HackMITea: A Hackathon From the Eyes of a Physics Major

Recently, I’ve had the opportunity to visit a friend up north and experience my first ever hackathon, hosted by none other than MIT. Every year, undergraduate students from all over the world gather to Cambridge to attend this student-run event. The goal of a hackathon is to get as far as possible in realizing an innovative project that aligns with an overall theme. For HackMIT, we had 24 hours to design a tech-based project focused one of four areas: sustainability, education, health and accessibility, and interactive media. In working on my project, I developed my coding skills a bit while getting to witness firsthand what life is like for both a CS major and an MIT student.

This piece is called Alchemy, and it was my favorite statue at MIT

The Opening Ceremony

On the day of the competition, we all gathered in a huge gymnasium for the opening ceremony. I was immediately blown away by the sheer number of competitors lining the bleachers in preparation for the our guest speaker, Ali Partovi, to kickoff the hackathon.

Endless tables of competitors planning out their course of action for the next 24 hours.

When the ceremony finally began, we were introduced to all the sponsors, and at this point, I gained my first piece of insight into the computer science community. All of the sponsors at the event appeared to be tech-based companies, with some of them, including Partovi, being representatives of mentorship programs designed to facilitate the innovation process for aspiring computer scientists. This along with sponsors ranging from cryptocurrency to health technology painted a picture for me of a community that fostered ambition in achieving goals teetering between wealth and altruism.

After the sponsors were introduced, Partovi began his speech, which was, interestingly enough, one of the highlights of my time in Cambridge. Apparently an avid Matrix fan, Partovi attempted to kick off his talk by sharing several scenes and quotes from The Matrix, but he was met with technical difficulties, resulting in a few awkward yet entertaining minutes of Partovi adamantly trying to resolve the issues and share with us the masterpiece that is the 1999 original version of The Matrix. Eventually, he succeeded and we were rewarded with a geeky adoration of the classic film, with Partovi confessing that he even named his mentorship company Neo after the movie’s main character. Partovi highlights a conversation between two characters in the film that discusses the idea of not having to “dodge bullets.” And from this, he segues into his main discussion.

The rest of the speech was centered around Partovi’s experience in the tech industry and about not being afraid of failure, which he calls “bullets.” Though outside of that primary purpose, there were some questionable decisions in his talk. He began by discussing a time when he failed to close a deal with Steve Jobs because he acted dishonestly, which appeared to be a valuable lesson on its own, but he then followed this discussion with a time he lost out on a deal for being too honest. I will leave it to the reader to decide the moral of this story. Afterwards, Partovi took the opportunity to promote his mentorship program Neo and in the process make a comment that later sparked a feud with another mentorship program sponsoring the event.

The Project

After the ceremony, my team took a moment to explore the MIT grounds before beginning our project. Within the gymnasium itself, there were sponsors hosting independent coding challenges and offering freebies. I recognized this as a so-called “networking opportunity” that I always hear CS majors talking about. There were also various workshops, including one on how to work with Hugging Face AI models, which was interesting, and one on how to mint NFT’s, which was less interesting.

Although many groups stayed in the gym, we decided to leave and find a more quiet place on campus to work instead. Our journey led us all throughout MIT, which had a very interesting layout. The entire campus was a vast network of buildings and hallways with an interconnected basement system. One hallway in Building 17 can lead you to Building 22. It was as if the entire school were its own small indoor city. This was not what I expected when I heard the rumor that MIT students don’t go outside. Eventually, our journey led us to the MIT banana room, a room that provides students with an endless supply of bananas as they slave away on their homework assignments or prepare for their next exam. We sat down on some brightly colored bean bags amidst a wall of banana paintings and got to work.

The banana crates in MIT’s banana room, providing much needed potassium to the students of MIT.

We chose to work in the interactive media category. Our initial goal was to design a game using a large language model. The premise of the game involves two parties, guessers and pretenders. The guessers would have to choose a prompt for the pretenders to answer, but among the pretenders is an AI that will also respond to the prompt. The goal of the guessers is deduce which response was AI generated, while the pretenders’ goal is to fool the guessers into choosing incorrectly. In challenging the guessers to distinguish an AI from real people, the game highlights how far AI has come in mimicking human speech and thought. On the other hand, pretenders are challenged to “act like an AI” in the sense of thinking on their feet and producing a seeming clear and cogent response according to various prompts.

