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Conferences, Lectures, & Seminars
Events for October
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When does human-centered AI (fail to) scale?
Wed, Oct 02, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Angel Hwang (she/her), Assistant Professor, USC Annenberg School for Communication and Journalism
Talk Title: When does human-centered AI (fail to) scale?
Abstract: State-of-the-art AI systems are built and deployed at the societal scale, increasing the need to consider sociotechnical factors for implementing systems of such magnitude. In contrast, individual user experience has long been the core of designing and developing user-friendly technologies. Through a series of experiments and case studies, I examine challenges and breakdowns as one extends individual-centered approaches to design societal-scale AI systems.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Angel Hsing-Chi Hwang (she/her) is an Assistant Professor at USC Annenberg School for Communication and Journalism. Her research explores the societal impact of AI-powered technologies on work practices. In her past and present work, she focuses on how practitioners design, build, and/or apply AI to facilitate group interaction, produce creative content, and balance everyday wellness.
Host: CAIS
More Info: https://cais.usc.edu/events/usc-cais-seminar-with-dr-angel-hwang/
Location: Montgomery Ross Fisher Building (school Of Social Work) (MRF) - 102
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
Event Link: https://cais.usc.edu/events/usc-cais-seminar-with-dr-angel-hwang/
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Identifying Critical Scenarios for Automated Driving Safety Validation
Wed, Oct 09, 2024 @ 10:30 AM - 11:30 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Adam Molin, Denso
Talk Title: Identifying Critical Scenarios for Automated Driving Safety Validation
Abstract: Verification and Validation (V&V) processes play a vital role in ensuring the safety and reliability of automated driving. Scenario-based testing in simulation has emerged as an effective approach for identifying critical scenarios that challenge the capabilities of automated driving systems. This presentation aims to explore the methodology to automatically find unknown critical test cases using specification-guided scenario-based testing. The talk will discuss the limitations of current techniques and how these can be overcome by the usage of generative AI for synthesizing critical scenarios.
This lecture satisfies the requirements for CSCI 591: Research Colloquium.
Host: Prof. Jyo Deshmukh
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
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Modeling Human Motion Behaviors and 3D Environment from Real-World Capture
Tue, Oct 15, 2024 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Andrew Feng , Associate Director - Geospatial Research, USC-ICT
Talk Title: Modeling Human Motion Behaviors and 3D Environment from Real-World Capture
Abstract: Synthesizing believable human motions based on input conditions is an essential task that will find many applications in gaming, simulation, and virtual reality. Various conditional inputs can be utilized to drive the motion synthesis process such as speech, music, action categories, and natural language text descriptions. Generating motions from text prompts or speech audios requires modeling of both languages and motions, which is especially challenging as the model needs to learn a cross-modal mapping to produce motion sequences. Another challenge in learning the motion synthesis model is that the cross-modal mapping may not be deterministic. For instance, there may be multiple viable gesture motions for the same speech utterance that are all plausible. The first part of this talk will cover our research in leveraging discrete latent space learning and recent generative modeling methods to address such challenges. Our proposed method models the motion segments as discrete codes and learns the underlying data distributions for these motion units. Therefore it does not suffer from the over-smoothed or damped animations caused by the deterministic mapping of the regression models in previous methods. Modeling the real world environment from multi-view images remain significant challenges in computer vision and graphics. The resulting models need to retain both accurate visual appearances and geometry to be valuable for digital twins, simulation, or scan-to-BIM applications. 3D Gaussian Splatting (3DGS) has recently advanced the field to be a viable method for novel view synthesis and real-time rendering. The second part of the talk will cover our recent research work in 3DGS for revising the training and densification strategy to improve the radiance field and geometry reconstructions.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Andrew Feng is currently the Associate Director of Geospatial Research at USC-ICT. He leads the Terrain Research group at ICT focusing on geospatial R&D initiatives in support of the Army’s One World Terrain project. Previously, he was a research scientist working on gesture synthesis, character animation and automatic 3D avatar generation. His research work involves applying machine learning techniques to solve computer graphics problems such as 3D model reconstructions, semantic segmentations, and animation synthesis. He received his Ph.D. and M.S. degree in computer science from the University of Illinois at Urbana-Champaign.
Host: Jonathan Gratch, Research Professor
Location: Olin Hall of Engineering (OHE) - 100c
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
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USC CAIS Seminar with Dr. Frederic Reamer
Wed, Oct 16, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Frederic Reamer, Professor Emeritus, School of Social Work - Rhode Island College
Talk Title: USC CAIS Seminar with Dr. Frederic Reamer
Abstract: Artificial intelligence (AI) is becoming increasingly prevalent in the behavioral health professions. AI is being used to conduct client risk assessments; assist people in crisis; strengthen prevention efforts; document clinical services; identify systemic biases in the delivery of services; provide professional education and clinical supervision; and predict practitioner burnout and service outcomes, among other uses.
