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Conferences, Lectures, & Seminars
Events for May
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Wed, May 01, 2024 @ 09:45 AM - 10:45 AM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Jeff Saucerman, Ph.D., Professor of Biomedical Engineering and Cardiovascular Medicine Vivian Pinn Scholar, School of Medicine University of Virginia
Talk Title: Fusing mechanistic networks and machine learning to understand inflammation-fibrosis coupling
Abstract: Inflammation and fibrosis are conserved phases of wound healing in the heart,skin, and other organs. Yet therapeutic attempts at manipulating inflammationand fibrosis have had limited success. In this talk, I will present ourcomputational and experimental systems biology research on cardiacinflammation and fibrosis. These studies include large scale computationalmodels of the intracellular signaling networks of multiple cardiac cell types,experimental drug screens, and new methods that fuse mechanistic andmachine-learning approaches to understand how these drugs work. Ourcomputational models are validated with new experiments in cells and mice.
Biography: Dr. Jeff Saucerman is a Professor of Biomedical Engineering and Professor ofCardiovascular Medicine at the University of Virginia. He leads a research group in cardiacsystems biology, focused on identifying and controlling the molecular networks involved inheart disease. He received a B.S. in Engineering Science from Pennsylvania StateUniversity, Ph.D. in Bioengineering from the University of California San Diego, andcompleted a postdoctoral fellowship with Dr. Donald Bers at Loyola University Chicago. Dr.Saucerman has received a number of awards including an NSF CAREER Award, Fellow ofthe American Heart Association and American Institute of Medical and BiologicalEngineering, the Dean’s Excellence in Teaching Award, BME Mentoring Award, and theVivian Pinn Scholar Award.
Host: Stacey Finley
Location: 101
Audiences: Everyone Is Invited
Contact: Carla Stanard
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Wed, May 01, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Paula Cannon, Ph.D. , Distinguished Professor of Molecular Microbiology and Immunology in the Keck School of Medicine of USC
Talk Title: Move over CAR T cells -“ engineering B cells to express custom molecules
Abstract: We use CRISPR/Cas9 gene editing to reprogram B cells to express custom antibodies and antibody-like molecules. These include broadly neutralizing antibodies that can control HIV, but which are not made in response to candidate HIV vaccines. To do this, we developed a simplified gene editing protocol that inserts custom antigen-recognizing domains into constant regions of the immunoglobulin locus, resulting in molecules that mimic the heavy chain only antibodies found in Camelids. This approach preserves the important features of natural antibody expression, allowing engineered B cells to respond to matched antigens and differentiate into antibody-secreting cells. I will present our data evaluating this approach in ex vivo human tonsil organoids and in non-human primates, and describe the flexibility and potential applications of this new type of immune cell therapy.
Biography: Paula Cannon, PhD, is a Distinguished Professor of Molecular Microbiology and Immunology in the Keck School of Medicine of USC. She obtained her PhD in bacterial gene transfer from the University of Liverpool in the UK and did postdoctoral work on HIV and gene therapy at both Harvard and Oxford Universities. Dr. Cannon uses gene editing technologies such as CRISPR/Cas9 to manipulate immune cells, with the goal of developing cell therapy treatments for HIV, cancer and other chronic diseases. Most recently, her group has been editing B cells to express completely customized molecules, such as antibodies that can neutralize multiple different strains of HIV. Such a platform could turn B cells into factories in the body to secrete antibodies with desirable properties, including those that are not easily generated by vaccination. Dr. Cannon is well known as a gene therapist and will become the president of the American Society for Gene and Cell Therapy in 2024.
Host: Peter Wang
Location: Corwin D. Denney Research Center (DRB) - 146
Audiences: Everyone Is Invited
Contact: Carla Stanard
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Computational Science Distinguished Seminar Series
Thu, May 02, 2024 @ 09:30 AM - 10:30 AM
USC School of Advanced Computing
Conferences, Lectures, & Seminars
Speaker: Yannis Kevrekidis, Johns Hopkins University
Talk Title: No Equations, No Variables, No Space and No Time: Data and the Modeling of Complex Systems
Abstract: I will give an overview of a research path in data driven modeling of complex systems over the last 30 or so years – from the early days of shallow neural networks and autoencoders for nonlinear dynamical system identification, to the more recent derivation of data driven “emergent” spaces in which to better learn generative PDE laws. In all illustrations presented, I will try to point out connections between the “traditional” numerical analysis we know and love, and the more modern data-driven tools and techniques we now have – and some mathematical questions they hopefully make possible for us to answer.
