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Ramin Hasani is a machine learning scientist at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and a Principal AI/ML Scientist at the Vanguard Group. He is renowned for his pioneering work on Liquid Neural Networks (LNNs), a novel class of time-continuous neural networks inspired by the nervous system of small organisms like C. elegans. His research interests span robust and efficient machine learning, causal discovery, robotics, and control systems, with a focus on developing AI systems that can understand and interact with the physical world reliably and adaptively. He received his Ph.D. with highest honors (Sub Auspiciis Praesidentis) in Computer Science from TU Wien, Vienna, Austria.
Ramin Hasani's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Developed and introduced Liquid Neural Networks, a type of time-continuous neural network that offers enhanced robustness, interpretability, and computational efficiency for processing sequential data, particularly in dynamic and uncertain environments.
Awarded his Ph.D. in Computer Science from TU Wien, Vienna, Austria, under the highest possible distinction in Austria, recognizing exceptional academic achievements throughout his studies.
Conducts cutting-edge research at one of the world's leading artificial intelligence laboratories, focusing on trustworthy AI, causal deep learning, and neural system identification and control.
Applies advanced AI and machine learning techniques to complex financial problems, leading research and development in these areas for a major global investment management company.
Recognized in the Forbes 30 Under 30 list for Europe in the Science and Healthcare category in 2021 for his impactful contributions to artificial intelligence and neural networks.
Technische Universität Wien - Year 2016
Politecnico di Milano - Year 2012
Ferdowsi University of Mashhad - Year 2007
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Liquid AI is an innovative artificial intelligence company, spun out of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), that is pioneering the development of a new class of AI models known as liquid neural networks (LNNs). These models are designed to be more efficient, robust, and adaptable than traditional deep learning systems, allowing them to learn continuously from new data and operate effectively with fewer computational resources. Liquid AI aims to apply this technology to solve complex problems in areas such as robotics, autonomous vehicles, climate modeling, and critical decision-making processes, pushing the boundaries of AI capabilities.
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