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Events for December
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CS Colloquium: Yuto Nakanishi, Ph.D. (GITAI USA, Inc.) - Challenge to Develop Space Robots for Building a Moonbase
Fri, Dec 01, 2023 @ 03:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yuto Nakanishi, Ph.D., GITAI USA, Inc.
Talk Title: Challenge to Develop Space Robots for Building a Moonbase
Abstract: GITAI is a space robotics start-up developing tools to reduce the risk and cost of labor in space. Our robots are capable of autonomous operations including structure assembly and handling tools in a vacuum. We are working towards a robotics space labor force that could reduce space labor costs by 100-fold.GITAI is unique among space start-ups in developing all the mechatronics, electronics, and software of the robot in-house to achieve a tight integration of the best technologies.In this talk, I will talk about why GITAI focuses on developing space robots with my robotics experiences at the University of Tokyo, SCHAFT, and Google and will introduce GITAI’s latest challenge of space robot development, especially our new inchworm modular arms, and rovers for future lunar exploration.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Chief Robotics Officer of GITAI. Former Founder & CEO of SCHAFT. After retiring as an assistant professor at the University of Tokyo Graduate School of Information Science and Technology (JSK Lab), he founded the bipedal robot startup, SCHAFT, the champion of DARPA Robotics Challenge Trials in 2013. He later sold the company to Google in 2013, and had led Tokyo biped platforms development team under Google X for 5 years. Now, he joined GITAI to develop space robots to build moon base.
Host: Stefanos Nikolaidis
Location: Hedco Pertroleum and Chemical Engineering Building (HED) - 116
Audiences: Everyone Is Invited
Contact: CS Events
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PhD Thesis Defense - Gautam Salhotra
Tue, Dec 05, 2023 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Gautam Salhotra
Committee Members: Gaurav Sukhatme (chair), Somil Bansal, Daniel Seita
Title: Accelerating Robot Manipulation with demonstrations
Abstract: Robot manipulation of complex objects, such as cloth, is challenging due to difficulties in perceiving and exploring the environment. Pure reinforcement learning (RL) is difficult in this setting, as it requires extensive exploration of the state space, which can be inefficient and dangerous. Demonstrations from humans can alleviate the need for exploration, but collecting good demonstrations can be time-consuming and expensive. Therefore, a good balance between perception, exploration, and imitation is needed to solve manipulation of complex objects.This thesis focuses on dexterous manipulation of complex objects, such as cloth, using images and without assuming full state information during inference. It also aims to achieve efficient learning by reducing interactions with the environment during exploration and reducing the overhead of collecting demonstrations. To achieve these goals, we present i. a learning algorithm that uses a motion planner in the loop, to enable efficient long horizon exploration, ii. A framework for visual manipulation of complex deformable objects using demonstrations from a set of agents with different embodiments. iii. An LfD algorithm for dexterous tasks with rigid objects, such as peg insertion with high precision, using images and a multi-task attention-based architecture.These contributions enable robots to manipulate complex objects efficiently and with high precision, using images alone. This opens up new possibilities for robots to be used in a wider range of applications, such as manufacturing, logistics, and healthcareLocation: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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CS Colloquium: Yitao Liang - Towards Generalist Agents in a Open-World Environment
Tue, Dec 05, 2023 @ 04:00 PM - 05:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yitao Liang, Peking University
Talk Title: Towards Generalist Agents in a Open-World Environment
Abstract: With the advent of large language models, the debate about whether generalist agents are coming resurges. It maybe an over ambitious goal. Yet, to make any progress, we need an appropriate testing bed accompanied with principled evaluation protocols. In our past findings, we noticed that the prior testing beds for agents are mostly designed to have one specific task and goal (sometimes specified by one reward function). This greatly limits our ability to benchmark whether we are making significant progress in building a generalist agent. In this tutorial, we will introduce the comprehensive efforts from my group and a few other related prominent research labs of using open-world environments (e.g., Minecraft) to target generalist agents. We will dig into why now it is a good time to do the switch; what are the characteristics of those environments; what are the unique challenges to them and how addressing those challenges are indispensable from generalist agents; and lastly, how the latest research in this area is reshaping our community
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Yitao Liang is an assistant professor at Peking University. He obtained his Ph.D. degree in Computer Science from UCLA, advised by Prof. Guy Van den Broeck. His research interests span knowledge reasoning and machine learning.His work has received recognition from top AI conferences; for example, the best-paper honorable mention from AAMAS 2016, the best paper from RL for Real Life workshop in ICML 2019, a best paper runner-up from the LLD workshop in NeurIPS 2017, a best paper from the TEACH workshop in ICML2023. He regularly serves as area chairs in top venues. Recently, his group Team CraftJarvis (craftjarvis.org) is taking a neural-symbolic approach to building a generalist agents in open-world environments
Host: Jieyu Zhao
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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CS Colloquium: Oren Salzman (Technion - Israel Institute) - Towards Contact-Aided Motion Planning for Tendon-Driven Continuum Robots: A step-by-step tutorial of applying heuristic search in the wild.
