Presentation: Deep Learning for Science

Track: AI Meets the Physical World

Location: Cyril Magnin II

Duration: 4:20pm - 5:10pm

Day of week: Tuesday

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Abstract

How is deep learning affecting the world of science? In this talk, Prabhat (Data and Analytics team lead at NERSC, Berkeley Lab) discusses machine learning's impact on climatology, astronomy, cosmology, neuroscience, genomics, and high-energy physics. The talk will conclude with a list of open challenges in applied Deep Learning, and the future of AI in powering scientific discoveries.

Speaker: Prabhat

Data and Analytics Group Lead @NERSC

Prabhat leads the Data and Analytics Services team at NERSC. His current research interests applied statistics, machine learning, and high performance computing. He has worked on topics in scientific data management, parallel I/O, scientific visualization, computer graphics and computer vision in the past. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley.

Find Prabhat at

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.