Events for the 2nd week of February
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Geostationary Littoral Imaging and Monitoring Radiometer
Tue, Feb 04, 2020 @ 11:00 AM - 12:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Jeff Puschell, Principal Engineering Fellow and Chief Scientist, Space Systems at Raytheon Space and Airborne Systems in El Segundo, California
Talk Title: Geostationary Littoral Imaging and Monitoring Radiometer
Host: Mahta Moghadda, PhD
More Information: GLIMR SEMINAR 2-4-20 DR. JEFF PUSCHELL Final 1-24-20.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Luz Antunez-Castillo, MBA, EdD
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Wed, Feb 05, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Christopher Ré, Department of Computer Science at Stanford University
Talk Title: If You Want to be Rich, Get a lot of Money: Theory and Systems for Weak Supervision
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: If you want to build a high-quality machine learning product, build a large, high-quality training set. At first glance, this seems as useful as the statement "if you want to be rich, get a lot of money." However, a key idea driving our work is that new theoretical and systems concepts including weak supervision, automatic data augmentation policies, and more, can enable engineers to build training sets more quickly and cost effectively. Along with state-of-the-art results on benchmarks, these concepts have allowed our group and collaborators to build a range of state-of-the-art applications including patient-care monitoring on electronic health records, automatic triage systems for radiologists, and enabling cardiologists to spot rare abnormalities in video MRI-along with widely used products from Apple and Google. This talk describes the theoretical and systems challenges that such applications create.
On the machine-learning theory side, a key problem is estimating the quality and correlation of various sources of training data-”but without ground truth labels. This problem connects to classical questions about estimating the covariance of latent variable models. We describe our new techniques that solve this case and can even improve fully supervised methods for estimating the structure of graphical models.
On the machine-learning systems side, this theory opens up new ways to build machine-learning systems. Here, we describe our recent work on systems that help engineers build and maintain machine learning products-without writing low-level code in frameworks like TensorFlow. These systems draw on recent ideas in machine learning, e.g., zero-code deep learning systems, and twists on classical data management ideas, e.g., schemas to separate the model, the supervision, and down-stream serving code.
Much of this work is open source and available at http://snorkel.org or my website.
Biography: Christopher (Chris) Ré is an associate professor in the Department of Computer Science at Stanford University who is affiliated with the Statistical Machine Learning Group and Stanford AI Lab. His recent work is to understand how software and hardware systems will change as a result of machine learning along with a continuing, petulant drive to work on math problems. Research from his group has been incorporated into scientific and humanitarian efforts, such as the fight against human trafficking, along with products from technology and enterprise companies. He cofounded a company, based on his research into machine learning systems, that was acquired by Apple in 2017. More recently, he cofounded SambaNova systems based, in part, on his work on accelerating machine learning. He received a SIGMOD Dissertation Award in 2010, an NSF CAREER Award in 2011, an Alfred P. Sloan Fellowship in 2013, a Moore Data Driven Investigator Award in 2014, the VLDB early Career Award in 2015, the MacArthur Foundation Fellowship in 2015, and an Okawa Research Grant in 2016. His research contributions have spanned database theory, database systems, and machine learning, and his work has won best paper at a premier venue in each area, respectively, at PODS 2012, SIGMOD 2014, and ICML 2016.
Host: Paul Bogdan, pbogdan@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Fri, Feb 07, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Nikunj Mehta, Falkonry
Talk Title: Discovering and Explaining Patterns in Industrial Multivariate Time Series Data
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Complex assets and process units exhibit many different behaviors during the course of industrial operations. Identifying and removing sources of inefficiency in these operations is essential for advancing manufacturing and process operations. In this talk, we explain how classification as opposed to anomaly detection and forecasting is the essential machine learning problem for Industry 4.0. We explain the main challenges for these machine learning problems to motivate research directions. We then describe a signal processing pipeline and user interface for democratizing such machine learning and real-time processing.
Biography: Dr. Nikunj founded Falkonry after realizing that very valuable operational data produced in industrial infrastructure goes mostly unutilized in the energy, manufacturing and transportation sectors. Falkonry has enabled companies to scale predictive operations. Falkonry has significantly improved their uptime, yield and quality. Prior to Falkonry, Dr. Mehta led software architecture and customer success for C3 IoT. Earlier, he led innovation teams at Oracle focused on database technology and led the creation of the IndexedDB standard for databases embedded inside all modern browsers. He has contributed to standards at both W3C and IETF, and is a member of the ACM.
Host: Paul Bogdan, pbogdan@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White