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Events for March 26, 2020

  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Aditya Grover (Stanford University) - Machine Learning for Accelerating Scientific Discovery

    Thu, Mar 26, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Aditya Grover, Stanford University

    Talk Title: Machine Learning for Accelerating Scientific Discovery

    Series: CS Colloquium

    Abstract: The dramatic increase in both sensor capabilities and computational power over the last few decades has created enormous opportunities for using machine learning (ML) to enhance scientific discovery. To realize this potential, ML systems must seamlessly integrate with the key tools for scientific discovery. For instance, how can we incorporate scientific domain knowledge within ML algorithms? How can we use ML to quantify uncertainty in simulations? How can we use ML to plan experiments under real-world budget constraints? For these questions, I'll first present the key computational and statistical challenges through the lens of probabilistic modeling. Next, I'll highlight limitations of existing approaches for scaling to high-dimensional data and present algorithms from my research that can effectively overcome these challenges. These algorithms are theoretically principled, domain-agnostic, and exhibit strong empirical performance. Notably, I'll describe a collaboration with chemists and material scientists where we used probabilistic models to efficiently optimize an experimental pipeline for electric batteries. Finally, I'll conclude with an overview of future opportunities for using ML to accelerate scientific discovery.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Aditya Grover is a fifth-year Ph.D. candidate in Computer Science at Stanford University advised by Stefano Ermon. His research focuses on probabilistic modeling and reasoning and is grounded in real-world scientific applications. Aditya's research has been published in top scientific and ML/AI venues (e.g., Nature, NeurIPS, ICML, ICLR, AAAI, AISTATS), included in widely-used open source ML software, and deployed into production at major technology companies. His work has been recognized with a best paper award (StarAI), a Lieberman Fellowship, a Data Science Institute Scholarship, and a Microsoft Research Ph.D. Fellowship. He is also a Teaching Fellow at Stanford since 2018, where he co-created and teaches a new class on Deep Generative Models. Previously, Aditya obtained his bachelors in Computer Science and Engineering from IIT Delhi in 2015, where he received a best undergraduate thesis award.

    Host: Bistra Dilkina

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Undergraduate Admission Virtual Information Session

    Thu, Mar 26, 2020 @ 02:00 PM - 03:00 PM

    Viterbi School of Engineering Undergraduate Admission

    Workshops & Infosessions


    Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for prospective first-year students and their family members to learn more about the USC Viterbi undergraduate experience.Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life.Guests will be able to ask questions and engage in further discussion toward the end of the session.

    Please register here!

    Audiences: Everyone Is Invited

    Posted By: Viterbi Admission

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  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Zhihao Jia (Stanford University) - Automated Discovery of Machine Learning Optimizations

    Thu, Mar 26, 2020 @ 04:00 PM - 05:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Zhihao Jia, Stanford University

    Talk Title: Automated Discovery of Machine Learning Optimizations

    Series: CS Colloquium

    Abstract: As an increasingly important workload, machine learning (ML) applications require different performance optimization techniques from traditional runtimes and compilers. In particular, to accelerate ML applications, it is generally necessary to perform ML computations on heterogeneous hardware and parallelize computations using multiple data dimensions, neither of which is even expressible in traditional compilers and runtimes. In this talk, I will describe my work on automated discovery of performance optimizations to accelerate ML computations.

    TASO, the Tensor Algebra SuperOptimizer, optimizes the computation graphs of deep neural networks (DNNs) by automatically generating potential graph optimizations and formally verifying their correctness. TASO outperforms rule-based graph optimizers in existing ML systems (e.g., TensorFlow, TensorRT, and TVM) by up to 3x by automatically discovering novel graph optimizations, while also requiring significantly less human effort.

    FlexFlow is a system for accelerating distributed DNN training. FlexFlow identifies parallelization dimensions not considered in existing ML systems (e.g., TensorFlow and PyTorch) and automatically discovers fast parallelization strategies for a specific parallel machine. Companies and national labs are using FlexFlow to train production ML models that do not scale well in current ML systems, achieving over 10x performance improvement.

    I will also outline future research directions for further automating ML systems, such as codesigning ML models, software systems, and hardware backends for end-to-end ML deployment.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Zhihao Jia is a Ph.D. candidate in the Computer Science department at Stanford University working with Alex Aiken and Matei Zaharia. His research interests lie in the intersection of computer systems and machine learning, with a focus on building efficient, scalable, and high-performance systems for ML computations.

    Host: Leana Golubchik

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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