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Computer Science

D-Index
41
Citations
10024
World Ranking
8665
National Ranking
111

Overview

Vijay Chandrasekhar is affiliated with the Agency for Science, Technology and Research in Singapore and has contributed extensively to research in computer science, particularly within the fields of artificial intelligence, computer vision and pattern recognition, electrical and electronic engineering, hardware and architecture, as well as computer networks and communications.

Their recent scholarly work includes publications on topics such as homomorphic encryption, deep convolutional neural networks, Bayesian deep active learning, neural network pruning, and image generation using classification latent space representations. Notable papers include:

  • Towards the AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data With GPUs (2020, IEEE Transactions on Emerging Topics in Computing)
  • DOReN: Toward Efficient Deep Convolutional Neural Networks with Fully Homomorphic Encryption (2021, IEEE Transactions on Information Forensics and Security)
  • Bayesian Deep Active Learning for Analog Circuit Performance Classification (2022, 2022 IEEE International Symposium on Circuits and Systems (ISCAS))
  • Learning to Prune Deep Neural Networks via Reinforcement Learning (2020, arXiv (Cornell University))
  • Classify and generate: Using classification latent space representations for image generations (2021, Neurocomputing)

Frequent co-authors collaborating with Vijay Chandrasekhar include Chuan-Sheng Foo, Jie Lin, Chan Fook Mun, Benjamin Hong Meng Tan, and Khin Mi Mi Aung.

Publications have appeared in venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Emerging Topics in Computing
  • IEEE Transactions on Information Forensics and Security
  • 2022 IEEE International Symposium on Circuits and Systems (ISCAS)
  • ITM Web of Conferences

Their research spans several subfields with the following focal points:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Hardware and Architecture
  • Computer Networks and Communications

Key topics of investigation include:

  • Cryptography and Data Security
  • Privacy-Preserving Technologies in Data
  • Cryptographic Implementations and Security
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Generative Adversarial Networks and Image Synthesis

Best Publications

  • ICDAR 2015 competition on Robust Reading

    Dimosthenis Karatzas;Lluis Gomez-Bigorda;Anguelos Nicolaou;Suman Ghosh

  • Efficient GAN-Based Anomaly Detection

    Houssam Zenati;Chuan Sheng Foo;Bruno Lecouat;Gaurav Manek

  • Compressed Histogram of Gradients: A Low-Bitrate Descriptor

    Vijay Chandrasekhar;Gabriel Takacs;David M. Chen;Sam S. Tsai

  • Localization in underwater sensor networks: survey and challenges

    Vijay Chandrasekhar;Winston Kg Seah;Yoo Sang Choo;How Voon Ee

  • Outdoors augmented reality on mobile phone using loxel-based visual feature organization

    Gabriel Takacs;Vijay Chandrasekhar;Natasha Gelfand;Yingen Xiong

  • Mobile Visual Search

    B Girod;V Chandrasekhar;D M Chen;Ngai-Man Cheung

  • Adversarially Learned Anomaly Detection

    Houssam Zenati;Manon Romain;Chuan-Sheng Foo;Bruno Lecouat

  • Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing

    Juan Pablo Correa-Baena;Kedar Hippalgaonkar;Jeroen van Duren;Shaffiq Jaffer

  • CHoG: Compressed histogram of gradients A low bit-rate feature descriptor

    Vijay Chandrasekhar;Gabriel Takacs;David Chen;Sam Tsai

  • An Area Localization Scheme for Underwater Sensor Networks

    V. Chandrasekhar;W. Seah

  • Unified Real-Time Tracking and Recognition with Rotation-Invariant Fast Features

    Gabriel Takacs;Vijay Chandrasekhar;Sam Tsai;David Chen

  • The stanford mobile visual search data set

    Vijay R. Chandrasekhar;David M. Chen;Sam S. Tsai;Ngai-Man Cheung

  • A*3D Dataset: Towards Autonomous Driving in Challenging Environments

    Quang-Hieu Pham;Pierre Sevestre;Ramanpreet Singh Pahwa;Huijing Zhan

  • Overview of the MPEG-CDVS Standard

    Ling-Yu Duan;Vijay Chandrasekhar;Jie Chen;Jie Lin

  • Towards the AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data With GPUs

    Ahmad Al Badawi;Chao Jin;Jie Lin;Chan Fook Mun

  • Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile

    Panayotis Mertikopoulos;Bruno Lecouat;Bruno Lecouat;Houssam Zenati;Houssam Zenati;Chuan-Sheng Foo;Chuan-Sheng Foo

  • Transform coding of image feature descriptors

    Vijay Chandrasekhar;Gabriel Takacs;David Chen;Sam S. Tsai

  • Tree Histogram Coding for Mobile Image Matching

    David M. Chen;Sam S. Tsai;Vijay Chandrasekhar;Gabriel Takacs

  • DehazeGAN: When Image Dehazing Meets Differential Programming

    Hongyuan Zhu;Xi Peng;Vijay Chandrasekhar;Liyuan Li

  • Object detection meets knowledge graphs

    Yuan Fang;Kingsley Kuan;Jie Lin;Cheston Tan

Frequent Co-Authors

Bernd Girod
Bernd Girod Stanford University
Radek Grzeszczuk
Radek Grzeszczuk Microsoft (United States)
Ngai-Man Cheung
Ngai-Man Cheung Singapore University of Technology and Design
Jatinder Pal Singh
Jatinder Pal Singh Facebook (United States)
Winston K. G. Seah
Winston K. G. Seah Victoria University of Wellington
Stefan Winkler
Stefan Winkler National University of Singapore
Supratik Guha
Supratik Guha Argonne National Laboratory
Ling-Yu Duan
Ling-Yu Duan Peking University
Vladan Stevanović
Vladan Stevanović Colorado School of Mines

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