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SUNMONTUEWEDTHUFRISAT
Events for the 2nd week of December
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VLP Festive Finals Study Slam Day 1
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Mon, Dec 04, 2023 @ 10:00 AM - 05:00 PM
Viterbi School of Engineering Student Affairs
Student Activity
Do finals have you feeling frosty? Get cozy at the VLP during our Festive Finals Study Slam! Warm up in our public or private study spaces, eat tasty festive treats, and grab some giveaways that will help you deck the halls with great grades!
Location: Ronald Tutor Hall of Engineering (RTH) - 222
Audiences: Everyone Is Invited
Contact: Alex Bronz
Event Link: https://cglink.me/2nB/r393527
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DEI: Fall 2023 Town Hall (AME Dept.)
Mon, Dec 04, 2023 @ 11:30 AM - 01:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Multiple panelists, Multiple
Talk Title: AME Town Hall
Abstract: Take a break from studying and join us for lunch! AME is hosting its second Town Hall, featuring Melissa Orme (Boeing Additive Manufacturing), Kenneth Bonner (Viterbi Associate Dean for Inclusion and Diversity Initiatives), Laura Crabtree (CEO of Epsilon3), Leslie King (Part-time Lecturer of AME) and Alejandra Uranga (WISE Gabilan Assistant Professor and Assistant Professor of AME). Our panelists - representing industry, higher education administration, and academia - will discuss diversity, equity, and inclusion in the field of aerospace and mechanical engineering. This is a community approach to advocacy, which will facilitate open communication between students, staff, and faculty so that barriers to DEI can be identified and overcome.
ACCESSIBILITY: We encourage everyone to participate in the programs and activities. If you anticipate needing any type of accommodation, have questions about the physical access, and/or require materials in an alternate format, please contact Victoria Sevilla (vasevill@usc.edu).
Host: AME Department
More Info: https://forms.gle/HSVHHxa88VvzfTVb6
More Information: Fall 2023 Town Hall flier JPEG.jpg
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://forms.gle/HSVHHxa88VvzfTVb6
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VLP Festive Finals Study Slam Day 2
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Tue, Dec 05, 2023 @ 10:00 AM - 05:00 PM
Viterbi School of Engineering Student Affairs
Student Activity
Do finals have you feeling frosty? Get cozy at the VLP during our Festive Finals Study Slam! Warm up in our public or private study spaces, eat tasty festive treats, and grab some giveaways that will help you deck the halls with great grades!
Location: Ronald Tutor Hall of Engineering (RTH) - 222
Audiences: Everyone Is Invited
Contact: Alex Bronz
Event Link: https://cglink.me/2nB/r393530
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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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