Yaniv Gurwicz Email & Phone Number | Research scientist in machine learning, deep learning and pattern recognition with extensive experience of 15 years in research, development and prototyping for computer vision, time-series data and various real-world domains. Publishing: • papers in peer-reviewed scientific journals, and at top-tier machine learning conferences. • US patents. Currently conducting research on: • continual learning and catastrophic forgetting of deep networks based on concepts from neuroscience. • causal inference (e.g., cause and effect, intervention, counterfactuals and confounders) through casual graphs with a direct translation to deep neural networks. Expert in: machine learning deep learning pattern recognition reinforcement learning computer vision probabilistic graphical models anomaly detection self-supervised learning Bayesian networks and casual inference semi-supervised learning using spectral graph theory continual learning.

Yaniv Gurwicz

Research scientist in machine learning, deep learning and pattern recognition with extensive experi...
Israel

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Yaniv Gurwicz’s Email yaniv.gurwicz@nice.com

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Yaniv Gurwicz’s Location Israel

Yaniv Gurwicz’s Current Industry Intel Labs

Yaniv Gurwicz’s Prior Industry Nice | Elbit Systems | Iai Israel Aerospace Industries

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Research Scientist

Present

Research Scientist

2017-08-01 00:00:00 - Present

Senior Algorithm Researcher

2010-01-01 00:00:00 - 2017-01-01 00:00:00

Algorithm Researcher And Developer

2001-01-01 00:00:00 - 2004-01-01 00:00:00

Algorithm Engineer

1998-01-01 00:00:00 - 2001-01-01 00:00:00

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Yaniv Gurwicz is a Research scientist in machine learning, deep learning and pattern recognition with extensive experience of 15 years in research, development and prototyping for computer vision, time-series data and various real-world domains. Publishing: • papers in peer-reviewed scientific journals, and at top-tier machine learning conferences. • US patents. Currently conducting research on: • continual learning and catastrophic forgetting of deep networks based on concepts from neuroscience. • causal inference (e.g., cause and effect, intervention, counterfactuals and confounders) through casual graphs with a direct translation to deep neural networks. Expert in: machine learning deep learning pattern recognition reinforcement learning computer vision probabilistic graphical models anomaly detection self-supervised learning Bayesian networks and casual inference semi-supervised learning using spectral graph theory continual learning.