Category: Lectures

 Meet and Greet with John Cheney-Lippold Is Mon., Nov. 18, 3-4 pm

John Cheney-Lippold

Meet & Greet with John Cheney-Lippold, University of Michigan associate professor of American culture and digital studies and author of We Are Data: Algorithms and the Making of Our Digital Selves, will take place Monday, November 18, from 3:00 – 4:00 p.m., in Rekhi G09.

Dr. Cheney-Lippold will present “Algorithms, Accidents, and the Imposition of a World of Calculation” on Monday, November 18, at 7:00 p.m. in EERC 103. The lecture is part of the Institute for Policy, Ethics, and Culture’s Algorithmic Culture Series.

Algorithms are everywhere, organizing the near limitless data that exists in our world. Derived from our every search, like, click, and purchase, algorithms determine the news we get, the ads we see, the information accessible to us and even who our friends are. These complex configurations not only form knowledge and social relationships in the digital and physical world, but also determine who we are and who we can be, both on and offline.

The explosive, sometimes accidental transformations performed by statistics and algorithms alter our world to produce “someone else,” no longer the beings we thought we were.

To demonstrate how statistics and algorithms are fundamentally transformative, Cheney-Lippold explores the use of statistics to invalidate the signature of a multimillion-dollar will and to objectify racial categories in the case of People vs. Collins. He also examines the accidental algorithmics that led to the lethal collision of a Tesla autonomous vehicle.

His lecture reorients many of the pressing questions of contemporary culture of algorithmic bias, ethics, and ideas of justice.

Download the event flyer.

John Cheney-Lippold to Present Algorithmic Culture Series Lecture November 18

John Cheney-Lippold

The Institute for Policy, Ethics, and Culture’s Algorithmic Culture series continues with “Algorithms, Accidents, and the Imposition of a World of Calculation,” a keynote lecture from John Cheney-Lippold, on Monday, Nov. 18, at 7 p.m. in EERC 0103. A Q&A will follow.

Cheney-Lippold is an associate professor of american culture and digital studies at the University of Michigan. He is the author of We Are Data: Algorithms and the Making of our Digital Selves (NYU Press, 2017).

Algorithms are everywhere, organizing the near limitless data that exists in our world. Derived from our every search, like, click, and purchase, algorithms determine the news we get, the ads we see, the information accessible to us and even who our friends are. These complex configurations not only form knowledge and social relationships in the digital and physical world, but also determine who we are and who we can be, both on and offline.

The book, We Are Data by John Cheney-Lippold

The explosive, sometimes accidental transformations performed by statistics and algorithms alter our world to produce “someone else,” no longer the beings we thought we were. To demonstrate how statistics and algorithms are fundamentally transformative, Cheney-Lippold explores the use of statistics to invalidate the signature of a multimillion-dollar will and to objectify racial categories in the case of People vs. Collins. He also examines the accidental algorithmics that led to the lethal collision of a Tesla autonomous vehicle. This lecture reorients many of the pressing questions of contemporary culture of algorithmic bias, ethics, and ideas of justice.

The Algorithmic Culture series will conclude in December with a presentation from Meredith Broussard entitled “Artificial UnIntelligence.” Broussard’s lecture will be held Thursday, Dec. 5 at 7 p.m. in the Memorial Union Building, Ballroom B.

The mission of the Institute for Policy, Ethics, and Culture is to promote research, policy engagement, and teaching that address the ethical and cultural challenges, implications, and strategies unique to the emerging technocultural environment. Its goals are to promote innovative research and collaboration on policy, ethics, and culture; contribute to policy making in Michigan and beyond; and provide students with tools to work proactively in the emerging environment.

Weihua Zhou to Present Friday Seminar Talk

Weihua Zhou

The College of Computing (CC) will present a Friday Seminar Talk on November 15, at 3:00 p.m. in Rekhi 214. Featured this week is Weihua Zhou, assistant professor of Health Informatics and member of the ICC’s Center for Data Sciences. He will present his research titled: “Information retrieval and knowledge discovery from cardiovascular images to improve the treatment of heart failure.” Refreshments will be served.

Abstract: More than 5 million Americans live with heart failure, and the annual new incidence is about 670,000. Once diagnosed, around 50% of patients with heart failure will die within 5 years. Cardiac resynchronization therapy (CRT) is a standard treatment for heart failure. However, based on the current guidelines, 30-40% of patients who have CRT do not benefit from CRT. One of Zhou’s research projects is to improve CRT favorable response by information retrieval and knowledge discovery from clinical records and cardiovascular images. By applying statistical analysis, machine learning, and computer vision to his unique CRT patient database, Zhou has made a number of innovations to select appropriate patients and navigate the real-time surgery. His CRT software toolkit is being validated by 17 hospitals in a large prospective clinical trial.

ACSHF Forum Monday

Beth Veinott

One challenge affecting a variety of teams, such as software development, engineering, military, and crisis management, is overconfidence in the effectiveness of their plans.  Referred to as the planning fallacy, Buehler et al. (1994) suggests that ignoring past failures is a key cognitive element in this phenomenon. This talk summarizes recent experiments examinin the effect of counterfactual reasoning strategies, thinking about what might have happened under different circumstances, on people’s reasons, confidence and predictions.

Leveraging a collaborative, structured analytic technique called the Premortem, this project extends research on counterfactual reasoning to estimates in planning. The results will be discussed in the context of advances in machine learning, AI, and crowdsourcing that have changed the information available to teams.

Anna Little to Present Talk October 18, 1 p.m.

Anna Little

Dr. Anna Little, a postdoc in the Department of Computational Mathematics, Science, and Engineering at Michigan State University, will present her talk, “Robust Statistical Procedures for Clustering in High Dimensions,” on Friday, October 18, 2019, at 1:00 p.m., in Fisher Hall Room 327B.

Dr. Little completed a PhD in mathematics at Duke University in 2011. She has been at Michigan State since 2018.  Visit her website at www.anna-little.com.

Lecture Abstract: This talk addresses multiple topics related to robust statistical procedures for clustering in high dimensions, including path-based spectral clustering (a new method), classical multidimensional scaling (an old method), and clustering in signal processing. Path-based spectral clustering is a novel approach which combines a data driven metric with graph-based clustering. Using a data driven metric allows for fast algorithms and strong theoretical guarantees when clusters concentrate around low-dimensional sets.

Another approach to high-dimensional clustering is classical multidimensional scaling (CMDS), a dimension reduction technique widely popular across disciplines due to its simplicity and generality. CMDS followed by a simple clustering algorithm can exactly recover all cluster labels with high probability when the signal to noise ratio is high enough. However, scaling conditions become increasingly restrictive as the ambient dimension increases, illustrating the need for robust unbiasing procedures in high dimensions.  Clustering in signal
processing is the final topic; in this context each data point corresponds to a corrupted signal. The classic multireference alignment problem is generalized to include random dilation in addition to random translation and additive noise, and a wavelet based approach is used to define an unbiased representation of the target signal(s) which is robust to high frequency perturbations.

Download the event flyer.