Caltech Stem

On Tuesday, March 24th, we had a meeting with Professor Hassibi over Zoom. Prior to our talk, we had issues with our code regarding the fact that it didn't produce the results expected. He told us to find and graph the eigenvalues of the graph Laplacian, as that would indicate the number of clusters found and value you can refer other datapoints to. He noted that graphing the corresponding eigenvector would indicate whether a cluster had been isolated or not. On that note, it was up to us to tinker with values for a bit and try different algorithms like K-means. Below is a video of the meeting with Professor Hassibi: This week has been very interesting, to say the least. As you may know, COVID-19 has just taken the U.S. for a turn and has put a pause on daily life, forcing businesses to adapt, overcome, or fail in some cases. For us high schoolers, classes have migrated to the Zoom platform. With it followed teachers with bad internet service, leading to indecipherable dialect...

       After our meeting with Ethan, Dr. Hassibi's graduate student, we were tasked to implement spectral clustering on the adjacency matrices dataset. In order to determine the algorithm's integrity, we have to use false positives and false negatives. As mentioned, the first step was to clean the data and use spectral clustering on it. We soon realized that the spectral clustering function would only work on R2019b. We downloaded R2019b and it recognized spectralcluster as a function. During the weekend, I did a bit of self-study and tried to teach myself MATLAB syntax which I found similar to Python 3.8. Below is a video that I found helpful. The error displayed in the console when we tried to use spectralcluster on R2019a        The first step is to load in the dataset which is set as a struct, a block of memory with physically grouped variables. Assign observed  and rawAdj  to CAdj  and Adj  respectively to t...

Last Thursday, we met with Ethan Abbasi, Dr. Hassibi's soon-to-be Ph.D. graduate, to discuss our project on semi-crowdsourced clustering. He needed little time to understand our project and dataset which meant that we could get right into possible algorithms. The first one that was mentioned was "Spectral Clustering", one that was extensively researched last semester, and Ethan gave us advice like adding matrixes together to achieve cliques. He also had us think about performance metrics (a way to measure the success of an algorithm). Two metrics we thought of were mathematical representations of false positives and false negatives. In the last attempt to brainstorm algorithms, we brought up min-cut. However, as demonstrated by Ethan, without understanding convex optimization, min-cut would be very inefficient. A photo of topics we have discussed during our meeting with Ethan This weekend during robotics, the programming team, including myself, has successfully im...

       Ramya Korlakai Vinayak, a graduate from Caltech under Prof. Babak Hassibi, graciously offered us a dataset that was labeled using MTurk (Amazon Mechanical Turk), a crowdsourcing marketplace. The folder contained four MatLab files: Birds5EdgeQuery300workers , Birds5TriangleQuery285workers , Dogs3EdgeQuery300workers , and Dogs3TriangleQuery320workers . The naming convention is as follows: Animal Classified + Number of Breeds + Comparison Query Method + Number of workers. It is still vague as to which one we will use for our project but they are all very similar in some sense. Each file has 6 variables: Adj : Adjacency matrix under standard rules (and -1 = unobserved) AdjWithMultiples : Adj  with entries observed more than once due to random queries CAdj : logical, mapping matrix to tell if an edge is observed (1) or not (0) count : number of edges observed (no multiples) groundtruth : actual breed of the animal m : number of nodes in the graph ...

This week has been interesting, to say the least. Something I have achieved over the break was finishing my application for the BU Rise program. The summer research camp in Boston is very intriguing to me because I get to work with professors on their research. This activity is something I might want to pursue in the near future which is why I really want to attend the program. My application has five essays that took me quite the time to complete. I asked friends and teachers to look over my pieces. This caused my works to under several iterations but in the end, it was all worth it. Wish me luck! Above is a video describing what students do during BU RISE. I guess there's also the fact that I want to visit Boston during the summer. I came from Massachusetts to California after 8th grade. It's been 3+ years and you bet I'm missing my home. I think I even had a plan for when I get there. First I want to meet up with my friends at Lexington and afterward go see family...

On the Efficiency of Data Collection for Crowdsourced Classification :        The paper aims to explain the accuracy gaps between popular collection policies (state-action mapping) on crowdsourced data. The quality of crowdsourced data is often highly variable so it is suspected that it is the cause. However, studies show that the policies used to collect such data have a strong impact on the accuracy of the system. The first theoretical explanation of the accuracy gaps is that the non-adaptive uniform allocation, and the adaptive uncertainty sampling and information gain maximization. Done with the representation of the collection process in terms of random walks in the log-odds (log of odds of guessing correctly/incorrectly) domain, the author derives lower and upper bounds on the accuracy of the policies. The bounds accurately tell us by how much do two adaptive policies trump a non-adaptive one. It is believed that techniques used by them are applicable to addi...

       This past week I was hit by a cold that knocked me out for nearly a week. I missed two and a half days of school because of it. I believe I caught it from Matthew Lee after he showed up to robotics practice after his episode of the flu. I suspect this because the day after I felt absolutely horrible. Catching a cold/flu at this time was especially not good because of recent news about the Coronavirus spreading to the U.S. I got worried but my symptoms weren't matching up so I was in the clear. I spent the next few days quarantining myself in my room to not get my peers and family sick. Below is a video detailing what the Coronavirus is in case you haven't seen the news:        Also during this week, I learned about the legal strife between Caltech and Apple which resulted in a total victory for the educational institution. The bout was regarding Apple infringing on Caltech's Wifi chip that can correctly perform tasks on encrypted dat...