Internships and Research Positions

Selected Publications

We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations. The dataset covers over 6,000 m2 and contains over 70,000 RGB images, along with the corresponding depths, surface normals, semantic annotations, global XYZ images (all in forms of both regular and 360◦ equirectangular images) as well as camera information. It also includes registered raw and semantically annotated 3D meshes and point clouds. The dataset enables development of joint and cross-modal learning models and potentially unsupervised approaches utilizing the regularities present in large-scale indoor spaces.


Locality Prior

A wiring cost for neural networks.

Flame Wars: Automatic Insult Detection

Detecting abusive comments with char-LSTMs.


Summarization of Articles using LexRank for Texts via Intermediate Embdedded Representations. Extractive text summarization on the NYT dataset.


I love teaching, and think that it’s an important part of research.

I have TA’ed:

  • CS331b: Representation Learning in Computer Vision (Fall 2017)
  • CS103: Mathematical Foundations of Computing (Winter 2015)