Assistant Director for Asia and Africa
Office of Terrorist Financing and Financial Crimes
Department of the Treasury
Building 46 Map
Frank Tong, PhD
What neural processes underlie people's ability to perceive, attend to, or remember, visual features and complex objects? To what extent might high-level processes of attention or memory depend on accessing relevant information in early visual areas? Using functional MRI and pattern classification methods, my lab has found that it is possible to decode what item a person is seeing, attending to, or remembering, from activity patterns at early stages of visual processing. Our studies reveal that rudimentary processing of orientation emerges in the lateral geniculate nucleus, and that orientation-selective responses in the primary visual cortex can be dynamically altered by attentional relevance and surrounding visual context. In studies of visual working memory, we find that information about simple and complex stimuli is actively maintained in the detailed activity patterns of the visual cortex, even after the overall BOLD response has fallen to baseline levels. Finally, we find that when observers must identify objects in the presence of visual clutter, detailed patterns of attentional feedback to early visual areas serve to separate the attended object from background noise. Object-based attention can thereby serve the function of active noise filtering. Taken together, our results support an interactive model of visual processing, in which feedback signals to early visual areas are important for the top-down selection and maintenance of visual information needed to perform demanding cognitive tasks.
One Main Street
East Arcade - 1st floor
Cambridge, MA 02142
Meeting Opportunity, Presented by MIT Startup Exchange
How we eat, how we are being served food, what we feed animals, and what we know about nutrition is currently undergoing radical change due to societal changes as well as recent advances in food tech. In fact, food tech innovation has made leaps forward even over the last three years. However, despite breakthrough research, innovation, and startup activity, and first adopters being very excited, the food industry as a whole has not yet implemented all these changes. The general public is ever more bombarded by confusing messaging on nutrition, technology, business models, and gadgets building on biotech, sensing, ICT, or sharing economy concepts.
MIT Startup Exchange (STEX) is convening a workshop to discuss the latest advances in food tech innovation from the perspective of the corporates, academics, VCs, and startups in the MIT ecosystem redefining the field today. We aim to cover innovation models, technologies, collaboration patterns, and partnerships and we will look 3-5 years into the future and see where research, innovation, startups, industry and consumers will be in terms of food, lifestyle, nutrition, manufacturing, and services.
Food tech innovation: Next Gen nutrition, manufacturing, services, sensing, and beyond will be held on Wednesday, April 22, 2015, 8:45 AM to 11:30 AM at One Main Street, Cambridge. The target audience is the MIT innovation ecosystem, including faculty, students, startups, and ILP member companies. Ten seats are reserved for MIT startup founders. The workshop will cater to and feature startups from several STEX clusters. Our Healthcare cluster contains 64 startups. Our advanced manufacturing cluster contains 56 startups. Our Biotech cluster contains 133 startups. All are welcome and attendance is free.
08:30 AM Breakfast and registration.
08:50 AM Welcome: "MIT's Food tech startups," Trond Undheim, Ph.D., Lead, MIT Startup Exchange, MIT ILP (host).
09:00 AM Introductory remarks: "Connecting industry to research, innovation, and startups", Karl Koster, Executive Director, MIT ILP.
09:10 AM "Future opportunities for food tech product innovation from a research perspective", Omer Yilmaz, PhD, Assistant Professor of Biology, MIT, gastrointestinal pathologist, Massachusetts General Hospital and Harvard Medical School.
09:25 AM "The Future of Food", Manoj Fenelon, Director of Foresight, PepsiCo.
Larry Gilbertson, Ph.D., Cambridge Site Lead, Biotechnology, Monsanto Company.
Omer Yilmaz, PhD, Assistant Professor of Biology, MIT, gastrointestinal pathologist, Massachusetts General Hospital and Harvard Medical School.
MIT connected startup executives (Fredric Abramson, Ph.D., Founder, Digital Nutrition, LLC, Dr. Alain C. Briançon, Co-Founder/CEO, Kitchology Inc.)
Manoj Fenelon, Director of Foresight, PepsiCo.
Nestle (panelist, tbd).
