University of California, Riverside

Department of Electrical and Computer Engineering

Dr. Azalia Mirhoseini - A Talk on “Bringing the Machine into the Loop of Machine Learning”

Dr. Azalia Mirhoseini - A Talk on “Bringing the Machine into the Loop of Machine....

Dr. Azalia Mirhoseini - A Talk on “Bringing the Machine into the Loop of Machine Learning”

February 23, 2016 - 2:00 pm
Winston Chung Hall, 205/206


Contemporary analytical algorithms are often focused on functionality and accuracy with system performance as an afterthought. As their use/scale grows and the computing platforms become diverse, spanning from servers and desktops to smartphones and Internet of Things (IoT) devices, functionality is not just about algorithmic efficiency and accuracy, but also practicality on real-world computing machines. One-size fits all solutions will not meet the physical needs of emerging analytical application scenarios.


In this talk, I will present my research on novel computing frameworks that bring hardware into the loop of designing scalable inference algorithms and learning systems. I will describe how a multi-faceted design that holistically considers the computing domain parameters, namely data, algorithm, and machine, introduces game changing performance gains across the board, including runtime, energy, memory, and network bandwidth. I will then describe my tools which enable automatic end-to-end adoption of the proposed frameworks in a wide range of data inference application scenarios. On the theoretical side, I show how my new solutions reach the target machine's computation/communication bounds. On the practical side, I present customized approaches for a range of algorithms and applications (e.g., penalized regression, classification, and deep neural networks), datasets (e.g., visual and sensing), and machines (e.g., GPU, FPGA, CPU clusters, and heterogeneous architectures). I also demonstrate my approach towards enabling single-pass streaming learning problems. Finally, I discuss how lessons learned in the context of my holistic frameworks can bring new directions in the design of broader analytical scenarios such as privacy preserving and just-in-time computing. 


Azalia Mirhoseini is a postdoctoral researcher in the department of Electrical and Computer Engineering (ECE) at the University of California, San Diego and Rice University. She received her Ph.D. from Rice University where she worked on algorithms and architectures for performance efficient data analytics. Azalia's work has received a number of awards, including the best 2015 Ph.D. thesis award at Rice ECE department and fellowships from IBM, Schlumberger, and Microsoft Research.



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University of California, Riverside
900 University Ave.
Riverside, CA 92521
Tel: (951) 827-1012

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Electrical and Computer Engineering
Suite 343 Winston Chung Hall
University of California, Riverside
Riverside, CA 92521-0429

Tel: (951) 827-2484
Fax: (951) 827-2425
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