Wed, Dec 06, 2023 @ 09:30 AM - 10:30 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Oren Salzman, Technion - Israel Institute
Talk Title: Towards Contact-Aided Motion Planning for Tendon-Driven Continuum Robots: A step-by-step tutorial of applying heuristic search in the wild.
Abstract: Tendon-driven continuum robots (TDCRs), with their flexible backbones, offer the advantage of being used for navigating complex, cluttered environments. However, to do so, they typically require multiple segments, often leading to complex actuation and control challenges. To this end, we propose a novel approach to navigate cluttered spaces effectively for a single-segment long TDCR which is the simplest topology from a mechanical point of view. Our key insight is that by leveraging contact with the environment we can achieve multiple curvatures without mechanical alterations to the robot. Specifically, we propose a search-based motion planner for a single-segment TDCR. This planner, guided by a specially designed heuristic, discretizes the configuration space and employs a best-first search. In the talk I will cover the steps required to apply heuristic search to a complex robotic system; from kinematic modeling to heuristic computation. The talk assumes some background in heuristic search but requires no robotic background.The talk is based on joint work with Priyanka Rao and Jessica Burgner-Kars from UoT.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Oren Salzman is an assistant Professor at the Computer Science department at the Technion - Israel Instituteof Technology. His research focuses on revisiting classical computer science algorithms, tools and paradigms to address the computational challenges that arise when planning motions for robots. Combining techniques from diverse domains such as computational geometry, graph theory and machine learning, he strives to provide efficient algorithms with rigorous analysis for robot systems with many degrees of freedom moving in tight quarters. He completed a PhD in the School of Computer Science at Tel Aviv University under the supervision of Prof. Dan Halperin. He then continued his studies as a postdoctoral researcher at Carnegie Mellon University working with Siddhartha Srinivasa and Maxim Likhachev and as a research scientist at the National Robotics Engineering Center (NREC). Oren has published over sixty peer-reviewed conference and journal papers. He received the best paper and best student paper in ICAPS 18 and ICAPS 19, respectively as well as a nomination for the best-paper award at RSS 21.
Host: Sven Koenig
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: CS Events
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PhD Thesis Proposal - Nathan Bartley
Wed, Dec 06, 2023 @ 12:00 PM - 01:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Nathan Bartley Committee Members:
Kristina Lerman (chair)
Mike Ananny
Emilio Ferrara
red Morstatter
Barath Raghavan
Title: Content Exposure Bias and Online Social Networks
Abstract: Online social platforms employ personalized feed algorithms to gather and collate messages from accounts users follow. However, the network structure and activity of the followed users distorts content’s perceived popularity prior to personalization. We call this “exposure bias:” our research focuses on quantifying it using diverse metrics, and we evaluate different algorithms that underpin personalized feeds with these metrics. We use empirical X/Twitter data and simulations in a network to assess the influence different feeds have on exposure bias. Furthermore we are working on agent-based model simulations to comprehend the impact of changing feeds, with the ultimate goal of making interventions.
