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  • PhD Thesis Proposal - Isabel Rayas

    Wed, Jun 01, 2022 @ 01:30 PM - 03:00 PM

    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

    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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  • Repeating Event[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

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    Contact: Viterbi Admission

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  • PhD Defense - Chen-Yu Wei

    Fri, Jun 03, 2022 @ 03:00 PM - 05:00 PM

    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

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  • PhD Defense - Brandon Paulsen

    Mon, Jun 06, 2022 @ 09:00 AM - 11:00 AM

    Computer Science

    University Calendar


    PhD Candidate: Brandon Paulsen

    Title: Differential Verification of Deep Neural Networks

    Time: Monday, June 6 9:00AM PST

    Location: https://usc.zoom.us/j/94495717078?pwd=WXhJOTN5YVVKNFB3K2ExSVZSakdkZz09

    Committee:
    Chao Wang (Advisor)
    Jyotirmoy Deshmukh
    Murali Annavaram

    Abstract:
    Neural networks have become an integral component of cyber-physical systems, such as autonomous vehicles, automated delivery robots, and factory robots, and they have great potential in many other systems as well. However, flaws in these models are frequently discovered, and thus in high-stakes applications, ensuring their safety, robustness, and reliability is crucial. While many prior works have been devoted to this problem domain, they are limited because they primarily focus on a few narrowly defined safety properties, and they only focus on the most common neural network architectures and activation functions.

    This dissertation addresses these limitations by (1) studying a new class of safety properties -- differential properties -- for neural networks, and (2) developing accurate algorithms for formally proving (or disproving) them that are applicable to general neural network architectures and activation functions. We focus on neural network equivalence as the canonical example for a differential property, however other safety properties concerning input sensitivity and stability can be cast as differential properties as well.

    This dissertation makes four key contributions towards developing accurate and general algorithms for proving differential properties. First, we formalize the equivalence problem for neural networks, and then develop a novel technique based on interval analysis for proving equivalence of any two structurally similar feed-forward neural networks with ReLU activations. The key insight in this technique is in deriving formulas that relate the intermediate computations of the two neural networks, which allows us to accurately bound the maximum difference between the two networks over all inputs. Second, we develop a novel symbolic technique that further improves the analysis' accuracy.
    We demonstrate the effectiveness of these two techniques in proving equivalence of compressed neural networks with respect to the original neural networks. Finally, we propose two novel techniques for automatically synthesizing linear approximations for arbitrary nonlinear functions, thus allowing our differential techniques to apply to architectures and activation functions beyond feed-forward ReLU networks. We demonstrate that our synthesized linear approximations significantly improve accuracy versus the best alternative techniques.


    WebCast Link: https://usc.zoom.us/j/94495717078?pwd=WXhJOTN5YVVKNFB3K2ExSVZSakdkZz09

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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  • Repeating Event[Virtual] First-Year Admission Information Session

    Tue, Jun 07, 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

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    Contact: Viterbi Admission

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  • Repeating Event[Virtual] First-Year Admission Information Session

    Thu, Jun 09, 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

    View All Dates

    Contact: Viterbi Admission

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  • NL Seminar Sources of Variance in Pretraining and Finetuning LLMs

    Mon, Jun 13, 2022 @ 02:00 PM - 03:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Naomi Saphra, NYU

    Talk Title: Sources of Variance in Pretraining and Finetuning LLMs

    Series: NL Seminar

    Abstract: REMINDER
    Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.

    If you are an outside visitor, please inform us at (nlg DASH seminar DASH host AT isi DOR edu beforehand so we will be aware of your attendance and let you in.

    You have engaged in the very modern practice of transfer learning. You pretrained a model on a self supervised objective, then you finetuned it on a downstream task, and you find excellent performance on the test set. Aha, you say. I found a good pretraining procedure. Did you? You try finetuning again. The results are terrible! Aha, you say. I found a bad finetuning procedure. Did you?

    The random seeds for both pretraining and finetuning stages have a substantial influence on outcome. However, it is computationally expensive to pretrain new models, so measuring the robustness of a procedure across different seeds can be prohibitive. This talk will address, first, the influence that a pretraining seed has on both in domain and OOD performance. Then we will address the role of the finetuning seed. Much variation in OOD generalization can be ascribed to where the finetuning seeds direct SGD trajectories. In particular, we discuss how to predict generalization behavior in a finetuned model, based on topographic properties of its region of the loss surface. By understanding the degree of influence that random seeds have on performance, we can fairly evaluate a robust training procedure, rather than a single set of parameters. By understanding the mechanism of that influence, we can go further by developing improved training methods.