This webinar will examine cutting-edge ethical issues related to behavioral health practitioners’ use of AI; apply relevant ethical standards; and outline key elements of a strategy for practitioners’ ethical use of AI. Join Dr. Frederic Reamer as he examines ethical issues and risks related to informed consent and client autonomy; privacy and confidentiality; transparency; potential client misdiagnosis; client abandonment; client surveillance; plagiarism, dishonesty, fraud, and misrepresentation; algorithmic bias and unfairness; and use of evidence-based AI tools.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Register for Zoom webinar: https://usc.zoom.us/webinar/register/WN_DC48EaIORMy9ePEE86IGiA
Biography: Frederic G. Reamer has been on the faculty of the School of Social Work, Rhode Island College since 1983. His research and teaching have addressed a wide range of human service issues, including mental health, health care, criminal justice, public welfare, and professional ethics. Dr. Reamer received his Ph.D. (social work) from the University of Chicago. He has served as a social worker in correctional and mental health settings.
He serves as Associate Editor of the National Association of Social Workers Encyclopedia of Social Work (Oxford University Press and National Association of Social Workers). Since 2012, Dr. Reamer has served as the ethics instructor in the Providence (RI) Police Department Training Academy. Dr. Reamer has conducted extensive research on professional ethics. He has published 25 books and more than 190 journal articles, book chapters, and encyclopedia articles.
Dr. Reamer is the recipient of awards such as the NASW Mit Joyner Presidential Award, NASW Social Work Pioneer Award, and NASW Excellence in Ethics Award.
Host: CAIS
More Info: https://cais.usc.edu/events/usc-cais-seminar-with-dr-frederic-reamer/
Webcast: https://usc.zoom.us/webinar/register/WN_DC48EaIORMy9ePEE86IGiALocation: Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_DC48EaIORMy9ePEE86IGiA
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
Event Link: https://cais.usc.edu/events/usc-cais-seminar-with-dr-frederic-reamer/
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Generative Models and the Transport of Measure
Tue, Oct 22, 2024 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Gavin Kerrigan, PhD Candidate - Department of Computer Science, UC Irvine
Talk Title: Generative Models and the Transport of Measure
Abstract: A key theme in contemporary generative modeling is the continuous transport of measure, in which a simple reference distribution is gradually transformed into the data distribution. Many recent models, including diffusions and flows, can be viewed through this unifying lens. In this talk, we will first explore some geometric tools for studying dynamics in the space of probability measures. We will then leverage these tools to design generative models, with a focus on applications to inverse problems and complex data structures such as function-valued data.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
In-person ONLY; recording available post-presentation.
Biography: Gavin Kerrigan is a final year PhD candidate in the Department of Computer Science at UC Irvine, where he is advised by Padhraic Smyth. Prior to joining UCI, he obtained a BSc in mathematics from the Schreyer Honors College at Penn State University. His research focuses on advancing the theory and practice of deep generative models, ranging from fundamental methodology to applications in climate science. His work has been recognized through a best paper award at AISTATS'23 for contributions to function-space generative modeling.
Host: USC Machine Learning Center
Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
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The Algorithmic Abyss: Exploring Autonomy without Robotic Horror
Tue, Oct 22, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Juan Wachs , Professor & University Faculty Scholar, Industrial Engineering School - Purdue University
Talk Title: The Algorithmic Abyss: Exploring Autonomy without Robotic Horror
Abstract: Robots can already solve sophisticated problems ranging from playing games, autonomous driving, and dancing—given enough observational data for training. The core of such success resides in efficient algorithms, compliant hardware and robust computing, all implemented using carefully curated data collected before the training phase. Thus, robots learn in a “sterile” domain, under clean, controlled and to some extent supervised environments. As the target domain changes, however, moving to more quotidian scenarios, robots struggle to perform well. It is hard to think of an autonomous car trained in Silicon Valley being able to successfully navigate the crowded streets of New Delhi. – this is the “algorithm abyss”. Ideally, we would like to robots adapt to challenging settings while immersed in mundane settings, and learn from few observations. To address this hurdle, my work in the area of robotics and autonomous systems focuses on transferring skills and knowledge from controlled settings to the wild. In this talk, I emphasize strategies and techniques to address fundamental challenges in emergent, high-risk, high-stakes scenarios. Specifically, I will discuss work related to telesurgery, skill augmentation and bioinspired designs. While healthcare is one of the research domains discussed, the outcomes and findings are applicable to the range field of autonomous robotics. Progress in these directions will contribute to the public purpose of creating the knowledge for developing robots that are more accessible, effective and sensitive to social needs.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Zoom Details: https://usc.zoom.us/j/99548396089
Biography: Dr. Juan Wachs is a Professor and University Faculty Scholar in the Industrial Engineering School at Purdue University, Professor of Biomedical Engineering (by courtesy), an Adjunct Associate Professor of Surgery at IU School of Medicine, and Adjunct Professor at Johns Hopkins University. He recently served at NSF as a Program Director for Robotics and AI programs at CISE. He is also the director of the Intelligent Systems and Assistive Technologies (ISAT) Lab at Purdue, and he is affiliated with the Regenstrief Center for Healthcare Engineering. He completed postdoctoral training at the Naval Postgraduate School’s MOVES Institute under a National Research Council Fellowship from the National Academies of Sciences. Dr. Wachs received his B.Ed.Tech in Electrical Education in ORT Academic College, at the Hebrew University of Jerusalem campus. His M.Sc and Ph.D in Industrial Engineering and Management from the Ben-Gurion University of the Negev, Israel. He is the recipient of the 2013 Air Force Young Investigator Award, and the 2015 Helmsley Senior Scientist Fellow, and 2016 Fulbright U.S. Scholar, the James A. and Sharon M. Tompkins Rising Star Associate Professor, 2017, and the ACM Distinguished Speaker 2018. Since 2020 he has been elected University Faculty Scholar. He is also the Associate Editor of IEEE Transactions in Human-Machine Systems, Frontiers in Robotics and AI.