Location: https://usc.zoom.us/j/98609708157?pwd=VWJuMVROL3Z5YVZmWDFWQ2xRRzNOUT09
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: https://usc.zoom.us/j/98609708157?pwd=VWJuMVROL3Z5YVZmWDFWQ2xRRzNOUT09
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CSCI 591 Colloquium: Masashi Sugiyama (RIKEN/The University of Tokyo) - Machine Learning from Weak, Noisy, and Biased Supervision
Fri, Dec 08, 2023 @ 12:00 PM - 01:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Masashi Sugiyama , RIKEN/The University of Tokyo
Talk Title: Machine Learning from Weak, Noisy, and Biased Supervision
Abstract: In statistical inference and machine learning, we face a variety of uncertainties such as training data with insufficient information, label noise, and bias. In this talk, I will give an overview of our research on reliable machine learning, including weakly supervised classification (positive unlabeled classification, positive confidence classification, complementary label classification, etc.), noisy label classification (noise transition estimation, instance-dependent noise, clean sample selection, etc.), and transfer learning (joint importance-predictor estimation for covariate shift adaptation, dynamic importance estimation for full distribution shift, continuous distribution shift, etc.).
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Masashi Sugiyama received his Ph.D. in Computer Science from the Tokyo Institute of Technology in 2001. He has been a professor at the University of Tokyo since 2014, and also the director of the RIKEN Center for Advanced Intelligence Project (AIP) since 2016. He is (co-)author of Machine Learning in Non-Stationary Environments (MIT Press, 2012), Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), and Machine Learning from Weak Supervision (MIT Press, 2022). In 2022, he received the Award for Science and Technology from the Japanese Minister of Education, Culture, Sports, Science and Technology. He was program co-chair of the Neural Information Processing Systems (NeurIPS) conference in 2015, the International Conference on Artificial Intelligence and Statistics (AISTATS) in 2019, and the Asian Conference on Machine Learning (ACML) in 2010 and 2020.
Host: Yan Liu
Location: Seeley Wintersmith Mudd Memorial Hall (of Philosophy) (MHP) - 101
Audiences: Everyone Is Invited
Contact: CS Events
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PhD Thesis Defense - Tiantian Feng
Mon, Dec 11, 2023 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Tiantian Feng
Committee Members: Professor Shrikanth Narayanan, Professor Aiichiro Nakano, Professor Kristina Lerman, and Professor Morteza Dehghani (external)
Title: Foundation Model Assisted Privacy-Enhancing Computing in Human-centered Machine Intelligence
Abstract: Human-centered machine intelligence has revolutionized many leading domains, ranging from transportation and healthcare to education and defense, profoundly changing how people live, work, and interact with each other. These systems utilize state-of-the-art machine learning (ML) algorithms to achieve a deeper understanding of human conditions, such as state, trait, and interactions, which provide possibilities to create technologies that increasingly support and enhance human experiences. Despite promises human-centric ML systems deliver, they create critical risks in potentially leaking sensitive information that people might want to keep private. The sensitive information can be individual attributes (e.g., age, gender), states (e.g., health, emotions), or biometric fingerprints. In this thesis, I explore privacy-enhancing computation associated with human-centered ML. My thesis investigates established approaches to preserve privacy in diverse human-centered applications. However, we identify that these approaches are frequently ineffective when encountering low-resource data due to privacy restrictions in sensing, storing, and using such data. Concurrently, the foundation model is a rapidly evolving research field, leading to the success of modern generative AI capable of creating realistic and high-fidelity digital content. These advances in foundation models and generative AI also present opportunities for privacy-enhancing computing as high-quality generated content can serve as training data. This leads us to explore using the foundation model to generate training data to assist low-resource training encountered with sensitive data in human-centered applications. Our extensive experiments demonstrate the potential of the foundation model in assisting low-resource training caused by privacy constraints in obtaining human-centered signals.Location: Ronald Tutor Hall of Engineering (RTH) - 320
Audiences: Everyone Is Invited
Contact: CS Events
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Computer Science General Faculty Meeting
