Generative Art: One a day
Art created using code. Varies from combining GANs and diffusion models with CLIP to generating simple flow fields. Since the beginning of 2022, I have tried to create one piece a day.
Art created using code. Varies from combining GANs and diffusion models with CLIP to generating simple flow fields. Since the beginning of 2022, I have tried to create one piece a day.
LARC (Language-annotated Abstraction and Reasoning Corpus) is a dataset I collected with Yewen Pu and other post-doctorates in the MIT Computational Cognitive Sciences lab. ARC is a benchmark for abstract thinking ability in programs, and our dataset augments ARC with hundreds of natural-language descriptions which capture the entirety of the task, collected through a multi-person communication game.
For my final project for the class Advanced Natural Language Processing (6.864), I worked with Lowell Hensgen to explore how individual neurons contribute to the final answer of large multimodal models, as well as how model explainability relates to model performance on a suite of tasks.
For my final project for the class Computational Cognitive Science (9.66), I explored computationally inferring the moral values of different cultures using a hierarchical Bayesian model. To do this, I focused on The Moral Machine domain, a database of tens of thousands of decisions made about moral dilemmas from individuals around the world.
API and app that enables anyone to create and customize stock-trading algorithms without code, just by tuning parameters. The program runs a plethora of trading algorithms on the cloud involving quantitative analysis, sentiment analysis, and GRU.