Rahul Duggal
I'm an Applied Scientist with the AWS-AI Computer Vision group. Previously, I got my PhD in Computer Science from the Georgia Institute of Technology where I was advised by Polo Chau and Jimeng Sun.
My research focuses on empowering AI on edge devices (e.g. smartphones, smart watches). This requires developing neural networks that are:
- Efficient to run on resource constrained platforms.
- Robust to distributional changes in the test data.
- Resilient to class imbalance in the training data.
If you work in related areas or are just curious about my work, don't hesitate to reach out to me via email.
Github  / 
Google Scholar  / 
Email  / 
CV
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Publications
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Robust Efficient Edge AI: New Principles and Frameworks for Empowering Artificial Intelligence on Edge Devices
Rahul Duggal
PhD Dissertation, Georgia Tech, 2022
[Thesis][Slides][Video (coming soon)]
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Towards Regression-Free Neural Networks for Diverse Compute Platforms
Rahul Duggal,
Hao Zhou,
Jun Fang,
Shuo Yang,
Yuanjun Xiong,
Wei Xia
European Conference of Computer Vision (ECCV), 2022, Tel Aviv, Israel
[Paper]
[Poster]
[Video]
label neural architecture search, representation learning
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MalNet: A Large-Scale Cybersecurity Image Database of Malicious Software
Scott Freitas,
Rahul Duggal,
Polo Chau
International Conference on Information and Knowledge Management (CIKM), 2022, Atlanta, Georgia
[Paper]
[Demo]
[Code]
label malware-classification, class-imbalance
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Compatibility-aware Heterogeneous Visual Search
Rahul Duggal,
Hao Zhou,
Shuo Yang,
Yuanjun Xiong,
Wei Xia,
Zhuowen Tu,
Stefano Soatto
Computer Vision and Pattern Recognition (CVPR), 2021, Nashville, USA
[Paper]
[Supplementary]
[Poster]
[Video]
label neural architecture search, visual search, compatible representations
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HAR: Hardness Aware Reweighting for Imbalanced Datasets
Rahul Duggal,
Scott Freitas,
Sunny Dhamnani,
Polo Chau,
Jimeng Sun
IEEE Conference on Big Data (BigData), 2021, Orlando, USA
[Paper]
[Slides]
[Video]
label class-imbalance, early-exiting
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CUP: Cluster Pruning for Efficient Deep Neural Networks
Rahul Duggal,
Cao Xiao,
Richard Vuduc,
Polo Chau,
Jimeng Sun
IEEE Conference on Big Data (BigData), 2021, Orlando, USA
[Paper]
[Code]
[Slides]
[Video]
label filter-pruning, clustering, neural network compression
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NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks
Haekyu Park,
Nilaksh Das,
Rahul Duggal,
Austin P. Wright,
Omar Shaikh,
Fred Hohman,
Duen Horng Chau
IEEE Transactions on Visualization and Computer Graphics (TVCG), 2021
[Paper]
[Demo]
label concept-visualization, neural networks
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REST : Robust and Efficient Neural Networks for Sleep Staging in the Wild
Rahul Duggal*,
Scott Freitas*, Cao Xiao, Polo Chau, Jimeng Sun (* = equal contribution)
The Web Conference (WWW), 2020, Taipei (Oral)
[Paper]
[Arxiv]
[Code]
[Video]
label filter-pruning, noise-robustness, adversarial-training,
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GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images
Anubha Gupta,
Rahul Duggal,
Shiv Gehlot,
Ritu Gupta,
Anvit Mangal,
Lalit Kumar,
Nisarg Thakkar,
Devprakash Satpathy
Medical Image Analysis, 2020
[Paper]
[Code]
label stain-normalization, singular value decomposition, basis alignment
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PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma
Anubha Gupta,
Pramit Mallick,
Ojaswa Sharma,
Ritu Gupta,
Rahul Duggal
PLOS ONE, 2018
[Paper]
label stain-segmentation, level-sets
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SD-Layer: Stain Deconvolutional layer for CNNs in Medical Microscopic Imaging
Rahul Duggal,
Anubha Gupta,
Ritu Gupta,
Pramit Mallick
Medical Image Computing and Computer Assisted Intervention (MICCAI), 2017, Quebec City, Canada
[Paper]
[Code]
[Poster]
label tissue-classification, conv nets, optical-density colorspace
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P-TELU : Parametric Tan Hyperbolic Linear Unit Activation for Deep Neural Networks
Rahul Duggal,
Anubha Gupta
International Conference of Computer Vision Workshops (ICCVW), 2017, Venice, Italy
[Paper]
[Poster]
label activation functions, deep learning
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Overlapping Cell Nuclei Segmentation in Microscopic Images Using Deep Belief Networks
Rahul Duggal,
Anubha Gupta,
Ritu Gupta, Manya Wadhwa, Chirag Ahuja
Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), 2016, India
[Paper]
[Code]
[Poster]
label stain-segmentation, deep belief networks
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Pre-prints / Working Papers
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IMBNAS: Neural Architecture Search on Imabalanced Datasets
Rahul Duggal,
Shengyun (Anthony) Peng,
Hao Zhou,
Polo Chau
In Submission, 2022
[Arxiv]
labelneural architecture search, class-imbalance
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ConceptEvo: Interpreting Concept Evolution in Deep Learning Training
Haekyu Park,
Seongmin Lee,
Ben Hoover,
Austin Wright,
Omar Shaikh,
Rahul Duggal,
Nilaksh Das,
Judy Hoffman,
Polo Chau
In Submission, 2022
[Arxiv]
labelinterpretability
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