Wed, Dec 13, 2023 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Invited Faculty Only
Contact: Assistant to CS Chair
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PhD Thesis Defense - Kexuan Sun
Wed, Dec 13, 2023 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Committee members:
Prof. Jay Pujara
Prof. Aiichiro Nakano
Prof. Gerard Hoberg
Title: Advances in Understanding and Leveraging structured data for knowledge-intensive tasks
Abstract: Over the past few decades, the Web has evolved into an essential information hub. Among the vast repository of information, structured data, including well-organized tables, charts, and knowledge graphs, distinguishes itself as a valuable source of knowledge. This dissertation investigates techniques for understanding and harnessing such structured data to enhance knowledge-intensive applications. The first part of the dissertation focuses on tabular data. I first investigate approaches for understanding complex table structures by introducing an automated hybrid probabilistic system that identifies sub-structures within tables and their relationships, offering potential benefits for downstream tasks like data integration. I then explore approaches for selecting valuable information to answer questions relying heavily on financial tables. We approach this task by leveraging case-based reasoning, adapting solutions from existing questions to answer new questions effectively. The second part of the dissertation delves into the realm of KGs. I begin by investigating scientific KGs construction and empirically explore techniques that combine inherent graph structures and external entity-associated information. Additionally, I introduce a novel approach for accurately selecting important information from KGs to answer general-domain questions. These advances are necessary to fully exploit multi-source integrated systems that leverage unstructured and structured information together for knowledge delivery.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 203
Audiences: Everyone Is Invited
Contact: CS Events
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Thesis Proposal (Zihao He)
Wed, Dec 13, 2023 @ 03:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Committee members:
Kristina Lerman (Chair)
Emilio Ferrara
Jonathan May
Fred Morstatter
Marlon Twyman
Title: Exploring Polarization and Ideological Difference of Online Communities Through Language Models
Abstract: The proliferation of diverse information sources and social platform interactions has led to increased ideological polarization, presenting unique challenges in understanding and quantifying these divides. This thesis tackles the nuanced task of analyzing ideological polarization of online communities through language models. First, I extract contextualized topic embeddings from a pretrained language model, focusing on identifying polarized topics within various information sources. Next, I use a generative language model to probe into the ideological dimensions within social media discourse, specifically examining Twitter conversations around key political figures; this approach uncovers the complexities within user interactions and the formation of opinion clusters. Finally, I investigate the alignment between the affective responses of large language models and human ideologies.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 131A
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: https://usc.zoom.us/j/98773410609?pwd=SXQzekVMZjZ6dVhSdWJCRGlrVlFFZz09
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International Conference on Holodecks
Fri, Dec 15, 2023
Thomas Lord Department of Computer Science, USC Viterbi School of Engineering
Receptions & Special Events
Emerging immersive reality and interactive technology are realizing the vision of a holodeck. Holodecks will transform the future of human communication and perception, and how we interact with information and data. The International Conference on Holodecks is a forum for researchers, practitioners, vendors, application developers, and users of immersive and interactive technology for a realistic 2D and 3D simulation of a real or imaginary setting. Its objective is to enable its participants to exchange ideas and form collaborations to expedite the development of holodecks. We seek to develop a community that revolutionizes how we work, learn, play and entertain, receive medical care, and socialize.The conference will be held on December 15 from 9:00 am to 5:00 pm (with a social hour to follow) at the Salvatori Computer Science Center (SAL) at UPC.
Registration is required, and space is limited. Visit https://www.holodecks.quest/agenda to view the technical program.Location: Henry Salvatori Computer Science Center (SAL) -
Audiences: Everyone Is Invited
Contact: Shahram Ghandeharizadeh
Event Link: https://www.holodecks.quest/
