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University Calendar
Events for February

  • Viterbi Career & Internship Expo: Trojan Talks and Demos

    Mon, Feb 06, 2023 @ 10:00 AM - 05:00 PM

    Viterbi School of Engineering Career Connections

    University Calendar


    Groups of pre-registered students meet with an organization for a 45-minute company info session or company product or service showcase in an on-campus location.

    Pre-registration required on Viterbi Career Gateway: USC Viterbi School of Engineering > Events > Information Sessions

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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  • Viterbi Career & Internship Expo: On-Campus Trojan Talks and Demos

    Tue, Feb 07, 2023 @ 10:00 AM - 05:00 PM

    Viterbi School of Engineering Career Connections

    University Calendar


    Groups of pre-registered students meet with an organization for a 45-minute company info session or company product or service showcase in an on-campus location.

    Pre-registration is required on Viterbi Career Gateway:
    USC Viterbi School of Engineering > Events > Information Sessions

    For the most up-to-date information visit the Career & Internship Expo Website: USC Viterbi School of Engineering

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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  • Viterbi Career & Internship Expo: Career Fair (On-Campus)

    Wed, Feb 08, 2023 @ 10:00 AM - 05:00 PM

    Viterbi School of Engineering Career Connections

    University Calendar


    Viterbi Career Connections is excited to announce the Fall 2022 Career & Internship Fair will be hosted on campus! This recruitment event allows students the opportunity to have brief conversations with recruiters about full-time employment, internships, and co-ops. Join additional activities such as Trojan Talks and Meet & Greets on Febraury 6th and 7th.
    The Viterbi Career & Internship Expo is free and open to all students in the USC Viterbi School of Engineering. Don't forget your resume!

    For more information about the Expo: USC Viterbi School of Engineering

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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  • PhD Thesis Proposal - Baskin B. Senbaslar

    Thu, Feb 09, 2023 @ 12:30 PM - 02:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    Title:
    Thesis Proposal: Real-time Trajectory Planning for Mobile Robot Navigation in Cluttered Environments

    Committee:
    Gaurav Sukhatme (Chair), Laurent Itti, Mihailo R. Jovanovic, Sven Koenig, and Satish Kumar Thittamaranahalli

    Date:
    Thursday, February 9th, 12:30pm PST

    Location:
    RTH 406

    Zoom Meeting Details:
    https://usc.zoom.us/j/97713760524?pwd=Ynh5enZTTzlTZmNpdW5FWmlGSGhtQT09

    Abstract:
    Collision-free mobile robot navigation in cluttered environments is a central problem for many robotics applications. In such environments, obstacles can be static, i.e. stationary, or dynamic, i.e. moving. Dynamic obstacles can be cooperative or non-cooperative. Non-cooperative dynamic obstacles can be interactive, i.e. changing their behavior according to the behavior of other entities, or not interactive. Navigation in such environments with obstacles with different behavior modalities require real-time decision making capabilities.

    In this thesis proposal, we investigate the safety requirements against these different types of objects, and develop a real-time trajectory planning algorithm satisfying the safety requirements. We investigate the context in which the planner should be used and delve into its interaction with other system components, including reactive controllers and global trajectory planners

    Location: Ronald Tutor Hall of Engineering (RTH) - 406

    WebCast Link: https://usc.zoom.us/j/97713760524?pwd=Ynh5enZTTzlTZmNpdW5FWmlGSGhtQT09

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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  • PHD Thesis Proposal (John Francis)

    Thu, Feb 09, 2023 @ 01:30 PM - 03:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Candidate: John Francis
    Date: 02/09/23 (Thursday)

    Committee: Mike Zyda, Carl Kesselman, Jernej Barbic, Scott Fraser, Kate White

    Abstract:

    The structural modeling of cells can be accomplished by integrating images of cellular morphology from multiple scales and modalities using a parts based approach. In this thesis, we demonstrate a method for combining the statistical distribution of structures from x-ray tomography and fluorescence microscopy using neural networks to predict the localization of high resolution components in low resolution modalities by using the single cell as a shared unit of transfer.

    Location: https://usc.zoom.us/j/92031065960

    Audiences: Everyone Is Invited

    Contact: Asiroh Cham

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  • BME Seminar Speaker, Dr. Rui Cao

    Thu, Feb 23, 2023 @ 02:00 PM - 03:00 PM

    Alfred E. Mann Department of Biomedical Engineering

    University Calendar


    More Information: bme seminar rui cao.pdf

    Location: Corwin D. Denney Research Center (DRB) - 145

    Audiences: Everyone Is Invited

    Contact: Michele Medina

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  • PHD Thesis Proposal (Meryem M'Hamdi)

    Mon, Feb 27, 2023 @ 03:30 PM - 05:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    Presentation Title: Towards More Human-Like Cross-lingual Transfer Learning

    Abstract: Since their release, multilingual off-the-shelf representations such as M-BERT and other Transformer-based variants has gained tremendous popularity. Despite exhibiting surprisingly good zero-shot performance, they are often pre-trained and fine-tuned in a data-intensive manner and are less robust against data distribution shifts which is orthogonal to how humans learn. In this thesis proposal, we analyze and propose techniques to advance the capabilities of multilingual language models beyond this data-intensive identically distributed paradigm and more towards human-like cross-lingual transfer learning. We achieve that through human-inspired input requirements by adapting few-shot meta-learning approaches, human-inspired outcomes by understanding what it means to learn continually over a stream of languages, and cognitive human-learning strategies like spaced repetition to consolidate retention of knowledge learned across languages. We apply our techniques to information extraction, natural language understanding, question answering, and semantic search downstream tasks and analyze on typologically diverse benchmarks.


    Committee Members: Jonathan May (Chair), Kallirroi Georgila, Xuezhe Ma, Shrikanth Narayanan, Aiichiro Nakano

    Location: https://usc.zoom.us/j/94778821094?pwd=cFZISUdZZ0trUlpMNFdGSEE0TDExdz09

    Audiences: Everyone Is Invited

    Contact: Asiroh Cham

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  • PhD Candidate: Shichen Liu

    Tue, Feb 28, 2023 @ 03:00 PM - 04:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    Title: Learning to Optimize the Geometry and Appearance from Images.


    Abstract:
    The ability to infer geometry and appearance from images impacts various applications such as AR/VR, autonomous driving, and more. Compared to traditional methods, deep convolutional neural networks have proven to be more robust and accurate. However, the practical use of deep learning in these applications still faces three major challenges: (1) the acquisition of 3D training data; (2) the development of a fast, robust, and accurate 3D vision framework; (3) the integration of complex 3D representations into the neural network.

    To address these challenges, my research focuses on optimization techniques in the context of deep learning. Specifically, when paired 2D and 3D data is not available, we propose a differentiable rendering framework that allows neural networks to learn 3D shapes directly from 2D images. On the other hand, when full supervision is available, we develop a framework that trains a neural network to optimize the target representation and demonstrate the performance on the vanishing point detection task. Finally, we explore the face avatar creation task and propose dense visual-semantic correlation on top of a semantically-aligned UV space to effectively integrate complex 3D representations into the neural optimization framework. Our neural optimization techniques help to develop practical 3D computer vision systems.

    Committee members are Randall Hill, Andrew Nealen, Aiichiro Nakano, Stefanos Nikolaidis, and Yajie Zhao.

    Location: https://usc.zoom.us/j/3154287574.

    Audiences: Everyone Is Invited

    Contact: Asiroh Cham

    Event Link: https://usc.zoom.us/j/3154287574

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