Biography: Yannis Kevrekidis studied Chemical Engineering at the National Technical University in Athens. He then followed the steps of many alumni of that department to the University of Minnesota, where he studied with Rutherford Aris and Lanny Schmidt (as well as Don Aronson and Dick McGehee in Math). He was a Director's Fellow at the Center for Nonlinear Studies in Los Alamos in 1985-86 (when Soviets still existed and research funds were plentiful). He then had the good fortune of joining the faculty at Princeton, where he taught Chemical Engineering and also Applied and Computational Mathematics for 31 years; seven years ago he became Emeritus and started fresh at Johns Hopkins (where he somehow is also Professor of Urology). His work always had to do with nonlinear dynamics (from instabilities and bifurcation algorithms to spatiotemporal patterns to data science in the 90s, nonlinear identification, multiscale modeling, and back to data science/ML); and he had the additional good fortune to work with several truly talented experimentalists, like G. Ertl's group in Berlin. Currently -on leave from Hopkins- he works with the Defense Sciences Office at DARPA. When young and promising he was a Packard Fellow, a Presidential Young Investigator and the Ulam Scholar at Los Alamos National Laboratory. He holds the Colburn, CAST Wilhelm and Walker awards of the AIChE, the Crawford and the Reid prizes of SIAM, he is a member of the NAE, the American Academy of Arts and Sciences, and the Academy of Athens.
Host: The School of Advanced Computing
More Info: https://sac.usc.edu/events/?hash=1m3nCA4M337
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://sac.usc.edu/events/?hash=1m3nCA4M337
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Machine Learning Center Seminar
Thu, May 02, 2024 @ 12:00 PM - 01:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Pengtao Xie , Assistant Professor, Department of Electrical and Computer Engineering - University of California, San Diego
Talk Title: Foundation Models and Generative AI for Medical Imaging Segmentation in Ultra-Low Data Regimes
Abstract: Semantic segmentation of medical images is pivotal in disease diagnosis and treatment planning. While deep learning has excelled in automating this task, a major hurdle is the need for numerous annotated masks, which are resource-intensive to produce due to the required expertise and time. This scenario often leads to ultra-low data regimes where annotated images are scarce, challenging the generalization of deep learning models on test images. To address this, we introduce two complementary approaches. One involves developing foundation models. The other involves generating high-fidelity training data consisting of paired segmentation masks and medical images. In the former, our bi-level optimization based method can effectively adapt the general-domain Segment Anything Model (SAM) to the medical domain with just a few medical images. In the latter, our multi-level optimization based method can perform end-to-end generation of high-quality training data from a minimal number of real images. On eight segmentation tasks involving various diseases, organs, and imaging modalities, our methods demonstrate strong generalization performance in both in-domain and out-of-domain settings. Our methods require 8-12 times less training data than baselines to achieve comparable performance.
Biography: Pengtao Xie is an assistant professor in the Department of Electrical and Computer Engineering at the University of California San Diego. His research interest lies in machine learning for healthcare. His PhD thesis was selected as a top-5 finalist for the Doctoral Dissertation Award of the American Medical Informatics Association (AMIA). He was recognized as Global Top-100 Chinese Young Scholars in Artificial Intelligence by Baidu, Tencent AI-Lab Faculty Award, Innovator Award by the Pittsburgh Business Times, Amazon AWS Machine Learning Research Award, among others. He serves as an associate editor for the ACM Transactions on Computing for Healthcare, senior area chair for AAAI, area chairs for ICML and NeurIPS, etc.
Host: 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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Semiconductors & Microelectronics Technology Seminar - Ke Du, Thursday, May 2nd at 2pm in EEB 248
Thu, May 02, 2024 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ke Du, UC Riverside
Talk Title: Micro- and Nanofluidic Systems for Molecular Biosensing, Nanotoxicity, and Optogenetics
Series: Semiconductors & Microelectronics Technology
Abstract: Micro- and nanofluidic systems, in conjunction with biochemistry, microscopy, nanomaterials, and machine learning components, serve as potent tools with a wide array of applications in the biomedical field. These applications encompass crucial areas like molecular diagnosis, biophysics, and optogenetics. In this presentation, we shed light on an innovative pneumatic-controlled nano-sieve device. This device is packed with magnetic beads and facilitates the rapid concentration of drug-resistant bacteria from blood samples. Subsequently, an isothermal amplification and CRISPR assay are conducted. This system achieves an on-chip concentration factor of 20x, effectively pushing the bacterial detection threshold to 100 cfu/mL. To make sensing automatic and devoid of the need for specialized instruments, a computer vision program is developed. This program exhibits an approximate accuracy rate of 100% in discerning both positive and negative samples within the microfluidic chip. This attribute renders it particularly suitable for on-site detection in resource-limited environments. Furthermore, we delve into our recent strides in comprehending the interactions between nanomaterials and eukaryotic organisms. This understanding is facilitated by a deformable microfluidic platform, advanced microscopy, and molecular dynamic simulations. Within this context, we explore a range of clinical applications. These applications span from in vivo bioimaging employing optofluidics to addressing dentine hypersensitivity and advancing the realm of synthetic biology.
