基本信息
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职业迁徙
个人简介
Areas of research expertise
Varuna works on the following fundamental areas of research in the field of Machine Learning. While his work is focused on the fundamental AI Developments, Varuna's team work mostly on Sports analytics and Driverless Vehicle Control applications to apply this research.
Multi-Agent Reinforcement Learning: Building intelligent agents using reinforcement learning in multi-agent settings. Specific interests are in scalable reinforcement learning and methods to in build human domain knowledge and experience in to the reinforcement learning framework. The applications include, team sports analytics, data driven ghosting, mobile edge intelligence, vehicular traffic control.
Deep generative modelling: Learning muitimodal representations using deep neural networks such as Variational Auto-encoders and Normalizing flows. Applications include: text to image synthesis, neuro-symbolic approaches for sports analysis, and scene recognition, Portfolio optimization, customer profiling in finance
Neuro-symbolic approaches for computer vision: using logical representations in conjunction with neural networks for explainable and transferable machine learning model development. Applications include: body pose estimation, human activity recognition, and scene recognition in driverless vehicle control
Varuna works on the following fundamental areas of research in the field of Machine Learning. While his work is focused on the fundamental AI Developments, Varuna's team work mostly on Sports analytics and Driverless Vehicle Control applications to apply this research.
Multi-Agent Reinforcement Learning: Building intelligent agents using reinforcement learning in multi-agent settings. Specific interests are in scalable reinforcement learning and methods to in build human domain knowledge and experience in to the reinforcement learning framework. The applications include, team sports analytics, data driven ghosting, mobile edge intelligence, vehicular traffic control.
Deep generative modelling: Learning muitimodal representations using deep neural networks such as Variational Auto-encoders and Normalizing flows. Applications include: text to image synthesis, neuro-symbolic approaches for sports analysis, and scene recognition, Portfolio optimization, customer profiling in finance
Neuro-symbolic approaches for computer vision: using logical representations in conjunction with neural networks for explainable and transferable machine learning model development. Applications include: body pose estimation, human activity recognition, and scene recognition in driverless vehicle control
研究兴趣
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KNOWLEDGE-BASED SYSTEMS (2024): 111123-111123
CoRR (2024): 2423-2425
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CoRR (2024)
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2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)pp.1-5, (2023)
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2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)pp.1-8, (2023)
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ISPR (2)pp.155-168, (2023)
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