Sandeep Routray

Sandeep Routray

Machine Learning Engineer

Samsung Research

Biography

I am currently a member of the SmartThings Team at Samsung Research , Seoul, where my work involves: 1) developing a deep learning-based indoor positioning system and 2) vectorized floorplan reconstruction from partial/noisy LiDAR maps. I am also collaborating with Prof. Sanja Fidler at Vector Institute to improve object-centric representations by using image context in a self-supervised framework. I have also worked on ways to incorporate geometrical cues to learn dense representations for scene images.

I work at the intersection of scene understanding, self-supervised learning and robotics. Some of the problems that interest me are - learning actionable representations from scene images, understanding scenes at different levels of abstraction (from pixel level up to the image level) and the hierarchical relationships among them, and the challenges in working with 3D scenes. I am also interested in self-supervised approaches that jointly consider synthesis and discriminative tasks.

I graduated from Indian Institute of Technology Kanpur in May 2021, majoring in Electrical Engineering with minors in Machine Learning. I worked on natural language processing, optimization algorithms, stochastic modelling, and wireless networks during my undergraduate days.

For details, check my CV .

Interests
  • Computer Vision
  • Self/Weakly-Supervised Learning
  • Scene Understanding
  • Reinforcement Learning
Education
  • Btech in Electrical Engineering with Minors in Machine Learning, 2021

    Indian Institute of Technology Kanpur

Recent Publications

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(2020). CS-NET at SemEval-2020 Task 4: Siamese BERT for ComVE. In SemEval 2020.

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(2020). Spectral Efficiency in Poisson Cluster Based HetNets with Users-Basestations Correlation. In ANTS 2020.

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Projects

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Smooth Minimax Optimisation in Non-Euclidean Space Using Bregman Divergence Framework
Minimax type of problems arise in several domains such as machine learning, optimization, statistics, communication, and game theory. However, a majority of results are established for the Euclidean norm due to its special self-dual nature.
Smooth Minimax Optimisation in Non-Euclidean Space Using Bregman Divergence Framework
Resource Allocation in OFDMA Systems Using Reinforcement Learning
Long Term Evolution (LTE) makes use of Orthogonal Frequency Division Multiplexing (OFDM) to achieve high downlink data rates. Based on channel conditions of users, a resource scheduler can use dynamic resource allocation to provide best resource to users and optimize system performance.
Resource Allocation in OFDMA Systems Using Reinforcement Learning