I'm a software engineer, electrical engineering grad student, and guitar player/singer-songwriter.
Many people have asked me over the past few years if I could make websites for their bands or personal portfolios, so I've decided to take on some part-time projects. The first site I built is for the band Time King. I worked with my friends in the band as well as with graphic artist Louis Costa. The site is static and built on GitHub pages from scratch. I also have experience putting sites into production on Amazon Web Services using frameworks such as Django and Flask.
I graduated from Cooper Union's Electrical Engineering undergraduate program in 2014. Since then, I have been working on my Master's in the same department, with an expected graduation of December 2016. My coursework has spanned signal processing, machine learning, circuits and electronics, and computer science and engineering.
My Master's thesis is trying to denoise single channel audio recordings, particularly from concerts or other live events. In other words, can we remove crowd noise in an unsupervised way from a recording taken with a smartphone or handheld camera? This becomes particularly important when professional recording equipment is unavailable, like for the DIY kind of shows and events I like to go to. :)
Methods of noise removal in studios can involve removing hum in a relatively isolated environment. Since noise like that is stationary, you can usually just record the noise and then "subtract" that out, either spectrally or right in the time domain. However, you can't do that with crowd noise with people yelling, cheering, talking, dropping things, clanking, etc. More robust methods need to be used. As much as I dislike buzzwords, Deep Learning and Machine Learning appropriately come into play here, as deep neural networks can be used to try to model this noise. With a better approximation for noise, it follows that this noise can be removed from the noisy recording.
I'm currently playing guitar in Little Alien. We've had some success playing house shows on Long Island and at venues such as Revolution Bar & Music Hall and Amityville Music Hall. The best way I can describe our music is "technical but fun to listen to." Kind of like Prog meets Math meets Indie Rock.