Liquid neural networks are a class of AI algorithms that can learn to stay adaptable even after training. Liquid neural networks are inspired by how brain cells communicate with each other. They are robust to perturbations, they do not need to be large to generate interesting behavior, and show promise in learning necessary skills from data to perform well beyond their training data. Liquid neural networks have the potential to alleviate many sociotechnical challenges of large-scale machine learning systems, such as interpretability, accountability, fairness, and carbon footprint. Ramin Hasani is a Principal AI and Machine Learning Scientist at the Vanguard Group and a Research Affiliate at CSAIL MIT. Ramin’s research focuses on robust deep learning and decision-making in complex dynamical systems. Previously he was a Postdoctoral Associate at CSAIL MIT, leading research on modeling intelligence and sequential decision-making, with Prof. Daniela Rus. He received his Ph.D. degree with distinction in Computer Science at Vienna University of Technology (TU Wien), Austria (May 2020). His Ph.D. dissertation and continued research on Liquid Neural Networks got recognized internationally with numerous nominations and awards such as TÜV Austria Dissertation Award nomination in 2020, and HPC Innovation Excellence Award in 2022. He has also been a frequent TEDx Speaker.
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