    Biography: Naomi has interests relating to NLP learning dynamics how models learn to encode linguistic structure, and how we can encode useful inductive biases into the training process. Having earned a PhD from University of Edinburgh, they are now a postdoc at NYU. In their spare time, they play roller derby under the name Gaussian Retribution, do standup comedy, and shepherd programmers who cannot type into the world of code dictation.

    Host: Jon May and Thamme Gowda

    More Info: https://nlg.isi.edu/nl-seminar/

    Webcast: https://www.youtube.com/watch?v=Lni4PIlbJjI

    Location: Information Science Institute (ISI) - Virtual

    WebCast Link: https://www.youtube.com/watch?v=Lni4PIlbJjI

    Audiences: Everyone Is Invited

    Contact: Pete Zamar

    OutlookiCal
  • Repeating Event[Virtual] First-Year Admission Information Session

    Tue, Jun 14, 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

    View All Dates

    Contact: Viterbi Admission

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  • PhD Defense - Jiaoyang Li

    Thu, Jun 16, 2022 @ 04:00 PM - 06:00 PM

    Computer Science

    University Calendar


    PhD Candidate: Jiaoyng Li

    Title:
    Efficient and Effective Techniques for Large-Scale Multi-Agent Path Finding

    Committee:
    Sven Koenig, T. K. Satish Kumar, Satyandra K. Gupta, Nora Ayanian , and Brian C. Williams.

    Abstract:
    There is no doubt that robots will play a crucial role in the future and need to work as a team in increasingly more complex applications. Advances in robotics have laid the hardware foundations for building large-scale multi-robot systems, such as for mobile robots, vehicles, and drones. But how to coordinate robots intelligently is a difficult problem. In this dissertation, I introduce planning algorithms for solving this challenge with a focus on one fundamental problem: letting a large team of agents navigate without collisions in congested environments while minimizing their travel times. I present techniques based on heuristic search, symmetry breaking, and stochastic local search that can efficiently and effectively coordinate hundreds of agents with rigorous guarantees of completeness and even optimality and thousands of agents with good empirical performance (although no theoretical guarantees). These techniques speed up optimal and bounded-suboptimal algorithms by up to four orders of magnitude without sacrificing their theoretical guarantees and improve the solution quality of non-optimal algorithms by up to thirty-six times.

    Location: Henry Salvatori Computer Science Center (SAL) - 322

    WebCast Link: https://usc.zoom.us/j/93790809266?pwd=SDVIMWFtYTVtaEZpeVNGM0MxSWM2dz09

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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  • Repeating Event[Virtual] First-Year Admission Information Session

    Thu, Jun 16, 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

    View All Dates

    Contact: Viterbi Admission

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  • Repeating Event[Virtual] First-Year Admission Information Session

    Tue, Jun 21, 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

    View All Dates

    Contact: Viterbi Admission

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  • Ph.D. Thesis Proposal - Aniruddh G. Puranic

    Wed, Jun 22, 2022 @ 12:00 PM - 02:00 PM

    Computer Science

    University Calendar


    Candidate: Aniruddh G. Puranic

    Thesis title: Learning from Demonstrations with Temporal Logics

    Committee: Jyotirmoy V. Deshmukh, Stefanos Nikolaidis, Gaurav Sukhatme, Mukund Raghothaman, Somil Bansal, Julie Shah (MIT)

    Date: June 22, 2022 (Wednesday)
    Time: 12pm - 2pm Pacific Time
    Location: SAL 213

    Abstract:

    Learning-from-demonstrations (LfD) is a popular paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions. However, it is susceptible to imperfections in demonstrations and raises concerns of safety and interpretability in the learned control policies. To address these issues, we propose to use Signal Temporal Logic (STL) to express high-level robotic tasks and use its quantitative semantics to evaluate and rank the quality of demonstrations. Temporal logic-based specifications allow us to create non-Markovian rewards and are also capable of defining interesting causal dependencies between tasks such as sequential task specifications. We present our completed work which proposed the LfD-STL framework that learns from even suboptimal/imperfect demonstrations and STL specifications to infer rewards on which reinforcement learning can be performed to obtain control policies. Through numerous experiments, we have shown that our approach outperforms prior LfD methods.

