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University Calendar
Events for February
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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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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 NakanoLocation: 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
