem we’d use, based on two types of fish that could be caught (tuna and squid). She also made a graph that showed what the basic idea of our problem would be
Our penultimate meeting was a review of what we had discussed in the emails, as well as a further exploration of how we’d be implementing the game. We specifically talked about the cvxpy library, and Jessie sent me some materials including some starter code that would help code the game.
We also talked about the final presentation. We decided that I would work concurrently on the presentation and implementing the game in cvxpy, with higher priority to finishing the presentation. She sent a slideshow called “Giving a Talk” by Dan Larremore, which was about how to conduct a great presentation. We decided that I’d give a large portion (around half) of my focus to the “story” of overfishing and illegal fishing, which is the context behind the project.
In our last meeting, I went through all the slides in my presentation. Jessie gave some advice on how to make the slideshow better. Some of the most important advice included making the transitions between slides clearer (ie how does this slide connect to this slide), and which slides to prioritize and which slides to cut down to meet the time limit (specifically, doing less on explaining the concept behind gradient descent, which would take up a lot of time that could be spent on the project).
I’m confident in saying that this was a great program. I thought the balance between independent work and guidance from my mentor was perfect. This program is really about “You get what you put into it”, and I personally enjoyed the self-motivated nature of the work. I also liked how we could direct the project in any way we wanted to combine both my interests and my mentor’s expertise. Jessie is awesome, and her knowledge was super helpful to me in navigating this space, whether it was reading academic papers or setting up a game with a library I’d never used before.
Finally, I thought it was great to know more about Jessie as a person. Talking about her journey through academia and why she decided to take the path she did was extremely interesting and valuable, and I appreciate that this program had a focus on this personal side too.
Nathan Moreno 
Journal 1 - 1/11
In our first meeting I prepared a small slides presentation for my mentor to outline two possible problems I would be interested in tackling. One of my ideas had to do with making predictions about morphogenesis in developing organisms using AI and the other was about segmentation and tracking of cells using AI. I was unsure which would be a more realistic problem to work on for this internship, but Hyewon (my mentor) said that I was noticeably more interested in the first problem. She said she could see “my eyes light up” when I talked about it and asked if I was planning on researching this question/topic in the future. Hyewon encouraged me to work on the morphogenesis question because it would give me foundation for future research and satisfy me intellectually. She clarified concepts (convolutional neural networks, u-nets, and backpropagation) from the articles I showed her and helped me find supplementary resources on deep learning. Her encouragement excited me not just about the program but also about my future career in research!
Journal 2 - 1/13
For my second meeting, I prepared another small presentation about one specific article I had read (Exploring The Behavior of Bioelectric Circuits using Evolution Heuristic Search). She helped me differentiate between what aspects of the study were artificial intelligence versus simulation models and she helped me narrow down my problem statement. Additionally, we discussed how AI could be applied further to the problem, what kind of datasets could be used (synthetic vs. real), and how supervised/unsupervised learning works. She suggested I look into recurrent neural networks and generative models. For next meeting I was told to download the programs used in the study I looked at and start to play around with them just to get a better understanding of the work
Journal 3 - 1/18
For the third meeting, Hyewon and I met briefly to discuss what I had learned from programs I downloaded. She assured me that in research, researchers often start by downloading code from previous studies and manipulating its parameters to gain a better understanding of what has already been done. Additionally, I laid out what my presentation would go over and we discussed some final details about how recurrent neural networks and supervised learning can be used in the prediction of morphogenesis!
Journal 4 - 1/19
For our last meeting we looked over my presentation and Hyewon gave some final comments and suggestions. She suggested that I add slides not just about the problem statement I ended up focusing on but also about all the other topics and ideas we looked into. This way, I could “present my accomplishments” better. Hearing her say this was extremely encouraging because I felt as if I had not done enough for the final presentation without actually implementing any code, but Hyewon was able to reassure me that I had put a lot of effort into forming an idea and researching a range of topics. After the TryAI experience, I definitely feel more prepared and confident to start research projects on my own!