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Events for September 28, 2017
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USC LACI’s Life of a Consultant
Thu, Sep 28, 2017 @ 11:00 AM - 12:00 PM
Viterbi School of Engineering Student Organizations
University Calendar
Interested in exploring a career in consulting? Come to Los Angeles Community Impact's Life of a Consultant event on Thursday, September 28th from 7:30-9:30pm! Meet consultants from several industry-leading firms in a small group setting as they answer your questions about a consultant's typical work day, the recruiting process, the unique culture at each firm, and more. Light refreshments will be served.
You can RSVP for the event here: http://bit.ly/2xLWcrg. The link will go live on Wednesday, September 13 at 9:00AM, and you have to submit a $10 refundable deposit in order to confirm your place at the event. Contact LACI External Relations at laci.ambassador@gmail.com for any questions.Location: USC University Club, 705 W 34th St, Los Angeles, CA 90089
Audiences: Undergrad
Contact: usclatch
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The biomedical literature captures the most current biomedical knowledge and is a tremendously rich resource for research with over 26 million publications currently indexed in the US National Library of Medicine’s PubMed repository. Large-scale processin
Thu, Sep 28, 2017 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Karin Verspoor, University of Melbourne
Talk Title: The scientific literature as a resource for biological prediction and data validation
Abstract: The biomedical literature captures the most current biomedical knowledge and is a tremendously rich resource for research with over 26 million publications currently indexed in the US National Library of Medicines PubMed repository. Large scale processing of the literature enables direct biomedical knowledge discovery. In this presentation, I will introduce the use of text mining techniques for applications in protein function and phenotype prediction. I will also explore a novel alternative use of the literature to support curation of biological database records by cross checking their content with associated literature this work further broadens the value of the literature in bioinformatics applications.
Biography: Karin is a Professor in the School of Computing and Information Systems and Deputy Director of the Health and Biomedical Informatics Centre at the University of Melbourne. Her research focuses on text analytics and machine learning for biomedical applications, to enable knowledge extraction from unstructured data as well as to provide clinical decision support. A current active project is related to enabling precision medicine with machine learning.
Karin was previously the Scientific Director for Health and Life Sciences at NICTA. Prior to arriving in Australia from the United States she held research roles at the University of Colorado School of Medicine and Los Alamos National Laboratory, and spent 5 years developing language technology software in two start up companies.
Host: Gully Burns
Location: Information Science Institute (ISI) - 11th floor large conference room
Audiences: Everyone Is Invited
Contact: Kary LAU
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Scaling Machine Learning Performance with Moore's Law
Thu, Sep 28, 2017 @ 02:00 PM - 03:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Kunle Olukotun, Stanford University
Talk Title: Scaling Machine Learning Performance with Moore's Law
Abstract: The computational demands of machine learning (ML) requires energy efficient machine learning specific accelerators. This naturally results in heterogeneous computing platforms composed of CPUs and ML Accelerators. However, the staggering cost (the majority of the cost is for software development) of designing custom integrated circuits for many application domains makes it cost-prohibitive to design these accelerators. This situation calls for a new paradigm for designing accelerators that can provide energy-efficient ML-specific performance and easier software development. The key to this new paradigm is to enable application developers to optimize the underlying hardware to make it specific to their ML application needs. The new design paradigm consists of new application ML-specific programing languages, new machine learning algorithms, new compilation technology to target both existing (FPGAs) and new (Software Defined Hardware) reconfigurable architectures.