Host: Prof. Stefanos Nikolaidis
Webcast: https://usc.zoom.us/j/99548396089Location: Ronald Tutor Hall of Engineering (RTH) - 217
WebCast Link: https://usc.zoom.us/j/99548396089
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
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The Critical Role of Cyber Infrastructure in City Innovation and Beyond
Wed, Oct 23, 2024 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Zhenhui (Jessie) Li , Chief Scientist - Yunqi Academy of Engineering
Talk Title: The Critical Role of Cyber Infrastructure in City Innovation and Beyond
Abstract: Cities, humanity’s greatest inventions, offer vast opportunities for innovation in science and technology. The increasing availability of big data paints a promising future for our cities. Over the past decade, my work has focused on applying AI to address real-world city challenges. Recent collaborations with city practitioners have deepened my understanding of these complexities and refined my vision for achieving city intelligence.
In this talk, I will present my work on advanced AI techniques for city transportation problems, e.g., reinforcement learning for traffic signal control. I will then expand on this to discuss the resource-centric concept of city intelligence, using real-world practices to showcase its practical applications. Finally, I will emphasize the urgent need for new cyber infrastructure, vital not only for city innovations but for all scientific disciplines driven by big data and intensive computing.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
**Lecture will be in-person ONLY
Biography: Dr. Zhenhui (Jessie) Li currently serves as the Chief Scientist at the Yunqi Academy of Engineering, a non-profit institution situated in Hangzhou, China. Prior to this role, she held a tenured associate professor position at Pennsylvania State University. She earned her doctoral degree in Computer Science from the University of Illinois at Urbana-Champaign. Her research primarily focuses on advancing computing technologies to harness data for interdisciplinary studies, including those in smart city, environmental science, transportation, and ecology. For further information, you can visit her website at (https://jessielzh.com/).
Host: Machine Learning Center
Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: Everyone Is Invited
Contact: Machine Learning Center
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Language Models as Temporary Training Wheels to Improve Mental Health
Wed, Oct 30, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Tim Althoff, Assistant Professor, Allen School of Computer Science - University of Washington
Talk Title: Language Models as Temporary Training Wheels to Improve Mental Health
Abstract: Access to mental health care falls short of meeting the significant need. More than one billion individuals are affected by mental health conditions, with the majority not receiving the necessary treatment. In this talk, I will describe how human-AI collaboration, critically enabled by language models, can improve access to and quality of mental health support. Language models have the potential to act as temporary training wheels providing immediate support and guidance to help individuals develop essential mental health skills. This approach emphasizes the importance of using these tools as initial aids rather than long-term crutches. By offering structured assistance, practice, and feedback, language models can help individuals and professionals learn skills, such as cognitive reframing, emotional regulation, and conflict resolution. However, the ultimate goal is for individuals to gradually transition away from dependence on these models, fostering sustained skill development and long-term well-being. This talk will describe how language models can be developed towards these aims and evaluate their effectiveness across multiple randomized trials and real-world deployments with over 150,000 participants.
Learn to challenge unhelpful thinking with your personal AI assistant at https://bit.ly/changing-thoughts
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Register for Zoom webinar here: https://usc.zoom.us/webinar/register/WN_IFvScow2St2noJndL8FucA
Biography: Tim Althoff is an associate professor in the Allen School of Computer Science & Engineering at the University of Washington. Tim’s research seeks to better understand and empower people through data and computation. His AI research has directly improved mental health services utilized by over ten million people and informed federal policy. Tim holds a Ph.D. degree from the Computer Science Department at Stanford University. His work has received various awards including WWW, 2x ICWSM, ACL, UbiComp, and IMIA Best Paper Awards, the SIGKDD Dissertation Award 2019, and an NSF CAREER Award. Tim’s research has been covered internationally by news outlets including BBC, CNN, The Economist, The Wall Street Journal, and The New York Times.
Host: CAIS
More Info: https://cais.usc.edu/events/usc-cais-seminar-with-dr-tim-althoff/
Webcast: https://usc.zoom.us/webinar/register/WN_IFvScow2St2noJndL8FucALocation: Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_IFvScow2St2noJndL8FucA
Audiences: Everyone Is Invited
Contact: Hailey Winetrobe Nadel, MPH, CHES
Event Link: https://cais.usc.edu/events/usc-cais-seminar-with-dr-tim-althoff/