Biography: Dr. Ke Du is an assistant professor of chemical and environmental engineering at UC-Riverside and leads the Nanobiosensing, Nanomanufacturing, and Nanomaterials (3N) Lab. He received his Ph.D. degree at Stevens Institute of Technology in 2015. Following post- doctoral training at UC-Berkeley with Richard A. Mathies, he started his independent career at the Rochester Institute of Technology in 2018. In 2022, Du's lab moved back to California and joined UC-Riverside. Du's research interests include in vitro molecular diagnostics, in vivo bioimaging, nanotoxicity, and nanomanufacturing. He is recipient of numerous awards and honors such as the EIPBN Best Journal Paper Award (2022), the NIH Maximizing Investigators' Research Award (2021), the Burroughs Wellcome Fund (BWF) Collaborative Travel Grant (2019), the James H. Potter Award for the outstanding Ph.D. students (2014), and the NSF Graduate Student Fellowship (2012). He has been recognized as a global rising starin sensing by ACS Sensors and a finalist for the MINE 2020 Young Scientists Award. Du's research has been supported by NIGMS, NIAID, NSF, USDA, DOE, BWF, the UNYTE Translational Research Network, and industry partners such as L3Harris, Mammoth Biosciences, Colgate Palmolive, and Biological Mimetics. Additionaly, he serves as an early career editorial advisory member for Biomicrofluidics (AIP Publishing) and Sensors and Actuators Reports (Elsevier).
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu
More Information: Ke Du_2024-05-02.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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AI Seminar- Understanding LLMs through their Generative Behavior, Successes and Shortcomings
Fri, May 03, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Swabha Swayamdipta, USC
Talk Title: Understanding LLMs through their Generative Behavior, Successes and Shortcomings
Series: AI Seminar
Abstract: Abstract: Generative capabilities of large language models have grown beyond the wildest imagination of the broader AI research community, leading many to speculate whether these successes may be attributed to the training data or model design. I will present some work from my group which sheds light on understanding LLMs by studying their generative behavior, successes and shortcomings. First, I will show that standard inference algorithms work well because of the particular design behind LLMs. Next, I will discuss recently found successes and failures of LLMs on a combination of tasks, requiring world and domain-specific knowledge, linguistic capabilities and awareness of human and social utility. Overall, these findings paint a partial yet complex picture of our understanding of LLMs and provide a guide to the next steps forward.
This event will be recorded.
It will be posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI.
Biography: Swabha Swayamdipta is an Assistant Professor of Computer Science and a Gabilan Assistant Professor at the University of Southern California. Her research interests are in natural language processing and machine learning, with a primary interest in the estimation of dataset quality, understanding and evaluation of generative models of language, and using language technologies to understand social behavior. At USC, Swabha leads the Data, Interpretability, Language and Learning (DILL) Lab. She received her PhD from Carnegie Mellon University, followed by a postdoc at the Allen Institute for AI. Her work has received outstanding paper awards at ICML 2022, NeurIPS 2021 and an honorable mention for the best paper at ACL 2020. Her research is supported by awards from the Allen Institute for AI and Intel Labs.
Host: Jay Pujara and Karen Lake
More Info: https://www.isi.edu/events/4684/ai-seminar-understanding-llms-through-their-generative-behavior-successes-and-shortcomings/
Webcast: https://usc.zoom.us/j/95888595423?pwd=VHBLa041dUJWcWx0NEhuYmQrV29ZQT09Location: Information Science Institute (ISI) - Conf Rm#1135-37
WebCast Link: https://usc.zoom.us/j/95888595423?pwd=VHBLa041dUJWcWx0NEhuYmQrV29ZQT09
Audiences: Everyone Is Invited
Contact: Pete Zamar
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Fri, May 03, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Kate Havens, Ph.D., Associate Professor of Clinical Physical Therapy Division of Biokinesiology & Physical Therapy, USC
Talk Title: Oh Baby! Integrating Anatomy, Biomechanics, and Engineering to Address Postpartum Pain
Abstract: In this presentation, Dr. Havens will introduce the biomechanics underlying pelvic girdle pain and dysfunction in postpartum mothers, integrating musculoskeletal anatomical, orthopedic biomechanical, and engineering principles. She will delve into the unique adaptations during pregnancy and postpartum, focusing on posture, gait, and balance activities, alongside an exploration of the anatomy of the region. This knowledge informs innovative engineering solutions for mitigating perinatal biomechanical challenges, particularly the unique demands of infant caregiving tasks.
