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Events for August 31, 2022
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New & Continuing MS Student Group Advising Session (CSCI/DSCI)Wed, Aug 31, 2022 @ 10:00 AM - 11:00 AM
Computer Science
Workshops & Infosessions
If you are a New or Continuing MS student in the Computer Science Department or Data Science Program and have any questions or need assistance, please join us for today's optional group advising session via zoom. Access instructions will be sent to students directly. Note: D-clearance is not granted during advisement sessions. All requests for d-clearance must go through the myViterbi portal.
Location: Zoom
Audiences: Graduate
Contact: USC Computer Science
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The Communications Hub - Academic Writing and Speaking Tutoring for Viterbi Ph.D. Students
Wed, Aug 31, 2022 @ 10:00 AM - 12:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
The Communications Hub offers academic writing and speaking tutoring for Viterbi Ph.D. students! Bring your academic and professional work (at any stage) to faculty at the Engineering in Society Program!
Drop in hours are in RTH 222:
Monday: 10-12
Wednesday: 10-12
Friday: 10-12
We also offer online and custom appointments at https://sites.google.com/usc.edu/eishub/home.
See you at the Hub!Location: Ronald Tutor Hall of Engineering (RTH) - 222
Audiences: Graduate
Contact: Helen Choi
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Technology Innovation & Entrepreneurship Information Session
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Wed, Aug 31, 2022 @ 12:00 PM - 01:00 PM
USC Viterbi School of Engineering
Workshops & Infosessions
CALLING ALL INNOVATORS!
The USC Viterbi Office of Technology Innovation & Entrepreneurship invites you to join us on Wednesday, August 31 @ 12:00 PM for an information session hosted by Vice Dean Ellis Meng.
Come learn about:
Competitions: Learn how to develop a business model while competing for a cash prize! Teams will also have the chance to apply for prototyping funds and have access to business and technical mentors.
Curricular Programs and Classes: Learn to identify markets, position your technology, and pitch your ideas.
Internship Opportunities: Become an Entrepreneur in Training in an immersive, hands-on experience in a real startup! Other internship opportunities are also available.
$$$: Learn about how you can become eligible for a $50k I-Corps grant for your startup.
Building Your Team: Connect with like-minded students who share your passion about innovation and entrepreneurship.
RSVP
More Information: TIE INFO SESSION F2022 (1).pdf
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Everyone Is Invited
Contact: Johannah Murray/ Viterbi Office of Technology Innovation and Entrepenuership
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CS Undergraduate Live Chat Drop-in Advisement
Wed, Aug 31, 2022 @ 01:30 PM - 02:30 PM
Computer Science
Workshops & Infosessions
CS Advisors will be available on Tuesdays/Wednesdays/Thursdays this fall from 1:30pm to 2:30pm to assist undergraduates in our four majors (CSCI, CSBA, CSGA, and CECS) via Live Chat. Access the live chat through our website at https://cs.usc.edu/chat
Location: Live Chat on Website
Audiences: Undergrad
Contact: USC Computer Science
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PhD Thesis Proposal - Binh Vu
Wed, Aug 31, 2022 @ 03:00 PM - 04:30 PM
Computer Science
University Calendar
PhD Candidate: Binh Vu
Title: Building Semantic Description of Data Sources
Committee: Craig Knoblock, Sven Koenig, Yolanda Gil, Muhao Chen, Daniel O'Leary
Abstract: A semantic description of a data source precisely describes source attributes' types and the relationships between them. Building semantic descriptions is a prerequisite to automatically publish data to knowledge graphs (KGs). Previous work on this task can be placed into two groups: learning-based and value-linked methods. The learning-based methods require manually labeled semantic descriptions to train their systems. The value-linked methods use the linked entities in a data source to discover candidate semantic descriptions by matching the values in the source with values of entities' properties; hence they are unsupervised. However, the value-linked methods need linked entities and do not work well when the source's data is not in KGs. In this thesis proposal, we propose a method to address the limitations of the value-linked methods. We hypothesize that by exploiting knowledge from web tables and KGs, we can learn semantic descriptions of data sources even when there is little overlap between the sources' data and KGs.WebCast Link: https://usc.zoom.us/j/99238519131?pwd=R2ZUYlZoYVNiNXdxTXVFU1JFZXROdz09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Thesis Proposal - Chung-Wei Lee
Wed, Aug 31, 2022 @ 03:00 PM - 04:30 PM
Computer Science
University Calendar
PhD Candidate: Chung-Wei Lee
Title: Online Learning and Its Applications to Games and Partially Observable Systems
Committee: Haipeng Luo (host), David Kempe, Ashutosh Nayyar, Vatsal Sharan, Jiapeng Zhang
Abstract: Online Learning is a general framework for studying sequential decision-making. I will start with its applications in solving games. In particular, Online Learning has been shown as an essential theoretical foundation when building superhuman AI in poker games. We first focus on last-iterate convergence, a favorable property for online learning algorithms in two-player zero-sum games. In normal-form games, we show optimistic multiplicative weight updates (OMWU) and optimistic gradient descent ascent (OGDA) enjoy last-iterate convergence. We then generalize the results to extensive-form games (EFGs), which model sequential actions and incomplete information that appear in card games. We show that a family of regret minimization algorithms have last-iterate convergence, with some of them based on OMWU and OGDA can even converge exponentially fast. We then consider multiplayer games, where our goal becomes minimizing the individual regret of every player. We design two algorithms achieving logarithmic regret in EFGs based on ideas including a reduction from normal-form games and usage of a self-concordant regularizer on a lifted space.