    We then propose further extensions to this framework to develop metrics that provide intuitive explanations about demonstrators' behaviors, which combined with the interpretability of the learned robot policies, can help in building a safe and trusted robotic system for human interaction. As our long-term goals, we plan to use this metric as an optimization function to be used to potentially learn policies that perform better than the (imperfect) demonstrators.

    Location: Henry Salvatori Computer Science Center (SAL) - 213

    WebCast Link: https://usc.zoom.us/j/94560935551?pwd=ejY1UG1xTUZaQWJER1NOOUJNcGhQdz09

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

    OutlookiCal
  • NL Seminar-Weighted Finite-State Transducers: The Later Years

    Thu, Jun 23, 2022 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Kyle Gorman, Graduate Center, City University of New York and Google Inc.

    Talk Title: Weighted Finite-State Transducers: The Later Years

    Series: NL Seminar

    Abstract: REMINDER
    Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.

    If you are an outside visitor, please inform us at (nlg DASH seminar DASH host AT isi DOR edu beforehand so we will be aware of your attendance and let you in.

    In-person attendance will be permitted for USC ISI faculty, staff, students only. Open to the public virtually via the zoom registration link and online.

    While the deep learning tsunami defines the state of the art in speech and language processing, finite state transducer grammars developed by linguists and engineers are still widely used in highly multilingual settings, particularly for front end speech applications. In this talk, I will first briefly review the current state of the OpenFst and OpenGrm finite state transducer libraries. I will then discuss several recent innovations in the finite state world. These include algorithms for inducing text normalization and grapheme to phoneme grammars from parallel data, heuristic optimization of arbitrary weighted transducers, and an algorithm for efficiently computing the single shortest string of a wider variety of non deterministic weighted acceptors.

    Biography: Kyle Gorman is an assistant professor of linguistics at the Graduate Center, City University of New York, and director of the masters program in computational linguistics. He is also a software engineer in the speech and language algorithms group at Google. With Richard Sproat, he is the coauthor of Finite State Text Processing and the creator of Pynini, a finite state text processing library for Python. He has also published on statistical methods for comparing computational models, text normalization, grapheme to phoneme conversion, and morphological analysis, as well as many topics in linguistic theory.

    Host: Jon May and Thamme Gowda

    More Info: https://nlg.isi.edu/nl-seminar/

    Webcast: https://www.youtube.com/watch?v=BpEqB3Vj4mM

    Location: Information Science Institute (ISI) - Virtual

    WebCast Link: https://www.youtube.com/watch?v=BpEqB3Vj4mM

    Audiences: Everyone Is Invited

    Contact: Pete Zamar

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  • Repeating Event[Virtual] First-Year Admission Information Session

    Thu, Jun 23, 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

    View All Dates

    Contact: Viterbi Admission

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  • Virtual First-Year Admission Information Session

    Sat, Jun 25, 2022 @ 09:00 AM - 10:00 AM

    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

    OutlookiCal
  • PhD Defense - Rajat Tandon

    Mon, Jun 27, 2022 @ 03:30 PM - 05:30 PM

    Computer Science

    University Calendar


    PhD Candidate: Rajat Tandon

    Title: Protecting online services from sophisticated attacks

    Date and Time: Monday, 06/27 3:30pm

    Committee:
    Jelena Mirkovic (chair), Barath Raghavan, Ning Wang, Phebe Vayanos and Genevieve Bartlett

    Abstract: Online services are often targets of sophisticated attacks, which aim to overwhelm services or steal user data. In this work, we present solutions, which aim to protect services against sophisticated distributed denial-of-service attacks. These solutions can effectively handle attacks that: (1) involve sending requests which resemble legitimate ones, (2) involve exploiting vulnerabilities that exist in different online services, (3) take advantage of the changing trends in network traffic, and (4) often require online services to get help from their ISPs for mitigation, due to the high volumes of attack traffic.

    Zoom link: https://usc.zoom.us/j/93346323630?pwd=MlJIVTd3d29zMHcxdWd0VVI3bTh5QT09


    WebCast Link: https://usc.zoom.us/j/93346323630?pwd=MlJIVTd3d29zMHcxdWd0VVI3bTh5QT09

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

    OutlookiCal
  • Repeating Event[Virtual] First-Year Admission Information Session

    Tue, Jun 28, 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

    View All Dates

    Contact: Viterbi Admission

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  • Repeating EventFirst-Year Admission Information Session

    Wed, Jun 29, 2022

    Viterbi School of Engineering Undergraduate Admission

    Workshops & Infosessions


    Join the Viterbi School of Engineering for an in-depth discussion about our engineering and computer science undergraduate programs, the application process, and more on student life. This program is hosted by an admission counselor from the Viterbi School of Engineering and a current student (when classes are in session for the fall and spring semesters). It is designed for prospective high school students students and their family members to better understand our academic programs as well as how to best prepare for them. Prospective students and family members will be able to ask questions and engage in further discussion in order to better understand all aspects of our programs prior to applying.