Biography: Dr. Kate Havens is an Associate Professor in the Division of Biokinesiology and Physical Therapy and specializes in biomechanics and anatomical sciences. Her research interest is perinatal health. She studies biopsychosocial aspects of new motherhood and focuses her laboratory work on biomechanics underlying lumbopelvic pain and dysfunction in postpartum mothers.
Host: Megan McCain
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Carla Stanard
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Tue, May 07, 2024 @ 10:45 AM - 11:45 AM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Rong Li, Professor of Mechanobiology Institute, National University of Singapore Department of Cell Biology and Department of Chemical and Biomolecular Engineering, Johns Hopkins University School of Medicine
Talk Title: Mechanics and stress in cellular development, adaptation, and aging
Abstract: Mechanical processes are central to diverse cellular functions but can also be sources of cellular stress leading to aging phenotypes. My lab currently investigates three problems related to cell mechanics and stress: 1) how intracellular fluid dynamics coupled with cytoskeletal forces drive early mammalian development and reproductive aging; 2) how stress-induced protein aggregation and subsequent disaggregation are orchestrated by and affect organelles such as mitochondria and ER; and 3) the interplay between biophysical stress and chromosome instability and its contribution to cellular adaptation and cancer evolution. I will present a combination of recent findings in the first two areas of our research.
Biography: Professor Rong Li came from Johns Hopkins University where she served as the Director of the Centre for Cell Dynamics in the Johns Hopkins School of Medicine. She was recruited to NUS in 2019 as the second Director of Mechanobiology Institute (MBI). Professor Li is a globally respected leader in the study of cellular dynamics and mechanics. Her interdisciplinary research integrates genetics, quantitative imaging, biophysical measurements, mathematical modelling, genomics and proteomics — to understand how eukaryotic cells transmit their genomes, adapt to the environment, and establish distinct organisation to perform specialised functions. The diverse projects in Professor Rong Li’s lab contribute to two main research thrusts: cell and tissue aging; cellular and organismal adaptation. The study on aging focuses on understanding dynamic changes of crucial cellular components during the aging process and how these changes alter the mechanical functions of cells and tissues. The insights gained will be applied to the development of new methods for prolonging healthy aging and the repair and regeneration of deteriorated functions. The study of adaptation aims to understand the dynamics of genetic and epigenetic determinants of cells and tissues under acute or chronic stress which lead to adaptive behaviors ultimately beneficial or detrimental to the fitness of the organism. A potential application of the discoveries in this area is the prevention of cancer associated with chronic inflammatory diseases.
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Carla Stanard
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NL Seminar-Event Extraction for Epidemic Prediction
Thu, May 09, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Tanmay Parekh, UCLA
Talk Title: Event Extraction for Epidemic Prediction
Abstract: *Meeting hosts only admit on-line guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom. If you’re an outside visitor, please inform us at (nlg-seminar-host(at)isi.edu) to make us aware of your attendance so we can admit you. Specify if you will attend remotely or in person at least one business day prior to the event Provide your: full name, job title and professional affiliation and arrive at least 10 minutes before the seminar begins. If you do not have access to the 6th Floor for in-person attendance, please check in at the 10th floor main reception desk to register as a visitor and someone will escort you to the conference room location.
Tanmay Parekh is a third-year PhD student in Computer Science at the University of California Los Angeles (UCLA). He is advised by Prof. Nanyun Peng and Prof. Kai-Wei Chang. Previously, he completed his Masters at the Language Technologies Institute at Carnegie Mellon University (CMU) where he worked with Prof. Alan Black and Prof. Graham Neubig. He has completed his undergraduate studies at the Indian Institute of Technology Bombay (IITB). He has also worked in the industry at Amazon and Microsoft. He has worked on a wide range of research topics in multilingual, code-switching, controlled generation, and speech technologies. His current research focuses on improving the utilization and generalizability of Large Language Models (LLMs) for applications in Information Extraction (specifically Event Extraction) across various languages and domains.
