I'm a computer science student from Southern California studying in Boston. I’m interested in security research, machine learning, and software engineering.
I am currently working at Spark! as a machine learning project manager. Recently, I've been learning about adversary emulation frameworks & pentesting, supervised, unsupervised, & deep learning concepts, and scalable serverless deployment.
I am really excited about how my adversary emulation framework project turned out! Its special features include a hybrid encryption scheme, messaging apps to disguise communication, shell code injection, and a number of measures to avoid detection. Check out the research poster below, and see my GitHub for more!
I am constantly striving to improve my skills and seeking to collaborate on interesting projects!
An adversary emulation framework with special features including use of a messaging app as a communication channel, a hybrid encryption scheme, and shell code injection. The implant is served by a containerized listening server, MySQL database, and Flask app.
See Research Poster
A software pipeline to classify historic natural history specimens, built for the Harvard Herbarium & BU Spark!. The pipeline is composed of custom-trained, fine-tuned, and pre-trained models in conjunction with structural pattern matching algorithms to pick correct classifications in the correct context.
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An app that uses pre-trained on-device machine learning to identify when to take the perfect group photo. It tracks features such as smile and blink probabilities.
Email: en (at) bu (dot) edu
If you are interested in my work, email me or shoot me a message below!