Unfortunately, late into our project, we realized that we were too ambitious and would not be able to create a presentable program in time, so we decided to scrap the project in favor of a rhythm game involving hand gestures, aptly titled Hands Dance Revolution. By feeding data of different hand poses to a convolutional neural network, we were able to train the network to identify when those poses were being made on camera. We then used the Pygames library to create a basic game that kept a count of how many times the hand gestures were correctly matched in a given time limit. This game was a basic proof of concept of our actual idea, which would have been a game similar to a hybrid if Just Dance and Dance Dance Revolution in which hands would be positioned and posed to the beat of a song to score points.

The Conclusion

The creation of this project required several new skills. Although I had experience in Python with data science, using Python to make games was entirely new to me. My friend spent hours watching countless tutorials on convolutional neural networks. It was not until 6 in the morning that I went to bed for an hour, shortly before it was time to turn in our project. When the moment of truth came, we did not make as much progress as we had wanted, but we at least had a presentable project for the judges.

The fruits of our labor, the interface of our project. It’s not much, but it’s honest work.

After the window for projects closed, we had about an hour to prepare for our presentation on our project. During that time, we spoke with other teams, one of which had a project that I particularly liked. They designed an web extension that used an large language model trained on lecture transcripts in order to answer questions for students watching the lecture and redirect them to the appropriate timestamp in which relevant information is discussed.

When it was time to present our project, I spoke enthusiastically about our game idea and the next steps for our program. However, the judges did not seem too interested in the video game angle. It was not until one of my group members improvised and discussed the potential that our project had in helping people learn sign language or recover motor functions that the judges began to get excited. What I had not realized was that it was not just the project that the judges were assessing but us and our ability to sell our project. Throughout the presenting period, we were given advice by the judges on how we could better market our project and make it seem more appealing to potential investors. Once again, I was reminded that CS majors were very externally motivated and ambitious people.

A picture of my team with one of the judges. We turned something in, and that is all that matters.

Unfortunately, we did not make it to the second round of presentations, but this experience is something that I will never forget. Overall, I had fun seeing the sights in Cambridge and immersing myself in something new, but as a physics major interested in becoming a researching professor, I could not match the energy of most of the other competitors, who were more interested in working for big companies and creating startups. However, because of my different interests, the stress of the project was not as bad for me as it seemed for many other people. As an avid seeker of new experiences, I would definitely recommend doing most things at least once. But in particular, attending HackMIT or another hackathon appears to be an excellent way of deciding if the atmosphere of the computer science world is right for you.

Your journey starts here! Let’s talk!

ThemeRex © 2026. All rights reserved.
  Timothy Durham was convicted of rape when a test showed his DNA genotype matched the DNA recovered from the crime scene. Eye witnesses testified that Durham was in a different state at the time of the incident, but he wasn’t exonerated from his 3000 year sentence until follow up DNA analysis showed that the preliminary forensic analysis used in the trial was misinterpreted (Thomas et al.). DNA testing serves an invaluable part in our criminal justice system, but it comes with complications and bias that can lead to wrongful convictions and misinformed juries.  

Background:

Forensic genetics aims to identify the origin of a biological sample. It can be traced back to Karl Landsteiner who discovered human ABO blood groups. With blood groups, suspects could be eliminated based on their blood type. For example, if a suspect had type A blood and there was type B blood left at the scene by the perpetrator, the investigators could rule out the suspect. Unfortunately, the reverse didn’t work. If the suspect and perpetrator both had the same blood type, it can’t be concluded that the suspect was guilty. Blood identifiers evolved into 8 distinct categories which improved the ability to eliminate suspects, but could only conclude that (if the system matched) it might be the perpetrator or this person might be related to the perpetrator (Jobling and Gill). The field evolved in the 1980s with the discovery of Minisatellites, lines of repetitive DNA that don’t encode proteins. Minisatellite patterns in DNA create what we know today as the DNA fingerprint, and it has a much higher accuracy rate than previous methods (Augustyn et al.). Around the same time, a breakthrough for victims of sexual assault was discovered. In the case of assault, bodily fluids of both the victim and the assaulter can get mixed. A process called differential lysis was created which could selectively increase the concentration of sperm in a fluid sample thereby allowing for accurate DNA testing (Jobling and Gill). Forensics evolved to use single-locus probe testing after several years because of its ease of interpretation. It looked at specific minisatellites that had high variability between subjects. This was the first DNA-based method to be used in a criminal investigation. Two murder homicides from several years apart were connected using DNA evidence. The evidence was then used to exonerate an innocent man, and the guilty party was found when he tried to evade mandatory DNA testing for all men in the small town. His guilt was then reinforced when the DNA profiles matched, and he gave a confession (Jobling and Gill). Finding DNA at a crime scene doesn’t guarantee that you’ll find a murderer. There are issues with non-pure samples and limited samples that make DNA work difficult, and accreditation of facilities and new techniques and take time (Jobling and Gill).  

