Mohit Goyal

Mind the GAP is the first work that demonstrates that GAP significantly reduces nausea, disorientation, and total scores of cybersickness as well as subjective discomfort scores as compared to DP. We present a comprehensive protocol aimed at evaluating visually-induced discomfort and cybersickness in VST HMDs through key use cases. We hope that our comprehensive protocol sets a foundation for future studies aimed at refining these systems and enhancing user comfort in VST technologies.

Interactive object understanding, or what we can do to objects and how is a long-standing goal of computer vision. In this paper, we tackle this problem through observation of human hands in in-the-wild egocentric videos. We demonstrate that observation of what human hands interact with and how can provide both the relevant data and the necessary supervision. Attending to hands, readily localizes and stabilizes active objects for learning and reveals places where interactions with objects occur. Analyzing the hands shows what we can do to objects and how. We apply these basic principles on the EPIC-KITCHENS dataset, and successfully learn state-sensitive features, and object affordances (regions of interaction and afforded grasps), purely by observing hands in egocentric videos.

@inproceedings{goyal2022human,
  author = "Goyal, Mohit and Modi, Sahil and Goyal, Rishabh and Gupta, Saurabh",
  title = "Human Hands as Probes for Interactive Object Understanding",
  year = "2022",
  booktitle = "Computer Vision and Pattern Recognition (CVPR)"
}

Single-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined.

We consider lossless compression based on statistical data modeling followed by prediction-based encoding...

@INPROCEEDINGS{9418692,
  author={Goyal, Mohit and Tatwawadi, Kedar and Chandak, Shubham and Ochoa, Idoia},
  booktitle={2021 Data Compression Conference (DCC)}, 
  title={DZip: improved general-purpose loss less compression based on novel neural network modeling}, 
  year={2021},
  pages={153-162},
  doi={10.1109/DCC50243.2021.00023}}

Sequential data is being generated at an unprecedented pace...

@inproceedings{goyal2019deepzip,
  title = "DeepZip: Lossless Data Compression Using Recurrent Neural Networks",
  author = "Mohit Goyal and Kedar Tatwawadi and Shubham Chandak and Idoia Ochoa",
  year = "2019",
  booktitle = "Proceedings - DCC 2019"
}