Software Engineer
Hello! I'm a current student at the University of Michigan studying Computer Science. Previously, I worked at Capital One as a Software Engineer Intern and did development for high-traffic, internal platforms. My main interests are in systems, but I'm also interested in general backend, platform, and infrastructure work. Outside of computer science-y stuff, I love lockpicking, playing microstakes-NL Poker online, doing strange puzzles in various subjects, racing, and building my motorcycles (goal: expert motorcycle race license by 2028).
Software Engineer Intern · Summer 2024
I worked in a team responsible for an internal platform that orchestrates over 5 million outbound customer messages daily. My intern podmates and I spent ~1/3 of the internship period developing shared package libraries for the team's different applications; the rest of the summer was spent developing timezone-aware scheduling workflows for four different AWS Lambda services in the platform's architecture. I also got to use a lot of interesting C1 internal tooling. Overall, 8.5/10.
Software Engineer Intern · Summer 2023
This summer, I worked on incorporating AutoPay into credit recovery payment plans. It was my first exposure to Java (and Spring) and also my first ever time writing rigorous tests in a production environment. So, I spent the first two weeks learning what Spring (Boot) actually does and decrypting the giant codebase we are working on. Still, we didn't actually get to design a lot of stuff so this summer definitely was not as involved as my previous. 7/10.
Software Engineer Intern · Summer 2022
My first summer at Capital One and my first time working in a software engineering role in general! Alongside with just one other intern, I worked in a team responsible for reward points settlement in partnered credit cards (think Macy's CC and stuff like that). We were tasked with designing and fully implementing a prototype for a batch processor that can service 30,000+ transactions daily; the goal was to replace a legacy system still servicing existing rewards transactions for one specific partner. It was a lot of work (and fun) coming up with AWS architectures and even more work actually implementing it. Overall, it was a rewarding summer internship, just wished it was longer so we could finish up testing and maybe even see prod-deployment. 9.5/10.
Goal is to make this my dedicated trackbike (MAYBE compete in the middleweight twin's class). Right now, it's mostly stock other than ergos (rearsets) and crash protection. The bike has the best handling I've ever experienced and the Aprilia QS is very smooth as well.
With a help of a friend, I put together this insanely spec'd out race bike; we installed an exhaust, Vortex ergonomics (rearsets and clipons), crash protection, Ohlin's rear shock, and a quickshifter. This model is the absolute best bike in its class, and I'm sad to eventually sell it to dedicate more time to the Aprilia.
Currently enrolled. Description coming soon!
Currently enrolled. Description coming soon!
Undoubtedly the most difficult class I have ever taken. However, it is also probably the best class I have ever taken. The professor (Dr. Chen) is amazing, and the projects are head-bashingly difficult (I spent more than 80 hours on p3) but also very rewarding once you get them right. It is also unquestionably the most useful EECS class I've taken in terms of both quality and quantity of net-knowledge gained. Projects and lectures taught me core principles that I will probably never forget since they are ubiquitously applicable in virtually every computing system. 10/10.
A fairly rigorous class covering many different subjects in machine learning: linear regression, linear classifiers, SVMs, neural networks (incl. CNNs, RNNs, Transformers), decision trees (RF, boosting, etc.), clustering, bayesian networks, reinforcement learning, and probably a couple of other things I'm missing. It covers A LOT of material, and I wished I had taken it in an earlier semester in place of EECS 492 since I wasn't necessarily able to give it my all due to my busy schedule. Rating could be a lot higher if I wasn't taking EECS 482 concurrently. 9/10.
Lovely class with an amazing and seemingly always-prepared instructor (Suraj). He is so good at explaining new ideas that I don't think I was confused or lost in the material even once throughout the class. Despite not being very time-consuming, homeworks were very instructive and offered a lot of indepth insights not fully explained in lectures. 10/10.
Amazing class and fantastic professor (Halderman). All of the projects were not particularly difficult or time-consuming, but they were all for the most part very fun (except p1). The final project is undoubtedly the most fun I've had doing any assignment at this school. My only criticism is the exams are too difficult, making it impossible to do well in the time allotted. 9.25/10.
This class is a typical survey of artificial intelligence. It went over many search algorithms (including adversarial search which I found very cool), constraint satisfication problems, reinforcement learning, linear regression, decision trees, and even attention. Despite all of the content covered, I can't say I learned much. The projects weren't that involved and required little thinking/coding. 3/10.
It was a standard data structures & algorithms class, nothing to really write home about. Both professors who teach the class has been teaching it since the extinction of dinosaurs and are very good at what they do (GOAT Dr. P). The projects weren't particularly trivial but not difficult either; the class's difficulty is definitely overstated online. I also liked how labs and exams were structured, unlike some other core CS classes in the curriculum (ahem, 370). 8.75/10.
This class is a survey of "low-level" computing and is effectively a preview for computer architecture (EECS 470). The class covered digital logic, bit manipulation, linking, assembly (with ARM and LC2K, a toy 32-bit RISC architecture), processor design (pipelining), caching, virtual memory, and some low-level code optimizations. I enjoyed the lecture material and projects thoroughly but hated how homeworks/lab assignments were structured. 7/10 with strong 9/10 potential.
This is the school's canonical "introduction to proofs" class. It also serves as a gentle but fairly rigorous introduction to linear algebra. Nonetheless, it was my first Michigan math class and the first rigorous class I took here. Do I remember even 1/3 of the material? No! But it was a very rewarding class, and nothing beats the feeling of completing a proof after being stuck for hours. Also, bonus points for this class for such well-written homework assignments. 9/10.
This class went over fundamental theory and applications (discrete & continous); it was a pretty fun, (sometimes) intuitive class. For a 400-level math class, I expected more theory, but I still enjoyed it a lot. 8/10.