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SUNMONTUEWEDTHUFRISAT
Events for the 4th week of November
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PhD Defense- Ninareh Mehrabi
Mon, Nov 21, 2022 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Ninareh Mehrabi
Date: Monday, November 21st, 2022
Time: 10:00 am - noon PT
Zoom Meeting ID: 986 7933 6430
Passcode: 813783
Or via URL: https://usc.zoom.us/j/98679336430?pwd=akpBV05CQ3o5VVlwWnpxT2piVlB3QT09
Title: Responsible Artificial Intelligence for a Complex World
Abstract: With the advancement of Artificial Intelligence (AI) and its omnipresent role in different applications, it is crucial to ensure that AI systems comply with responsible practices. Moreover, the environment in which AI systems learn and interact with contains various external factors that might adversely affect their behavior. Thus, those systems should be able to mitigate potentially negative impacts of such factors. This dissertation explores several important dimensions that are essential for designing responsible AI systems. First, we focus on fairness as a central concept for responsible AI systems and analyze existing biases in various data sources and models. Moreover, we describe a framework based on interpretability for generating fair and equitable outcomes. Second, we discuss robustness to external perturbations as another important property for such systems. Next, we discuss human-centered AI systems which take natural language prompts as input, demonstrate possible issues due to ambiguous interpretation of those prompts, and describe a framework for resolving such ambiguities and generating faithful outcomes to human intention. Finally, we discuss ideas for designing AI systems that can internalize ethics and form a realization about the consequences of tasks and design choices associated with them. We hope that the contributions presented in this dissertation will move us closer to having more responsible AI systems.
WebCast Link: https://usc.zoom.us/j/98679336430?pwd=akpBV05CQ3o5VVlwWnpxT2piVlB3QT09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Tu Do
Mon, Nov 21, 2022 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Tu Do
Title: Optimizing Execution of In situ Workflows
Committee: Ewa Deelman (Chair), Aiichiro Nakano, Viktor Prasanna, Michela Taufer
Abstract:
Advances in high-performance computing (HPC) allow scientific simulations to run at an ever-increasing scale, generating a large amount of data that needs to be analyzed over time. Conventionally, the simulation outputs the entire simulated data set to the file system for later post-processing. Unfortunately, the slow growth of I/O technologies compared to the computing capability of present-day processors causes an I/O bottleneck of post-processing as saving data to storage is not as fast as data is generated. According to data-centric models, a new processing paradigm has recently emerged, called in situ, where simulation data is analyzed on-the-fly to reduce the expensive I/O cost of saving massive data for post-processing. Since an in situ workflow usually consists of co-located tasks running concurrently on the same resources in an iterative manner, the execution yields complicated behaviors that create challenges in evaluating the efficiency of an in situ run. To enable efficient execution of in situ workflows, this dissertation proposes a framework to enable in situ execution between simulations and analyses and introduces a computational efficiency model to characterize efficiency of an in situ execution. By extending the proposed performance model to resource-aware performance indicators, we introduce a method to assess resource usage, resource allocation, and resource provisioning for in situ workflow ensembles. Finally, we discuss the ideas of designing effective scheduling of a workflow ensemble through determining appropriate co-scheduling strategies and resource assignment for each simulation and analysis in the ensemble.
WebCast Link: https://usc.zoom.us/j/94496448526
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Tue, Nov 22, 2022 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mihaela van der Schaar, University of Cambridge
Talk Title: AI for Science: Discovering Diverse Classes of Equations in Medicine and Beyond
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Artificial Intelligence (AI) offers the promise of revolutionizing the way scientific discoveries are made and significantly accelerating their pace. This is important for numerous fields of study, including medicine. In this talk, I will present our research on AI for science over the past few years. I will start by briefly showing how we can discover closed-form prediction functions from cross-sectional data using symbolic metamodels. Then, I will introduce a new method, called D-CODE, which discovers closed-form ordinary differential equations (ODEs) from observed trajectories (longitudinal data).This method can only describe observable variables, yet many important variables in medical settings are often not observable. Hence, I will subsequently present the latent hybridisation model (LHM) that integrates a system of ODEs with machine-learned neural ODEs to fully describe the dynamics of the complex systems. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities. To solve these challenges, I will then present Neural Laplace, with which we can learn diverse classes of differential equations in the Laplace domain. I will conclude by presenting next research frontiers, including recent work on discovering partial differential questions from data (D-CIPHER). While these works are applicable in numerous scientific domains, in this talk I will illustrate the various works with examples from medicine, ranging from understanding cancer evolution to treating Covid-19. This work is joint work with Zhaozhi Qian, Krzysztof Kacprzyk and Sam Holt.
Biography: Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).
Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.
Mihaela is personally credited as inventor on 35 USA patents (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.
Host: Urbashi Mitra and Pierluigi Nuzzo
Webcast: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3QLocation: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3Q
Audiences: Everyone Is Invited
Contact: Talyia White
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***NO EPSTEIN INSTITUTE - ISE 651 SEMINAR (THANKSGIVING BREAK)***
Tue, Nov 22, 2022 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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PhD Thesis Proposal - Yunhao(Andy) Ge
Wed, Nov 23, 2022 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD. candidate: Yunhao(Andy) Ge
Title: Towards trustworthy, effortless, and human-like AI in computer vision
Time: Nov. 23, Wednesday 10:00AM-12:00AM (PST)
Location: Room B15 (basement), Hedco Neurosciences Building, 3641 Watt Way, Los Angeles, CA 90089, USA.
Abstract:
Artificial Intelligence (AI) has achieved great success in various domains, such as self-driving, medical diagnosis, and mobile robotics. Model and Data, two foundations of the current AI system, play significant roles in ensuring the success of AI. However, there are still challenges that remain to be addressed:
On the model side: how to make AI models be trustworthy and reliable? How to empower AI models with the learning and reasoning ability of the human brain? (1) Lack of trustworthiness is a big challenge: The bad transparency of model decisions hinders the understanding of errors and prevents saving more lives.
(2) Filling the gap between the human brain and AI models is challenging. "How do we humans get so much (ability) from so little (supervision)?" How can we build more powerful learning machines based on the same principles as the human brain?
On the data side: How can we minimize the human effort in labeling data and learn from increasingly weak forms of supervision? How to use synthetic data to substitute real-world data to avoid privacy and scalability issues?
To conquer the above mentioned challenges, my research focuses on three different but highly connected and mutually supported dimensions: 1) Human-centric and trustworthy AI: Understand the Human-centric properties of AI models. Such as Causal Explainability, Robustness, Steerability, and Domain Adaptation. 2) Humanoid AI: Simulate human cognitive learning ability. Such as Imagination, Visual Reasoning, and Multi-modal learning (CLIP). 3) Data-centric (human-effortless) AI: Use synthetic data and neural renderer (NeRF, DALL-E, GAN, VAE) to solve real-world computer vision problems (classification, detection, segmentation) with minimal supervision.
Committee members: Laurent Itti (Chair), Ram Nevatia, Greg Ver Steeg, Yan Liu, Nicolas Schweighofer.
Zoom link (hybrid):
Join Zoom Meeting
https://urldefense.com/v3/__https://usc.zoom.us/j/2226620525__;!!LIr3w8kk_Xxm!7LMAWz4bNVcqh3rTNdNUzTTvIPvcuauvaTgibRKRuQQ3EFj0WhFfn6m-Ovz35rpK$
Meeting ID: 222 662 0525
Location: Hedco Pertroleum and Chemical Engineering Building (HED) - B15
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