Biography: Kunle Olukotun is the Cadence Design Systems Professor of Electrical Engineering and Computer Science at Stanford University. Olukotun is well known as a pioneer in multicore processor design and the leader of the Stanford Hydra chip multipocessor (CMP) research project. Olukotun founded Afara Websystems to develop high-throughput, low-power multicore processors for server systems. The Afara multicore processor, called Niagara, was acquired by Sun Microsystems. Niagara derived processors now power all Oracle SPARC-based servers. Olukotun currently directs the Stanford Pervasive Parallelism Lab (PPL), which seeks to proliferate the use of heterogeneous parallelism in all application areas using Domain Specific Languages (DSLs). Olukotun is a member of the Data Analytics for What's Next (DAWN) Lab which is developing infrastructure for usable machine learning. Olukotun is an ACM Fellow and IEEE Fellow for contributions to multiprocessors on a chip and multi-threaded processor design. Olukotun received his Ph.D. in Computer Engineering from The University of Michigan.
Host: Xuehai Qian, x04459, xuehai.qian@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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CS Colloquium Event: Facebook Tech Talk
Thu, Sep 28, 2017 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Alex Helm, Catrina Manahan, Charles Kuykendoll, Yuandong Tian, Min Li, Qiachao Que, See Biography
Talk Title: AI in Games: Achievements and Challenges
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
Recently, substantial progress of AI has been made in applications that require advanced pattern reading, including computer vision, speech recognition and natural language processing. However, it remains an open problem whether AI will make the same level of progress in tasks that require sophisticated reasoning, planning and decision making in complicated game environments similar to the real-world. In this talk, I present the state-of-the-art approaches to build such an AI, our recent contributions in terms of designing more effective algorithms and building extensive and fast general environments, as well as issues and challenges.
Biography: Yuandong Tian is a Research Scientist in Facebook AI Research, working on reasoning with deep learning in games and theoretical analysis of deep non-convex models. He is the leader researcher and engineer for DarkForest (Facebook Computer Go project). Prior to that, he was a Software Engineer/Researcher in Google Self-Driving Car team during 2013-2014. He received Ph.D. in Robotics Institute, Carnegie Mellon University on 2013, Bachelor and Master degree of Computer Science in Shanghai Jiao Tong University. He is the recipient of 2013 ICCV Marr Prize Honorable Mentions for his work on global optimal solution to non-convex optimization in image alignment.
Host: CS Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Ryan Rozan
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CAIS Seminar: Dr. Peng Shi (University of Southern California) - Prediction and Optimization in School Choice
Thu, Sep 28, 2017 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Peng Shi, University of Southern California
Talk Title: Prediction and Optimization in School Choice
Abstract: In public school choice, students submit preference rankings for a given set of schools to the school board, which takes into account everyone's choices to compute the assignment. An important policy lever is what choice options to offer to each neighborhood, and how to prioritize between students. A key trade-off is between giving students equitable chances to go to the schools they want and controlling the city's school busing costs.
We study the optimization problem of choosing the choice menus and priorities for each neighborhood in order to maximize the sum of utilitarian and max-min welfare, subject to capacity and transportation constraints. The optimization is built on top of a predictive model of how students will choose given new choice menus, which we validate using both out-of-sample testing and a field experiment. Under a large market approximation, the optimization reduces to an assortment planning problem in which the objective is social-welfare rather than revenue. We show how to efficiently solve this sub-problem under various discrete choice models, and use this to produce better menus and priorities for Boston, which we evaluate by discrete simulations.
Biography: Dr. Peng Shi is an Assistant Professor of Data Science and Operations at the USC Marshall School of Business. He is interested in developing quantitative methodologies for the betterment of society. His current research focuses on optimization in matching markets, with applications in school choice, public housing, and online marketplaces. His research on school choice won multiple awards, including the ACM SIGecom Doctoral Dissertation Award, the INFORMS Public Sector Operations Best Paper Competition, and the INFORMS Doing Good with Good OR Student Paper Competition. Prior to joining USC, he completed a PhD in Operations Research at MIT, and was a postdoctoral researcher at Microsoft Research.
Host: Milind Tambe
Location: Seeley Wintersmith Mudd Memorial Hall (of Philosophy) (MHP) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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Chevron IT Information Session
Thu, Sep 28, 2017 @ 05:30 PM - 07:30 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: All Viterbi Students
Contact: RTH 218 Viterbi Career Connections
