I am a computer science graduate from Boston University interested in artificial intelligence, security research, and software engineering.
My most recent role was as an AI/ML team lead at BU Spark!, an experiential learning lab and quasi tech consulting firm with clients including the Harvard Herbarium and a handful of social impact nonprofits. In my role, I architected ML projects ensuring my teams were making steady progress towards meeting client expectations.
Recently, I've been building generative AI (Stable Diffusion, LLMs) and vision (CLIP, BLIP, ViT) projects. I am specifically interested in multimodal, ensamble, and augmentation research. See my GitHub for more projects!
I am seeking projects and work in these areas. If you would like to collaborate with me on a project, message me below!
Research paper on a model that predicts text prompts from generated images using an ensemble of multimodal models, including CLIP, BLIP, and ViT. Uses a high-quality dataset and of 100k custom generated images, cleaned to have low semantic similarity.
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Deep learning project and paper to forecast world events outside of the training data using large language models (LLMs) including GPT-3, GPT-3.5, and GPT-4. Prompt engineering techniques including few-shot learning, expert identification, and inference averaging are employed to increase accuracy.
View PaperAn 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.
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A vision transformer based machine learning pipeline built to classify plant specimens and organize records, built for the Harvard Herbarium & BU Spark!. The pipeline is composed of an ensamble of fine-tuned OCR and NER models along with a ViT plant classifier to pick correct classifications in the correct context.
Visit RepoDeep learning project to create a model that finds similar videos using a dataset of 1000s of videos. Scored 8th place of 212 participants in Meta AI’s Video Similarity Challenge in the more difficult "matching" track.
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Tail-recursive interpreter designed to parse, push, and execute 12 unique commands on a custom made stack. Compiler implemented to compile a traditional programming language into custom-built stack language.
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