    Reserve your spot!

    Audiences: Everyone Is Invited

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    Contact: Viterbi Admission

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  • NL Seminar-Anti Queer Bias in Large Language Models

    Thu, Jun 30, 2022 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Katy Felkner, USC/ISI

    Talk Title: Anti-Queer Bias in Large Language Models

    Series: NL Seminar

    Abstract: REMINDER:
    Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.

    If you are an outside visitor, please inform us at (nlg DASH seminar DASH host AT isi DOR edu beforehand so we will be aware of your attendance and let you in.

    In-person attendance will be permitted for USC ISI faculty, staff, students only. Open to the public virtually via the zoom registration link and online.

    Happy Pride. To close out Pride Month at ISI, this talk will discuss fairness and bias in LLMs as it relates to the LGBTQ community. We will explore current methods for detecting and mitigating bias in LLMs, as well as the lack of current research focusing specifically on homophobic and transphobic biases. The talk will present recent exploratory work on whether and to what extent biases against queer and trans people are encoded in large language models LLMs such as BERT. It will discuss a new method for reducing these biases in downstream tasks: fine-tuning the models on data written by and or about queer people. It will also discuss a new benchmark dataset, WinoQueer, modeled after other bias detection benchmarks but addressing homophobic and transphobic biases. This work was accepted to the Queer in AI workshop at NAACL 2022.

    Biography: Katy Felkner is a rising 3rd year PhD student at USC Information Sciences Institute. Her primary research focus is extremely low-resource machine translation. She is also interest in fairness and bias in large language models. Prior to USC, she received dual bachelors degrees in Computer Science and Letters general humanities from the University of Oklahoma. Her research is supported by an NSF Graduate Research Fellowship. Katy is passionate about making computer science more welcoming for women and queer students.

    Host: Jon May and Thamme Gowda

    More Info: https://nlg.isi.edu/nl-seminar/

    Webcast: https://usc.zoom.us/j/96713436677

    Location: Information Science Institute (ISI) - Virtual

    WebCast Link: https://usc.zoom.us/j/96713436677

    Audiences: Everyone Is Invited

    Contact: Pete Zamar

    OutlookiCal
  • PhD Thesis Proposal - Jacqueline [Lina] Brixey

    Thu, Jun 30, 2022 @ 04:00 PM - 05:30 PM

    Computer Science

    University Calendar


    PhD Candidate: Jacqueline [Lina] Brixey

    Title: Code-switching dialogue systems for language documentation and conversational fluency

    Committee: David Traum, Maja Mataric, Khalil Iskarous (USC Linguistics),Kallirroi Georgila, and Jon May

    Date: June 30th
    Time: 4pm - 5:30 pm
    Zoom: https://usc.zoom.us/j/93068920916


    Abstract:
    Bilingual dialogue systems, or systems that can speak two or more languages within a single conversation, are important systems to consider and implement because many people in the world are bilingual. Code-switching, or switching languages either within a single utterance or between utterances, is a common behavior of bilinguals. Programs that could understand and respond to a user in several languages would allow more users to benefit and interact with dialogue systems comfortably. Current state of the art includes dialogue systems that can speak multiple languages, such as Amazon Alexa that can respond to queries in English or Spanish, among other languages. However, few systems have been developed to process multiple languages being present within a single utterance or to initiate code-switching. I hypothesize that code-switching in dialogue systems can lead to a better user experience and more productive interactions than monolingual systems. I will explore this hypothesis in two applications.The first is a system named DAPEL that records endangered languages through dialogue. The second is a system, named Masheli, designed for language learners to practice conversational fluency. Both applications will focus on Choctaw-English bilingualism; Choctaw is a low-resource and endangered American indigenous language.



    WebCast Link: https://usc.zoom.us/j/93068920916

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

    OutlookiCal
  • Repeating Event[Virtual] First-Year Admission Information Session

    Thu, Jun 30, 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

    View All Dates

    Contact: Viterbi Admission

    OutlookiCal