Events
Past Events
Machine Learning Seminar with Zhi Ding (ECE, UC Davis)
Zhi Ding, Distinguished Professor of Electrical and Computer Engineering at the University of California, Davis, will speak on Non-Blackbox Deep Learning for Massive MIMO Wireless Communication Systems.
Math Colloquium: How mathematical AI is transforming biosciences
Dr. Guowei Wei, Foundation Professor of Mathematics, Electrical and Computer Engineering, and Biochemistry and Molecular Biology at Michigan State University, will speak on "How mathematical AI is transforming biosciences".
Machine Learning Seminar Series with Tan Bui-Thanh
Tan Bui-Thanh, an associate professor and the endowed William J Murray Jr. Fellow in Engineering No. 4, of the Oden Institute for Computational Engineering & Sciences, and the Department of Aerospace Engineering & Engineering Mechanics at the University of Texas at Austin, will speak on Enabling approaches for real-time deployment, calibration, and UQ for digital twins.
Machine Learning Seminar Series with Anna Scaglione (ECE, Cornell)
Anna Scaglione, currently a professor in electrical and computer at Cornell Tech, the New York City campus of Cornell University, will speak on A User Guide to Low-Pass Graph Signal Processing and its Applications.
CS&E Colloquium: Tianfan Fu
This week's CS&E Colloquium speaker, Tianfan Fu (Georgia Institute of Technology), will give a talk entitled, "Deep Learning for Drug Discovery and Development."
Clone of National Academies Digital Twin Workshops - Engineering
A National Academies of Sciences, Engineering, and Medicine-appointed ad hoc committee will identify needs and opportunities to advance the mathematical, statistical, and computational foundations of digital twins in applications across science, medicine, engineering, and society. They have organized a series of workshops including this workshop focusing on engineering.
Using examples from oil and gas engineering, airframe sustainment, manufacturing, and more, workshop speakers will discuss the definition of a digital twin and identify current methods, promising practices, and key technical challenges for their development and use. The workshop will focus on issues related to uncertainty quantification, data assimilation, and data visualization, as well as opportunities for translation of promising practices to and from other fields.
Register for the project's information-gathering workshop on digital twins in the Engineering field.
February 7, 9:00 AM - 1:00 PM (CST) and February 9, 9:00 AM - 1:00 PM (CST)
National Academies Digital Twin Workshops - Atmospheric, Climate, and Sustainability Science
A National Academies of Sciences, Engineering, and Medicine-appointed ad hoc committee will identify needs and opportunities to advance the mathematical, statistical, and computational foundations of digital twins in applications across science, medicine, engineering, and society. They have organized a series of workshops including this workshop focusing on focusing on atmospheric, climate, and sustainability science.
This workshop invites speakers and panelists—including Mike Goodchild (University of California - Santa Barbara), Venkatramani Balaji (Schmidt Futures), Amy McGovern (University of Oklahoma), Anima Anandkumar (California Institute of Technology), and others—to discuss the definition of a digital twin and its applications.
The workshop will focus on key technical challenges for developing and using digital twins, including issues related to ML/AI, big data, and data assimilation. The workshop will also explore opportunities for translation of promising practices to and from other fields.
Register for the project's information-gathering workshop on digital twins in the Atmospheric, Climate, and Sustainability Science field.
February 1, 9:00 AM - 1:30 PM (CST) and February 2, 9:00 AM - 12:00 PM (CST)
National Academies Digital Twin Workshops - Biomedical
A National Academies of Sciences, Engineering, and Medicine-appointed ad hoc committee will identify needs and opportunities to advance the mathematical, statistical, and computational foundations of digital twins in applications across science, medicine, engineering, and society.
Register for the project's information-gathering workshop on digital twins in the Biomedical field (January 30).
For more information, please visit the National Academies website.
Machine Learning Seminar Series with Yifan Peng (PHS, Cornell U.)
Clinical natural language processing and deep learning in assisting medical image analysis
Medical imaging has been a common examination in daily clinical routines for screening and diagnosis of a variety of diseases. Although hospitals have accumulated a large number of image exams and associated reports, it is yet challenging to use them to build high-precision computer-aided diagnosis systems effectively. In this talk, I will present an overview of cutting-edge techniques for mining existing free-text report data to assist medical image analysis via natural language processing and deep learning. Specifically, I will discuss both pattern-based and machine learning-based methods to detect findings/diseases and their attributes (e.g., type, location, size) from the chest x-ray and CT reports. Using these methods, we can construct large-scale medical image datasets with rich information. I will also demonstrate three case studies of medical image analysis using these datasets: (i) common thorax disease detection and report generation from chest X-rays and (ii) lesion detection and segmentation from CT images.
About Dr. Peng
Dr. Peng is an assistant professor at the Department of Population Health Sciences at Weill Cornell Medicine. His main research interests include BioNLP and medical image analysis. Before joining Cornell Medicine, Dr. Peng was a research fellow at the National Center for Biotechnology Information (NCBI), the National Library of Medicine (NLM), National Institutes of Health (NIH). He obtained his Ph.D. degree from the University of Delaware. During his doctoral training, he investigated applications of machine learning in biomedical text-mining, with a focus on deep analysis of the linguistic structures of biomedical texts.
Computer Science Colloquium: On Leaky Models and Unintended Inferences
David Evans (University of Virginia)
Machine learning offers the promise to train models that perform surprisingly well on a wide range of tasks, merely by using massive computing power and generic training algorithms on available data sets. It is an open question, however, what else those models might learn about their training data, and how an adversary with some access to the model may be able to reveal it.
In this talk, I will discuss a variety of inference risks associated with machine-trained models, with a particular focus on surprising (and potentially harmful) things a model may reveal not just about individual training records but about the overall distribution of its training data. This includes attacks an adversary may use to learn statistical properties about the training distribution and about whether certain kinds of data are or are not included, and the potential for an adversary to use a model to make sensitive inferences about individuals, even for attributes not directly related to the task and regardless of whether those individuals are included the training data.
I’ll conclude with some thoughts on why defending against these types of attacks is hard, and what we might learn about how we should be training and exposing models.
About David Evans
David Evans is a Professor of Computer Science at the University of Virginia where he leads research on security and privacy with a recent focus on adversarial machine learning and inference risks in machine learning, and teaches courses on a wide variety of topics including biology, ethics, economics, and theory of computing. He is the author of an open computer science textbook and a children's book on combinatorics and computability and co-author of a book on secure computation.
He won the Outstanding Faculty Award from the State Council of Higher Education for Virginia and is Program Co-Chair for the 2022 and 2023 IEEE European Symposia on Security and Privacy. He was Program Co-Chair for the 24th ACM Conference on Computer and Communications Security (CCS 2017) and the 30th (2009) and 31st (2010) IEEE Symposia on Security and Privacy, where he initiated the Systematization of Knowledge (SoK) papers.
He has SB, SM and PhD degrees in Computer Science from MIT and has been a faculty member at the University of Virginia since 1999.