Artificial intelligence is poised to play an increasingly large role in societies across the world. Accordingly, there is a growing interest in ensuring that AI is used in a responsible and beneficial manner. A range of perspectives and contributions are needed, spanning the full spectrum from fundamental research to sustained deployments.
This seminar series will explore how artificial intelligence can contribute to solving social problems. For example, what role can AI play in promoting health, access to opportunity, and sustainable development? How can AI initiatives be deployed in an ethical, inclusive, and accountable manner?
Scientific Natural Language Processing and the Fight Against COVID-19
Abstract: This talk will describe the dramatic creation of the COVID-19 Open Research Dataset (CORD-19) and the broad range of efforts, both inside and outside of the Semantic Scholar project, to garner insights into COVID-19 and its treatment based on this growing corpus of research papers.
Stevie Chancellor (Northwestern University)
I build and critically examine human-centered algorithms for high-risk, dangerous health behaviors in online communities. I use digital trace data from millions of interactions on social media to understand and identify high-risk behaviors with machine learning and computational linguistics, combined with interdisciplinary insights from clinical psychology critical data studies.
Along the way, I explore how to conduct human-centered machine learning, an approach that deliberately refocuses technological design and implementation on the needs of humans, communities, and stakeholders. This includes tensions around rigor and robustness, construct validity, platform governance, and ethical issues within this research agenda. I deeply care about doing right by people and communities, and (recently) have been thinking about how to develop more ethical and compassionate research practices in data-driven approaches.
My domain of interest for these questions is online communities and mental health, and behaviors like pro-eating disorder, opioid addiction, and suicidal ideation.
Eric Rice (Northwestern University)
Eric Rice is an associate professor and the founding co-director of the USC Center for Artificial Intelligence in Society, a joint venture of the USC Suzanne Dworak-Peck School of Social Work and the USC Viterbi School of Engineering. Rice received a BA from the University of Chicago, and an MA and PhD in Sociology from Stanford University. He was a postdoctoral fellow at the University of California, Los Angeles. He joined the USC faculty in 2009. In 2012 he received the John B. Reid Early Career Award through the Society for Prevention Research. He specializes in social network science and theory, as well as community-based research. His primary focus is on youth experiencing homelessness and how issues of social network influence may affect risk-taking behaviors and resilience. For several years he has been working with colleague Milind Tambe to merge social work science and AI, seeking novel solutions to major social problems such as homelessness and HIV. Rice is the author of more than 100 peer-reviewed articles in such publications as the American Journal of Public Health, AIDS and Behavior, the Journal of Adolescent Health, Pediatrics, Child Development, and the Journal of the Society for Social Work Research. He is the recipient of grants from the National Institute of Mental Health, the California HIV/AIDS Research Program, the Army Research Office and other agencies.
Oren Etzioni (Allen Institute for AI)
Dr. Oren Etzioni is Chief Executive Officer at AI2. He has been a Professor at the University of Washington’s Computer Science department since 1991. His awards include Seattle’s Geek of the Year (2013), and he has founded or co-founded several companies, including Farecast (acquired by Microsoft). He has written over 100 technical papers, as well as commentary on AI for The New York Times, Wired, and Nature. He helped to pioneer meta-search, online comparison shopping, machine reading, and Open Information Extraction.
Stefano Ermon (Stanford University)
I am an Assistant Professor in the Department of Computer Science at Stanford University, where I am affiliated with the Artificial Intelligence Laboratory and a fellow of the Woods Institute for the Environment.
My research is centered on techniques for scalable and accurate inference in graphical models, statistical modeling of data, large-scale combinatorial optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging field of computational sustainability.
Elaine Nsoesie (Boston University)
Dr. Nsoesie applies data science methodologies to global health problems, using digital data and technology to improve health, particularly in the realm of surveillance of chronic and infectious diseases. She has also been appointed as a BU Data Science Faculty Fellow, as part of the BU Data Science Initiative at the Hariri Institute for Computing. The Data Science Faculty Fellows program assembles a cluster of uniquely talented faculty whose expertise transcends traditional disciplinary boundaries to enable fundamental advances in data science. Dr. Nsoesie completed her PhD in Computational Epidemiology from the Genetics, Bioinformatics and Computational Biology program at Virginia Tech, and her PhD dissertation, Sensitivity Analysis and Forecasting in Network Epidemiology Models, at the Network Dynamics and Simulations Science Lab at Virginia Tech BioComplexity Institute. After postdoctoral associate positions at Harvard Medical School and Boston Children’s Hospital, Dr. Nsoesie joined the faculty of the Institute for Health Metrics and Evaluation (IHME) at the University of Washington.
Madhav Marathe (University of Virginia)
Madhav Marathe is an endowed Distinguished Professor in Biocomplexity, Director of the Network Systems Science and Advanced Computing (NSSAC) Division, Biocomplexity Institute and Initiative, and a tenured Professor of Computer Science at the University of Virginia. Dr. Marathe is a passionate advocate and practitioner of transdisciplinary team science. During his 25-year professional career, he has established and led a number of large transdisciplinary projects and groups. His areas of expertise are network science, artificial intelligence, high performance computing, computational epidemiology, biological and socially coupled systems, and data analytics.
Maimuna Majumder (Boston Children’s Hospital and Harvard Medical School)
Dr. Maimuna (Maia) Majumder is a computational epidemiologist specializing in emerging epidemics and a recent graduate of the Engineering Systems program at MIT’s Institute for Data, Systems, and Society (IDSS). In between her graduate studies and her current position at CHIP, Maia spent a year at the Health Policy Data Science lab at Harvard Medical School’s Health Care Policy department as a postdoctoral fellow. During her masters and doctoral studies at MIT, she was funded through a graduate fellowship at HealthMap. Prior to Maia’s arrival at MIT, she earned a Bachelors of Science in Engineering Science (with a concentration in Civil and Environmental Engineering) and a Masters of Public Health in Epidemiology and Biostatistics at Tufts University. While at Tufts, Maia was a field researcher with the International Centre for Diarrheal Disease Research, Bangladesh (ICDDR,B), where she worked with clinic patients (and their data) to learn how to better tell their stories. Her current research interests involve probabilistic modeling, artificial intelligence, and “systems epidemiology” in the context of public health, with a focus on causal inference for infectious disease surveillance using digital disease data (e.g. search trends; news and social media). She also enjoys exploring novel techniques for data procurement, writing about data for the general public, and creating meaningful data visualizations. As of January 2019, Maia has been engaged in pandemic response efforts and is a leading expert in COVID-19 epidemiology.
Sera Linardi (University of Pittsburgh)
Sera Linardi is an Associate Professor at the Graduate School of Public and International Affairs (GSPIA) at the University of Pittsburgh, where she directs the PittSmartLiving Human Behavior Laboratory. She received her Ph.D. in Social Science at the California Institute of Technology after working as a computer scientist at Adobe Systems. She bridges academic research and practical challenges in public/social services provision, specifically around prosocial behavior, information aggregation, and behavior economics of the poor. Her research has been published in economics, management, and political science journals (Journal of Public Economics, Management Science, Games and Economic Behavior, British Journal of Political Science) and won the 2016 Midwest Political Science Association Best Paper in Comparative Politics Award. Her work is currently supported by the NSF and the Heinz Endowment.