I'm a Senior studying Computer Science at Stanford University. I am focused on Machine Learning and am involved in research at the Stanford Vision Lab. I am interested in CS education and have headed many education initiatives, such as TA-ing multiple high level AI classes during my undergrad and founding and teaching an AI class through CS + Social Good.
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Spearheaded the development of Microsoft’s new cutting-edge Deep Learning-based handwriting recognition effort. Experimented with input features, model architectures, and schedules to reach recognition state-of-the-art result set by Google Research.
Worked with Salesforce Research time on a project which involved predicting diagnoses in pathological slides using AI. Ran experiments on full-slide pathology data and iterated based off of experimental results; manuscript in progress
Worked on a couple projects. The first was a project involving artistic creativity with GANs. The second is a dataset collection and cleaning project, with the goal of releasing the dataset to the public.
Worked with interdisciplinary group to help democratize AI education. Created educational content on deep learning and iterate through drafts, survey target audience to create better content
Devleoped state-of-the-art NLP models in production-ready environments using GPU-optimized TensorFlow. Created deep learning framework in Python that will be used to develop, test, and deploy models across the org.
Worked in Ng group on applying AI to medicine. Led the group focused on applying Computer Vision techniques to classify abnormalities in ultrasounds of the leg.
Interned on ML team. Designed, developed, and back-tested Deep Learning models on historical financial data. Delivered research to Nasdaq through internal whitepaper.
Prototyped three business models in nine days to solve the problem of facilitating future connected markets using Blockchain. Delivered three business models and respective products to IDEO.
An article describing the current state of Neural Style Transfer, as well as my contributions to the space
An article that introduces the reader to neural networks by guiding them through building a simple NN
Final paper for "Convolutional Neural Networks" class project, a cutting-edge research project on neural style transfer
This past year, I took Stanford’s CS 231n course on Convolutional Neural Networks. My final project for the course dealt with a super cool concept called neural style transfer, in which the style of a piece of artwork is transferred onto a picture. Here’s a classic example–a picture of Hoover Tower at Stanford, in the style of The Starry Night:
In my last post, we used our micro-framework to learn about and create a Convolutional Neural Network. It was super cool, so check it out if you haven’t already. Now, in my final post for this tutorial series, we’ll be similarly learning about and building Recurrent Neural Networks (RNNs). RNNs are neural networks that are fantastic at time-dependent tasks, especially tasks that have to do with time series as an input. RNNs can serially process each time step of the series in order to build a semantic representation of the whole time series, one step at a time.
I am pleased to announce that I have published my first Python library: Krikos!