In addition to solving EFGs as an application of Online Learning to partially observable systems, we discuss other examples, including dynamic pricing and recommender systems. Specifically, we formulate the problems as bandits with graph feedback and preference elicitation and discuss our contributions therein. Finally, I will talk about future work in all directions.
WebCast Link: https://usc.zoom.us/j/96191886806?pwd=UExwOXRyaG9ETUhmaW5udEF3TjYzQT09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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New & Continuing MS Student Group Advising Session (CSCI/DSCI)
Wed, Aug 31, 2022 @ 03:30 PM - 04:30 PM
Computer Science
Workshops & Infosessions
If you are a New or Continuing MS student in the Computer Science Department or Data Science Program and have any questions or need assistance, please join us for today's optional group advising session via zoom. Access instructions will be sent to students directly. Note: D-clearance is not granted during advisement sessions. All requests for d-clearance must go through the myViterbi portal.
Location: Zoom
Audiences: Graduate
Contact: USC Computer Science
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AME Seminar
Wed, Aug 31, 2022 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Ali Mani, Stanford University
Talk Title: Macroscopic Forcing Method: a computational method for evaluation of turbulence closure operators
Abstract: This study presents a numerical procedure, which we call the macroscopic forcing method (MFM), which reveals the differential operators acting upon the mean fields of quantities transported by underlying fluctuating flows. Specifically, MFM can reveal differential operators associated with turbulent transport of scalars and momentum. We present this methodology by considering canonical problems with increasing complexity. For spatially homogeneous and statistically stationary systems, we observe that eddy diffusivity can be approximated by an operator which in turbulent flows is on the order of the large-eddy size and 𝐷 is the Boussinesq limit eddy diffusivity. We show a cost-effective generalization of MFM for analysis of non-homogeneous and wall-bounded flows, where eddy diffusivity is found to be a non-local and non-isotropic operator acting on the macroscopic gradient of transported quantities. Towards the end of this talk, application of MFM on a canonical separated flow will be presented where the tensorial eddy viscosity is quantified, and its anisotropy is shown to be the key missing piece in RANS predictions.
Biography: Ali Mani is an associate professor of Mechanical Engineering at Stanford University. He is a faculty affiliate of the Center for Turbulence Research and a member of Institute for Computational and Mathematical Engineering at Stanford. He received his PhD in Mechanical Engineering from Stanford in 2009. Prior to joining the faculty in 2011, he was a senior postdoctoral associate at Massachusetts Institute of Technology in the Department of Chemical Engineering. His research group builds and utilizes large-scale high-fidelity numerical simulations, as well as methods of applied mathematics, to develop quantitative understanding of transport processes that involve strong coupling with fluid flow and commonly involve turbulence or chaos. His teaching includes the undergraduate engineering math classes and graduate courses on fluid mechanics and numerical analysis. He is the recipient of an Office of Naval Research Young Investigator Award (2015), NSF Career Award (2016), and Tau Beta Pi Teaching Honor Roll (2019).
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09Location: Seaver Science Library (SSL) - 202
WebCast Link: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
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