The MIT Center for Advanced Virtuality (MIT Virtuality for short) pioneers innovative experiences using technologies of virtuality — computing systems that construct imaginative experiences atop our physical world. Our approach to engineering and creative practices pushes the expressive potential of technologies of virtuality and simulates social and cognitive phenomena, while intrinsically considering their social and cultural impacts. This talk focuses on an important aspect of such technologies: virtual selves. Indeed nearly early everyone these days uses virtual identities, ranging from accounts for social media and online shopping to avatars in videogames or virtual reality. Given the widespread and growing use of such technologies, it is important to better understand their impacts and to establish innovative and best practices. In this talk, Harrell explores how our social identities are complicated by their intersection with extended reality technologies, videogames, social media, and related digital media forms. With an emphasis on equity, Harrell will explore how virtual identities both implement and transform persistent issues of class, gender, sex, race, ethnicity, and the dynamically construction social categories more generally.
Want some good news about the environment? In America, we have finally learned to grow our economy while taking less from the Earth year after year: less water, timber, and metal; fewer minerals and resources; even less energy. This talk is a show and tell about this profound change. Andy McAfee will show the evidence that we've started getting more from less and tell how it happened. The unlikely heroes of the tale are the cost pressures that come from intense competition and powerful digital tools that reduce the need for resources. In short, prices and processors are now letting us tread more lightly on the Earth. The story is full of surprises and also insights. In particular, it gives us a playbook for dealing with the major challenges still ahead of us: global warming, pollution, and species loss.
Currently, medical images require a physician to extract clinically relevant information. This talk will explore current work towards making images part of the quantitative medical history and to enable large-scale image-based studies of disease. Although large databases of clinical images contain a wealth of information, medical acquisition constraints result in sparse scans that miss much of the anatomy. These characteristics often render computational analysis impractical as standard processing algorithms tend to fail when applied to such images. Our goal is to enable application of existing algorithms that were originally developed for high resolution research scans to severely undersampled images. Application of the method is illustrated in the context of neurodegeneration and white matter disease studies in stroke patients.
This talk introduces a new generation of machine learning methods that provide state of the art performance and are very interpretable, introducing optimal classification (OCT) and regression (ORT) trees for prediction and prescription with and without hyperplanes. This talk shows that (a) Trees are very interpretable, (b) They can be calculated in large scale in practical times, and (c) In a large collection of real world data sets, they give comparable or better performance than random forests or boosted trees. Their prescriptive counterparts have a significant edge on interpretability and comparable or better performance than causal forests. Finally, we show that optimal trees with hyperplanes have at least as much modeling power as (feedforward, convolutional, and recurrent) neural networks and comparable performance in a variety of real world data sets. These results suggest that optimal trees are interpretable, practical to compute in large scale, and provide state of the art performance compared to black box methods.
Immune homeostasis requires constant collaboration between a diverse and dynamic set of cell types. Within our immune tissues, distinct cellular subsets must work together to defend against pathogenic threats, maintain tolerance, and establish memory. While surveying multiple healthy individuals enables exploration of potential ensemble immune solutions, contrasts against outliers of health and disease can reveal deviations that underscore diagnostic, therapeutic, and prophylactic features of enhanced function or dysfunction. Here, I will discuss how we can leverage single-cell genomic approaches – and, in particular, single-cell RNA-Seq – to explore the extensive functional diversity among immune cells within and across individuals, and uncover, from the bottom-up, distinct cell types and states associated with improved immunity. Moreover, I will discuss emerging experimental and computational strategies for altering ensemble cellular responses through targeted intra- or extracellular induction of these preferred types and states.
This talk introduces Emerald, a novel MIT technology for in-home non-intrusive patient monitoring. The Emerald device is a WiFi-like box that runs customized machine learning algorithms to learn digital biomarkers from the wireless signals in the patient's home. It can remotely monitor the patient’s gait speed, falls, respiratory signal, heart rate, and sleep quality and stages. The sensing is completely passive – i.e., the patient can go about her normal life without having to wear any sensors on her body, write a diary, or actively measure herself. This talk will discuss the technology and the results from pilot studies in various therapeutic areas.
Breast cancer is global problem with over 500,000 women dying from the disease every year, yet all of our decisions and insights are based on only a fraction of the information that exists at both the patient and population level. In this talk, we explore a machine learning approach to cancer that integrates rich patient information at population scale, and discuss the type of tools this enables. We have developed A.I systems for automatically reading mammograms, performing personalized risk assessment and mining medical records and implemented them clinically at Massachusetts General Hospital.