MIT Media Lab researcher Joy Buolamwini SM ’17 created the Gender Shades project (www.gendershades.org) to examine error rates in the gender classification systems of three commercially available facial-analysis products. Her accompanying paper shows a significant accuracy gap between classifying male and female faces, as well as between darker and lighter faces. One gap was most pronounced: the highest error for light-skinned males was .08% while, for darker females, it was 34.7%—raising questions about the data sets used to train such machine learning systems. Buolamwini is founder of the Algorithmic Justice League, devoted to highlighting algorithmic bias and developing practices of accountability during the design, development, and deployment of coded systems.
Dina Katabi SM ’99, PhD ’03 is the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT, leader of the NETMIT research group at CSAIL, and director of the MIT Center for Wireless Networks and Machine Computing. Katabi combines novel sensing technologies with machine learning, optimization theory, and signal processing algorithms to solve real-world problems. One of these problems—inspired by Katabi’s own family experiences—is the noninvasive in-home monitoring of the elderly. She and her team have developed a device that uses low-power wireless signals, like WiFi, to track human motion, which among other health applications can generate an alert if an occupant falls or appears likely to fall.
The Financial Economist and the Cryptographer
Andrew Lo, Charles E. and Susan T. Harris Professor at the MIT Sloan School of Management, has teamed up with Vinod Vaikuntanathan SM ’05, PhD ’09, associate professor of electrical engineering and computer science, to measure the economic effects of cyberattacks. Together they are creating a multiparty platform to collect data that will give markets and firms better cybersecurity risk information, while respecting privacy concerns. Their initial efforts will be focused on helping financial institutions to share intrusion data using secure multiparty computation techniques, and they’ve already signed up several companies for a pilot project.
Health care analytics are a passion for Dimitris Bertsimas SM ’87, PhD ’88, the Boeing Leaders for Global Operations Professor at the MIT Sloan School of Management and co-director of MIT’s Operations Research Center. His group has developed a system called LiA (Lifestyle Analytics) that guides personalized diabetes management by eliciting patient preferences, modeling blood glucose behavior, and updating these models to match individual measurements, and generating a customized diet and exercise plan. Among Bertsimas’s many other projects are systems for the analysis and design of clinical trials for cancer drug combinations; health plan selection that weighs the needs of both employer and employees; and decision support for surgeons selecting donor kidneys for their patients.
Deep Sea Algorithms
Genevieve Flaspohler, a PhD student in the MIT/Woods Hole Oceanographic Institute (WHOI) Joint Program, is collaborating with her advisors—WHOI assistant scientist Yogesh Girdhar and AeroAstro professor Nicholas Roy, who directs MIT CSAIL’s Robust Robotics Group—on the development of unsupervised or minimally supervised machine learning algorithms for autonomous underwater vehicles (AUVs). Together, they are honing the capability of AUVs to explore unknown marine environments that are inaccessible to human divers, while endowing them with the semantic understanding to interpret the images and other environmental sensor data they collect.
Research by Webcam
How do children learn so much from so little, so quickly? Professor of cognitive science Laura Schulz investigates that profound question in the Early Childhood Cognition Lab. Her group’s research can now benefit from a larger, diversified stream of data thanks to Lookit, an online laboratory platform conceived and run by research scientist Kim Scott PhD ’18. Via webcam, the researchers record babies’ reactions and attention to onscreen phenomena. Parents can participate from their own homes at their convenience, dramatically widening the demographics and volume of participants the researchers are able to recruit for a given study.
Social robotics pioneer Cynthia Breazeal SM ’93, ScD ’00 is associate professor of media arts and sciences, leads the Personal Robots Group at the MIT Media Lab, and is founder and chief experience officer of Jibo, Inc., maker of the world’s first “family robot.” Her research focuses on developing the principles, techniques, and technologies for personal robots that are socially and emotionally intelligent, interact and communicate with people in human-centric terms, and collaborate with people as helpful teammates and companions. Her recent work investigates the potential of social robots to help people of all ages achieve personal goals that contribute to quality of life in domains such as education, creativity, health care, well-being, and aging in place.
Reprogramming bacteria to detect and treat infectious disease is one of the great frontiers of synthetic biology. One of that field’s founders, James J. Collins—MIT’s Termeer Professor of Medical Engineering and Science and a member of the Broad Institute—designs and creates synthetic gene networks for a variety of biotechnology and medical applications. Collins and his team use network biology approaches to study antibiotic action, bacterial defense mechanisms, and the emergence of antibiotic resistance. According to Collins, AI is the key to fast-tracking such efforts, even as synthetic biology offers a new, living platform for the development of AI.
Predicting Sound from Video
In the Laboratory for Computational Audition, Joshua McDermott PhD ’07 operates at the intersection of psychology, neuroscience, and engineering. An assistant professor in the Department of Brain and Cognitive Sciences, McDermott and his team work to understand how humans derive information from sound, to improve treatments for hearing impairment, and to enable the design of machine systems that mirror human abilities to recognize and interpret sound. In one such project, McDermott collaborated with colleagues from the Computer Science and Artificial Intelligence Lab (CSAIL) on an algorithm that can learn how to predict sound from video footage and produce the expected sound realistically. Such an algorithm could strengthen machines’ ability to understand the physical properties of objects.
Recipes for Sustainable Materials
Elsa Olivetti PhD ’07 (Atlantic Richfield Assistant Professor of Energy Studies in the Department of Materials Science and Engineering) and Stefanie Jegelka (X-Consortium Career Development Assistant Professor of Electrical Engineering and Computer Science) are joining forces to create a neural network that can pore through scientific papers and, through pattern recognition, extract “recipes” for producing particular types of materials. In a recent paper, Olivetti, Jegelka, and colleagues used this mechanism to suggest alternative recipes for known materials. Their work could also help to identify practical ways to create new materials with desirable properties.
Computational and systems biologist Aviv Regev—MIT biology professor; Broad Institute core member and chair of faculty; Howard Hughes Medical Institute Investigator; and cofounder of the Human Cell Atlas initiative—studies how complex molecular circuits function in cells and between cells in tissues. The Regev lab invents experimental methods and associated computational algorithms, motivated by machine learning, that allow inference even in the context of the vast space of biological possibilities. Her lab has pioneered foundational experimental and computational methods in single-cell genomics, working toward greater understanding of the function of cells and tissues in health and disease, including autoimmune disease, inflammation, and cancer.
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