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SUNMONTUEWEDTHUFRISAT
Events for the 5th week of May
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[Virtual] First-Year Admission Information SessionTue, May 31, 2022 @ 04:00 PM - 05:00 PM
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience. Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life. Guests will be able to ask questions and engage in further discussion toward the end of the session.
Register Here!
Audiences: Everyone Is Invited
Contact: Viterbi Admission
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PhD Thesis Proposal - Isabel Rayas
Wed, Jun 01, 2022 @ 01:30 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Isabel Rayas
Title: Advancing robot autonomy for long-horizon tasks
Committee:
Prof. Gaurav Sukhatme (chair)
Prof. Stefanos Nikolaidis
Prof. Dave Caron
Prof. Heather Culbertson
Prof. S.K. Gupta
Abstract:
Autonomy is essential for unstructured, long-horizon robotic tasks. Three aspects that help enable autonomy include allowing high-level goal descriptions in the task specification; reducing human intervention required to complete the task; and actively using information gained so far or about the problem in order to make a decision at each step in the task. In this talk, I will discuss how we can use techniques in motion planning to plan efficient motions for long-horizon, sequential tasks, and to learn how to represent motion constraints from demonstrations. Additionally, I will describe recent work and propose several projects using techniques in informative path planning to allow one or more autonomous robots to explore an environment while gathering information useful to the scientists that deployed them.
Zoom info:
Time: Jun 1, 2022 01:30 PM Pacific Time (US and Canada)
https://usc.zoom.us/j/91309840836?pwd=WXpsYXVuak1VVHlYcnYyYk9mNmZKZz09
Location: Ronald Tutor Hall of Engineering (RTH) - 406
WebCast Link: https://usc.zoom.us/j/91309840836?pwd=WXpsYXVuak1VVHlYcnYyYk9mNmZKZz09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Thesis Proposal - Yilei Zeng
Wed, Jun 01, 2022 @ 06:30 PM - 07:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Yilei Zeng
Title: "Learning Social Sequential Decision Making in Online Games"
Date and Time: 06/01 6:30pm
Committee:
Emilio Ferrara(Chair), Aiichiro Nakano(CS tenured), Stefanos Nikolaidis(CS tenure track), Dimitri Williams(Annenberg tenured), Michael Zyda (CS)
Abstract:
As the most significant entertainment industry by far, online games provide many of the most immersive experiences and are perceived as entrance points to the Metaverse. As the virtual worlds become more social and personalized, the need for human-centered AI to understand and model how humans make decisions in games grows. This thesis proposal introduces human-centered recommender systems in games that expand on three scales. We present social scenarios in microscale teams, mesoscale communities, and macroscale crowds. We also show the efficacies of small, heterogeneous, and multimodal data. The applications on the three scales are generalizable toward broader shopping, social, and content recommendations.
WebCast Link: https://usc.zoom.us/j/92485472421?pwd=cWxqQlIxa2Q3bHEvbkRiUnNEZFE2UT09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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[Virtual] First-Year Admission Information Session
Thu, Jun 02, 2022 @ 04:00 PM - 05:00 PM
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience. Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life. Guests will be able to ask questions and engage in further discussion toward the end of the session.
Register Here!
Audiences: Everyone Is Invited
Contact: Viterbi Admission
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PhD Defense - Chen-Yu Wei
Fri, Jun 03, 2022 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Chen-Yu Wei
Title: Robust and adaptive online decision making
Committee members: Haipeng Luo (host), David Kempe, Rahul Jain, Jaipeng Zhang
Time: 3pm - 5pm, June 3 (Friday)
Zoom link: https://usc.zoom.us/j/96811461450
Abstract:
Online learning (or online decision making) is a learning paradigm that involves real-time interactions between the learner and the environment. The learner has to make real-time decisions based on past data, and the learner's decision may further affect the data distribution in the future. This is more challenging than the traditional machine learning framework where the data is i.i.d. and the learner's decisions do not affect data distribution.
Because the learner's decisions are involved in the data collection process, an important general question is "how to efficiently explore the world in order to learn a good policy?" Past research has developed algorithms that can perform strategic exploration, and achieve near-optimal performance in the most difficult environment. However, this worst-case view is too pessimistic since there are usually some benign properties of the environment that the learner can take advantage of. Thus, a natural question is "how to design algorithms that can take advantage of the easiness of the environment?" We answer this question by developing algorithms whose performance can adapt to the easiness of the environment for several canonical online learning settings.
Since online learning is interactive, an adversary that exists in the environment may exploit the learner's algorithm, corrupt the data, and make the learner fail to learn good policies. If an algorithm totally fails only with a small amount of corruption, then the algorithm might be too unsafe to be deployed in practice. Therefore, we would like to have robust algorithms that can tolerate as much corruption as possible. We achieve the goal by developing algorithms whose performance scales optimally against the amount of corruption.
With adaptivity and robustness, an online learning algorithm will be able to more efficiently and more safely used in a wide spectrum of environments, without the learner having prior knowledge about the environment. We hope that the algorithmic techniques and insight developed in this thesis could be useful in improving existing algorithms for real applications.
WebCast Link: https://usc.zoom.us/j/96811461450
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
