I am a PhD student in Computer Science at Columbia University, where I am co-advised by Carl Vondrick and Rich Zemel. I am interested in developing AI systems that can reason about the word in complex, human-like ways, and applying these systems to real-world problems in healthcare and climate change. My research is supported by the National Science Foundation Graduate Fellowship.
I am trained in Indian Classical Music - I perform vocal concerts in the US and India, and am passionate about spreading awareness of this art form. I also enjoy yoga, playing tennis, and hiking.
Arjun Mani , Will Hinthorn, Nobline Yoo, Olga Russakovsky
VQA Workshop 2021 (Poster Spotlight)
We extend Visual Question Answering (VQA) to questions requiring pointing, a key component of human communication, and introduce benchmark datasets and model designs for this new question space.
Arjun Mani, Ryan Adams
Currently working on bringing the mathematics of geometric algebra into the computations and latent space of deep learning models. Our goal is to create more powerful and expressive internal representations using multivectors instead of vectors. Starting with word embedding models.
Assaf Hoogi, Arjun Subramaniam*, Rishi Veerapaneni*, Daniel Rubin
IEEE Transactions on Medical Imaging, vol. 36, no. 3, March 2017
We improve on the level-set segmentation approach by using convolutional neural networks to adaptively modify parameters controlling for the expansion and contraction of the outline. The approach is fully automatic once an initial point has been provided. We achieve ~10% improvements on MRI and CT datasets of lesions.
Arjun Mani, Stephen Meier, Thomas MacCarthy
Finalist, Regeneron Science Talent Search
We address the problem of predicting the effect of a mutation on an antibody’s binding affinity, in an attempt to identify beneficial mutations for antibody design. Our deep learning-based approach significantly outperforms existing methods.
ORF 569 Theory of Deep Learning, Fall 2021
Article coming soon!
Using random label experiments, we examine the extent to which pruning methods at initialization are data-dependent. We also examine the expressivity of these networks (e.g. their ability to fit true vs. random labels) and analyze their characteristics compared to networks pruned after training (e.g. using the lottery ticket hypothesis).
COS 521 Advanced Algorithms, Fall 2020
We provide theory showing that certain graphs will never converge to a correct opinion even if each node is initially biased towards it; we also empirically examine majority dynamics and the effects of seeding or higher thresholds for changing opinions.
COS 529 Advanced Computer Vision, Spring 2019
I study how to understand pointing gestures that have ambiguous intent, examining how ambiguity can be explicitly predicted by semantic segmentation models.
Best Overall, HackPrinceton Spring 2018
We built a chatbot that can respond to user voice/typed commands in realtime and perform linear regression, machine learning (SVM, logistic regression), and basic data analysis (mean/mode/etc.). Requires user only to upload a CSV file of the dataset.