Matthew T. Sit
Hello! I graduated in 2019 from UC Berkeley with a major in Electrical Engineering & Computer Sciences, and a minor in Bioengineering. During my time in undergrad, I conducted research in Computational Biology labs and I taught the data structures course at my university, as both a teaching assistant and a lecturer. In Summer 2018, I was a software engineering intern at Citadel. As of September 2019, I have become a software engineer at Google / YouTube.
hours of teaching experience

" I really like the attention to detail in your programming style. Also documentation is just super awesome. I just wanted to acknowledge again the fact that you’re a truly outstanding programmer because in academia most of the time what one accomplishes can often go unrecognized. Most importantly, thanks for showing that you are highly dependable person! "

// Mehmet Ağaoğlu, former Post-Doctoral Fellow with the Chung Lab (currently at Apple).
2017 - 2019
Chung Lab, Berkeley Optometry
In psychophysical vision experiments, patients are shown a stimulus on a screen, and are asked to respond. An essential source of data collected is the movement of the patient's eye in response to the stimuli, which is recorded as a video by a Scanning Laser Ophthalmoscope (SLO). I developed open-source, modularized, and also standalone software to computational analyze this retinal imaging data, which we call ReVAS.
ReVAS on GitHub
Lab website
2018 - 2019
Bender Lab, LBNL/UCSF
When a neuron is stimulated by an electrical signal, a voltage response is emitted. An accurate model of this gives us a cheaper way of performing experiements, and a better biophysical understanding of the system. By compartmentalizing our model and assigning parameter values, we can compare empirical voltage responses to our generated predictions according to a learned linear combination of scoring functions and an analysis of the sensitivity of each parameter. I architected a pipeline for running optimizations, visualizing results, and organizing our vast quantities of datasets through a self-documenting format called NWB (which is based upon HDF5).
NWB writeup
Lab website
2014 - 2015
Yeh Lab, UC Los Angeles
When antibiotics are used to combat bacteria, a full course must be delivered in order to ensure that any uneliminated bacteria do not have the opportunity to replicate and allow its population to evolve resistance. Failure to avoid imposing naturally selective pressure can have catastrophic public health consequences, as bacteria can evolve more rapidly than we can produce new drugs. I analyzed the popular and scientific press’s use of evolutionary terms to understand how this phenomenon is presented.
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PLOS publication
PeerJ publication
" Has a great attitude during lecture, as well as a sense of humor students can relate to. Makes super effective analogies that make course material easier to understand, and is well organized during lecture. "

// Student course evaluation, Summer 2019.
" Matthew definitely made a point to discuss why the material had personal interest to them. His attitude was contagious and promoted a positive learning atmosphere. "

// Student course evaluation, Summer 2019.
" He's the best GSI I have ever had. He has such calming, happy presence and made the material very accessible for everyone in the class. HE'S SO AWESOME!!!! Responds to emails extremely quickly, follows up on conversations with emails if a question was still unclear, genuinely cares about all of his students. I've never learned so well in a discussion section. "

// Student course evaluation, Spring 2019.
industry and projects
Citadel LLC
I enhanced the business utility of the firm's mission-critical market connector datasystem by modularizing it into dynamic library plugins complete with C/C++ bridges for compiler-independence.
Firm website
Generating Better Commit Messages
We generate commit messages from git diff logs, comparing the performance of state-of-the-art language models including recurrent neural nets with LSTMs, the Transformer, BERT, and GPT-2.
Colorimetric Detection of pH Strips
We apply machine learning techniques and models to determine the pH of chemical solutions given raw image data of pH test strips. We report our findings in comparison with those in the literature.
We create an Android app that uses facial recognition to streamline attendance taking in the classroom. Developed using Java at HackDavis 2017.
We create a Facebook Messenger Bot that leverages IBM Watson's natural language classifier. Developed using Node.js and Heroku at SFHacks 2017.
Independent Project
Automatic Ride Coordinator
I fully automate the role of ride coordinator, saving up to 30 minutes of time every week. Developed using Google Scripts.
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let's chat!