Subjectivity of DNA Interpretation:

In a 2009 publication, the National Academy of Science discusses the possibility for subjective influence in lab based forensics. The interpretation bias happens in complex cases where, for example, there could be a mixture of DNA. A study published in 2011 looked specifically at this issue. A gang rape happened in Georgia, and the mixture in DNA from the case was used to prosecute one suspect after another suspect took a plea bargain to testify against him. With DNA mixtures, there are three possible conclusions: suspect excluded, suspect cannot be excluded, and inconclusive. If the suspect is excluded, it means that there’s little to no possibility that his DNA is part of the sample. If the suspect cannot be excluded, it means that there’s a possibility that his DNA is part of the sample. In the trial, the DNA examiners who knew the context of the case concluded that the suspect being prosecuted could not be excluded. The 2011 study gave the DNA evidence from this case and the DNA profiles of the suspects to 17 forensic examiners. These examiners had no context of the case, and they all worked independently. Of the 17, only 1 came to the same conclusion (suspect cannot be excluded) as the examiners who knew the context of the case. Twelve of the 17 concluded that the suspect who was prosecuted and convicted had no relation to the DNA found in the sample. Not only does this show that subjectivity occurs due to the context of the case, but the 17 examiners came up with different findings based on their past experiences meaning that not all DNA analysis is objective. Some organizations like the Scientific Working Group on DNA Analysis Methods say that quantitative statistical evidence should be presented with every categorical conclusion, but the US doesn’t require this (Dror and Hampikian).

False Positives:

There are two categories that cause wrongful identification in a DNA report: coincidental matches and false positives. Coincidental matches are DNA matches between two unrelated people. The probability of these matches is about 1 to 500,000 for white individuals and about 1 to 350,000 for black individuals. This small probability of coincidental matches makes people feel confident in DNA evidence. False positives, on the other hand, are matches between DNA and test DNA that result from issues in the forensics lab. There have been multiple cases of forensics labs accidentally switching victim and suspected rapist DNA which lead to convictions that were later overturned when the lab realized the error. There are also cases of misreading or misunderstanding a sample. In a case from 1993, a man was convicted of rape because someone interpreted a mixed sample of victim and perpetrator DNA as an individual sample. The conviction was again overturned. To prevent false convictions, retesting DNA is allowed, but it’s not an efficient solution. Many people are pushing for statistical information on the probability of false positives, so that the jurors can make accurate judgements based on DNA evidence (Thomas et al.).  

Conclusion:

DNA analysis is a useful tool for the criminal justice system, but subjectivity can be an issue in forensic testing. To combat wrongful convictions, there are organizations like The Innocence Project who use DNA testing to free the innocent, but reformed forensic testing standards should be put in place to prevent subjectivity and to ensure juries are correctly informed during trials. https://www.innocenceproject.org/    

Citation:

Augustyn , Adam, et al. “DNA Finerprinting.” Encyclopedia Britannica , 26 Apr. 2018, www.britannica.com/science/DNA-fingerprinting#ref153186. Dror, Itiel E., and Greg Hampikian. “Subjectivity and Bias in Forensic DNA Mixture Interpretation.” Science & Justice, vol. 51, no. 4, 2011, pp. 204–208., doi:10.1016/j.scijus.2011.08.004. Jobling, Mark A., and Peter Gill. “Encoded Evidence: DNA in Forensic Analysis.” Nature Reviews: Genetics, vol. 5, no. 10, Oct. 2004, pp. 739–751., doi:10.1038/nrg1455. Thompson, William C, et al. “How the Probability of a False Positive Affects the Value of DNA Evidence.” Journal of Forensic Sciences, vol. 48, no. 1, Jan. 2003, pp. 1–8., doi:10.1520/jfs2001171.