Select a calendar:
Filter November Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for November 30, 2017
-
Can Effective Communication Transform Your Career?
Thu, Nov 30, 2017 @ 12:00 AM - 01:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Technical communication for engineers remains a valuable commodity that will differentiate you from the pack. In today's information-saturated age, how can you rise above the noise and become a trusted and valuable player? Transforming your professional potential starts with getting clear on communicating your message. Join this lunch time discussion to learn how to take the first step in outpacing the ranks to ignite your trajectory.
Irwin Umali graduated from USC with a BS in Industrial & Systems Engineering in 2007 and the following year with a MS in Industrial & Systems Engineering. Irwin uses his engineering background to help healthcare organizations and professionals improve their strategic performance.
To access this VIRTUAL workshop, go to https://bluejeans.com/289881167 and log in with your netID and password.Location: Online
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
-
CS Colloquium: Andrew Miller (Harvard) - Advances in Monte Carlo Variational Inference
Thu, Nov 30, 2017 @ 02:00 PM - 03:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Andrew Miller, Harvard
Talk Title: Advances in Monte Carlo Variational Inference
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
Probabilistic modeling is a natural framework for reasoning about noisy data. Well-constructed probabilistic models that combine prior knowledge with data can uncover latent structure, make predictions, and support scientific discovery. However, specifying a model and actually applying a model to data are two distinct challenges. In this talk, I will illustrate and address these challenges by presenting new models and inference methods. Monte Carlo variational inference (MCVI) is an optimization-based class of approximate inference algorithms applicable to a wide range of probabilistic models. I will present work that improves MCVI by increasing the expressiveness of approximations and the robustness of optimization. I will also present new probabilistic models developed for a variety of applied problems.
Biography: Andy Miller is a PhD candidate in computer science at Harvard University, studying statistical machine learning. He develops probabilistic models and inference methods for complex, high-dimensional data in applications ranging from astronomy to health care to sports analytics. He is currently in the final year of his program, advised by Ryan Adams (Princeton and Google Brain), Finale Doshi-Velez (Harvard), and Luke Bornn (Simon Fraser).
Host: Fei Sha
Location: Seeley G. Mudd Building (SGM) - 123
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
Memristive Accelerators for Data Intensive Computing: From Machine Learning to High- Performance Linear Algebra
Thu, Nov 30, 2017 @ 02:00 PM - 03:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Engin Ipek, University of Rochester
Talk Title: Memristive Accelerators for Data Intensive Computing: From Machine Learning to High- Performance Linear Algebra
Abstract: DRAM is facing severe scalability challenges due to precise charge placement and sensing hurdles in deep-submicron geometries. Resistive memories, such as phase-change memory (PCM), resistive RAM (RRAM), and spin-torque transfer magnetoresistive RAM (STT-MRAM), hold the potential to scale well beyond DRAM and are promising DRAM replacements. Although the near term application of these technologies will likely be in main memory and storage, their electrical properties also make it possible to design qualitatively new methods of accelerating important classes of workloads.
In this talk, I will examine high-performance memristive compute engines that combine two powerful capabilities: in-situ data processing and analog computing. Implementations of these engines using PCM, RRAM, and STT-MRAM will be introduced, and their application to machine learning, combinatorial optimization, and scientific computing workloads will be presented. The talk will conclude with a discussion of the novel error correction techniques that are necessary to make the reliability and precision of memristive accelerators competitive with digital systems.
Biography: Engin Ipek is an Associate Professor of Electrical & Computer Engineering and of Computer Science at the University of Rochester. His research interests are in energy-efficient architectures, high-performance memory systems, and the application of emerging technologies to computer systems. Prof. Ipek received his BS (2003), MS (2007), and Ph.D. (2008) degrees from Cornell University, all in Electrical and Computer Engineering. Prior to joining the University of Rochester, he was a researcher in the computer architecture group at Microsoft Research (2007-2009). His work has been recognized by the 2014 IEEE Computer Society TCCA Young Computer Architect Award, an HPCA 2016 distinguished paper award, three IEEE Micro Top Picks awards, an ASPLOS 2010 best paper award, an NSF CAREER award, and an invited Communications of the ACM research highlights article.
Host: Xuehai Qian, x04459, xuehai.qian@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
-
Epstein Institute Seminar, ISE 651
Thu, Nov 30, 2017 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Allen Yi, Professor, The Ohio State University
Talk Title: Thermoforming of Precision Optics
Host: Prof. Yong Chen
More Information: November 30, 2017.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
-
CS Colloquium: Zi Wang (MIT) - Bayesian Optimization and How to Scale it Up
Thu, Nov 30, 2017 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Zi Wang, MIT
Talk Title: Bayesian Optimization and How to Scale it Up
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
In recent years, Bayesian optimization (BO) has become a popular and effective approach to optimize an expensive black-box function with assumptions usually expressed by a Gaussian process prior. Successful applications include tuning hyper-parameters for neural networks, optimizing control parameters for robots, and designing biological experiments. Despite these successes, BO has been limited to small-scale and low-dimensional problems due to computational challenges with Gaussian processes and statistical challenges in high-dimensional settings. In this talk, I will present our recent work on scaling up BO from several aspects. First, I will introduce Max-value Entropy Search, a new BO strategy that improves sample-efficiency and obtains the first regret bound for a variant of the entropy search methods. Building on the new acquisition function, we extend our approach to high dimensions by learning the additive structures of the kernel. And finally, we propose a scalable high-dimensional BO approach that gives previously impossible results of scaling up BO to tens of thousands of observations within minutes of computation. We also show some interesting new findings on how evolutionary algorithms and BO are related, and establish novel connections among several well-known BO methods including entropy search, GP-UCB, and probability of improvement.
Biography: Zi Wang is a Ph.D. student at the MIT Computer Science and Artificial Intelligence Laboratory, advised by Stefanie Jegelka, Leslie Kaelbling and Tomás Lozano-Pérez. She received her S.M. in Electrical Engineering and Computer Science from MIT in Feb. 2016, and B.Eng. in Computer Science and Technology from Tsinghua University in Jul. 2014. Her research interests lie broadly in machine learning and artificial intelligence, currently with applications to robotics problems.
Host: Fei Sha
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