Biography: Early warnings and effective control measures are among the most important tools for policymakers to be prepared against the threat of any epidemic. Social media is an important information source here, as it is more timely than other alternatives like news and public health and is publicly accessible. Given the sheer volume of daily social media posts, there is a need for an automated system to monitor social media to provide early and effective epidemic prediction. To this end, I introduce two works to aid the creation of such an automated system using information extraction. In my first work, we pioneer exploiting Event Detection (ED) for better preparedness and early warnings of any upcoming epidemic by developing a framework to extract and analyze epidemic-related events from social media posts. We curate an epidemic event ontology comprising seven disease-agnostic event types and construct a Twitter dataset SPEED focused on the COVID-19 pandemic. Experimentation reveals how ED models trained on COVID-based SPEED can effectively detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue. Furthermore, we show that reporting sharp increases in the extracted events by our framework can provide warnings 4-9 weeks earlier than the WHO epidemic declaration for Monkeypox.
Since epidemics can originate across the globe, social media posts discussing them can be in varied languages. However, training supervised models on every language is a tedious and resource-expensive task. The alternative is the usage of zero-shot cross-lingual models. In this work, we introduce a new approach for label projection that can be used to generate synthetic training data in any language using the translate-train paradigm. This novel approach, CLaP, translates text to the target language and performs contextual translation on the labels using the translated text as the context, ensuring better accuracy for the translated labels. We leverage instruction-tuned language models with multilingual capabilities as our contextual translator, imposing the constraint of the presence of translated labels in the translated text via instructions. We benchmark CLaP with other label projection techniques on zero-shot cross-lingual transfer across 39 languages on two representative structured prediction tasks — event argument extraction (EAE) and named entity recognition (NER), showing over 2.4 F1 improvement for EAE and 1.4 F1 improvement for NER.
Host: Jon May and Justin Cho
More Info: https://www.isi.edu/research-groups-nlg/nlg-seminars/
Webcast: https://usc.zoom.us/j/93924756526Location: Information Science Institute (ISI) - Conf Rm#689
WebCast Link: https://usc.zoom.us/j/93924756526
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://www.isi.edu/research-groups-nlg/nlg-seminars/
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AI Seminar- Causal Inference to Inform Curation Practices in Online Platforms
Fri, May 10, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Giuseppe Russo, EPFL- Ecole Polytechnique Fédérale de Lausanne
Talk Title: Causal Inference to Inform Curation Practices in Online Platforms
Abstract: Digital platforms like Facebook, Wikipedia, Amazon, and LinkedIn play a foundational role in our society. They engage in content curation through moderation, recommendations, and monetization efforts, impacting individuals positively or negatively. In this talk, I will highlight the critical need for improving the existing methodologies used in these curation practices. I’ll make a case for the essential role of academic research in shaping policy and establishing best practices, drawing on two significant projects from my doctoral research.
First, I will delve into an observational study on Reddit that uncovered a mechanism potentially driving the proliferation of extremist communities online. Following that, I will detail the outcomes of a study assessing the impact of removing entire extremist groups from Reddit. To conclude, I will examine potential research paths aimed at improving digital platforms, with a special focus on both the promises and challenges introduced by the emergence of generative AI technologies. My research demonstrates that investigating the direct effects of content curation practices with rigor can significantly enhance the quality of online platforms.
Biography: I am a Postdoctoral Researcher at EPFL, guided by Professor Robert West. My research spans causal inference, machine learning, and the broader impacts of AI on both society and individuals. Currently, my focus is on understanding the effects of content moderation in online social networks. My research extends to the applying causal methods to decision-making processes related to health and sustainability.
I earned both my PhD and MSc from ETH Zurich, under the mentorship of Professor Frank Schweitzer, and completed my Bachelor's degree at the Politecnico di Milano. My work has been showcased at several academic conferences, including ACL, EMNLP, ICWSM, WWW, and IC2S2. Notably, it has been featured in the enlightening talk series at the International Conference of Computational Social Science (IC2S2).
Host: Fred Mortatter and Pete Zamar
More Info: https://www.isi.edu/events/4871/ai-seminar-causal-inference-to-inform-curation-practices-in-online-platforms/
Webcast: https://usc.zoom.us/webinar/register/WN_wV7p-C8-TF-MIyBOce7N4wLocation: Information Science Institute (ISI) - Virtual Only
WebCast Link: https://usc.zoom.us/webinar/register/WN_wV7p-C8-TF-MIyBOce7N4w
Audiences: Everyone Is Invited
Contact: Pete Zamar