About MIT Startup Exchange (STEX):
MIT Startup Exchange connects corporates to MIT startups, fostering quality interactions that lead to strong partnerships with impact across the MIT innovation ecosystem.The STEX web community platform and database has nearly 1000 active MIT startup companies at all stages of development and representing seven technology clusters: Tech/ICT, Biotech, Nanotech, Energy Tech, Advanced Manufacturing, Healthcare, and Hybrid Innovation. See http://startupexchange.mit.edu ABOUT and FAQS for more information.
Building E62 Map
MIT Lincoln Laboratory
Host: Andrew Lo
Rules, regulations, and policies are the basis of civilized society and are used to coordinate the activities of individuals who have a variety of goals and purposes. History has taught that over-regulation (too many rules) makes it difficult to compete and under-regulation (too few rules) can lead to crisis. This implies an optimal number of rules that avoids these two extremes. Rules create boundaries that define the latitude an individual has to perform their activities. This paper creates a Toy Model of a work environment and examines it with respect to the latitude provided to a normal individual and the latitude provided to an insider threat. Simulations with the Toy Model illustrate four regimes with respect to an insider threat: under-regulated, possibly optimal, tipping-point, and over-regulated. These regimes depend up the number of rules (N) and the minimum latitude (Lmin) required by a normal individual to carry out their activities. The Toy Model is then mapped onto the standard 1D Percolation Model from theoretical physics and the same behavior is observed. This allows the Toy Model to be generalized to a wide array of more complex models that have been well studied by the theoretical physics community and also show the same behavior. Finally, by estimating N and Lmin it should be possible to determine the regime of any particular environment.
Dr. Kepner leads large scale computing research across MIT's largest laboratory. Dr. Kepner is the most published author in the 60+ year history of Lincoln Laboratory. His published works span signal processing, data mining, databases, high performance computing, graph algorithms, cyber security, visualization, cloud computing, random matrix theory, abstract algebra, bioinformatics, astronomy, physics, and astrophysics. In addition he has authored two books on parallel computing and graph algorithms. He recently received Lincoln's highest honor for technical excellence "For his leadership and vision in bringing supercomputing to Lincoln Laboratory through the establishment of LLGrid; his pivotal role in open systems for embedded computing; his creativity in developing a novel database management language and schema; and his contributions to the field of graph analytics." More recently, Dr. Kepner has been at the forefront of developing new signal processing technique for genetic sequence analysis and operating on data while it is stored in encrypted form. Dr. Kepner is the Chair of the largest computing conference in New England (IEEE High Performance Extreme Computing) and Vice-Chair of SIAM Data Mining. Dr. Kepner received his Ph.D. in Astrophysics from Princeton University in 1998.
Coleman F. Fung Professor in the School of Engineering
and a Professor of Medicine (by Courtesy)
When deciding which programs to invest in, public health decision makers face a number of challenges, including limited resources to invest among many potential programs, incomplete information about the potential effects of programs, and objectives that include not only health maximization but social, political, and cultural considerations. OR-based modeling can play a key role in informing such decisions: by providing a structured framework that uses the best available evidence, imperfect as it may be, and that captures relevant uncertainties, complexities, and interactions, OR-based models can be used to evaluate the potential impact of alternative public health programs. This talk describes modeling efforts in which OR has played and can play a role in informing public health decision making. We conclude with a discussion of useful lessons for OR modelers who wish to work on health-related and policy-related problems.
Margaret Brandeau is the Coleman F. Fung Professor of Engineering and Professor of Medicine (by Courtesy) at Stanford University. Her research focuses on the development of applied mathematical and economic models to support health policy decisions. Her recent work has focused on HIV prevention and treatment programs, programs to control the spread of hepatitis B virus, and preparedness plans for bioterror response. She is a Fellow of the Institute for Operations Research and Management Science (INFORMS), and has received the President’s Award from INFORMS (recognizing important contributions to the welfare of society), the Pierskalla Prize from INFORMS (for research excellence in health care management science), the Award for Excellence in Application of Pharmacoeconomics and Health Outcomes Research from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), and a Presidential Young Investigator Award from the National Science Foundation, among other awards. Professor Brandeau earned a BS in Mathematics and an MS in Operations Research from MIT, and a PhD in Engineering-Economic Systems from Stanford University.
Building 32 Map
Austrian Academy of Sciences
Hosted by Alan P. Jasanoff and Ed Boyden
MIT general map location link
H. Jack Geiger
City University of New York Medical School