Ankit Goyal

I am a Ph.D. student in Computer Science at Princeton University. I am a member of Princeton Vision and Learning Lab, advised by Prof. Jia Deng. I completed my Masters in CSE from University of Michigan and Bachelors in EE from IIT Kanpur. Previously, I have interned at Microsoft Research India with Prateek Jain in Summer 2016 and with Shrikanth Narayanan and Tanaya Guha at USC in Summer 2015.

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I am interested in understanding various aspects of intelligence, especially reasoning and common sense. In particular, I want to develop computation models for various reasoning skills that humans possess.

Think Visually: Question Answering through Virtual Imagery
Ankit Goyal, Jian Wang, Jia Deng
ACL, 2018

We study geometric reasoning in the context of question-answering. We introduce Dynamic Spatial Memory Network (DSMN), a deep network architecture designed for answering questions that admit latent visual representations.

ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, Prateek Jain
ICML, 2017

We propose ProtoNN, a novel algorithm that addresses the problem of real-time and accurate prediction on resource-scarce devices. ProtoNN is inspired by k-Nearest Neighbor (KNN) but has several orders lower storage and prediction complexity.

A Multimodal Mixture-Of-Experts Model for Dynamic Emotion Prediction in Movies
Ankit Goyal, Naveen Kumar, Tanaya Guha, Shrikanth S. Narayanan
ICASSP, 2016

We address the problem of continuous emotion prediction in movies from multimodal cues. We propose a Mixture of Experts (MoE)-based fusion model that dynamically combines information from the audio and video modalities for predicting the emotion evoked in movies.

Object Matching Using Speeded Up Robust Features
Nishchal Kumar Verma, Ankit Goyal, A Harsha Vardhan, Rahul Kumar Sevakula, Al Salour
IES, 2016

We propose a robust algorithm which is capable of detecting all the instances of a particular object in a scene image using Speeded Up Robust Features.

Template Matching for Inventory Management using Fuzzy Color Histogram and Spatial Filters
Nishchal K Verma, Ankit Goyal, Anadi Chaman, Rahul K Sevakula, Al Salour
ICIEA, 2015

We propose a methodology for object counting using color histogram based segmentation and spatial filters.


I have been a Teaching Assistant for the following courses:

  • COS429: Computer Vision at Princeton University [Fall 2018]
  • EECS442: Computer Vision at University of Michigan [Fall 2017, Winter 2018]

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