Department of Chemistry Professor Christopher (Kit) Cummins has been honored with the 2017 Linus Pauling Medal, in recognition of his unparalleled synthetic and mechanistic studies of early-transition metal complexes, including reaction discovery and exploratory methods of development to improve nitrogen and phosphorous utilization. It is presented annually in recognition of outstanding achievement in chemistry in the spirit of, and in honor of, Linus Pauling, who was awarded the Nobel Prize in chemistry in 1954 and the Nobel Prize for peace in 1962. Cummins joins several current members of the Department of Chemistry in being named a Linus Pauling Medal awardee, including Tim Swager (2016), Stephen Buchwald (2014), and Stephen Lippard (2009), as well as former department members Alexander Rich (1995) and John Waugh (1984). In addition, Cummins Group researchers work to develop new starting materials in phosphate chemistry, including acid forms that provide a starting point for synthesizing new phosphate-based materials with applications in next-generation battery technologies and catalysis.
When organic chemists identify a useful chemical compound -- a new drug, for instance -- it's up to chemical engineers to determine how to mass-produce it. But MIT researchers are trying to put this process on a more secure empirical footing, with a computer system that's trained on thousands of examples of experimental reactions and that learns to predict what a reaction's major products will be. In tests, the system was able to predict a reaction's major product 72 percent of the time; 87 percent of the time, it ranked the major product among its three most likely results. In the past, chemists have built computer models that characterize reactions in terms of interactions at reaction sites.
The MIT Libraries honored the outstanding contributions of staff to the Institute at its Infinite Mile Awards ceremony on June 14. Web developer Matt Bernhardt's approach to work consistently involves thoughtful solutions that directly tie back to user needs, whether those users are the MIT community or library staff. Mary Jeanne Yuen, metadata production associate, helps others search the vast MIT Libraries map collections. Rix makes one's problems his problems and takes pleasure in helping his colleagues, sometimes with inadequate notice, sometimes stepping in when others are absent or unavailable, but always without fanfare.
A team of MIT and Stanford University researchers has developed a way to label neurons when they become active, essentially providing a snapshot of their activity at a moment in time. This approach could offer significant new insights into neuron function by offering greater temporal precision than current cell-labeling techniques, which capture activity across time windows of hours or days. Existing tools allow researchers to engineer cells so that when neurons turn on a gene called cfos, which helps cells respond to new information, they also turn on an artificially introduced gene for a fluorescent protein or another tagging molecule. The researchers designed their tool to respond to calcium, because neurons experience an flux of calcium ions every time they fire an electrical impulse.
"The ability to both fly and drive is useful in environments with a lot of barriers, since you can fly over ground obstacles and drive under overhead obstacles," says PhD student Brandon Araki, lead author on the paper. The project builds on Araki's previous work developing a "flying monkey" robot that crawls, grasps, and flies. Rus says that systems like theirs suggest that another approach to creating safe and effective flying cars is not to simply "put wings on cars," but to build on years of research in adding driving capabilities to drones. "As we begin to develop planning and control algorithms for flying cars, we are encouraged by the possibility of creating robots with these capabilities at small scale," Rus says.
Estay Forno is one of more than 140 undergraduate students from across the School of Engineering who took part in the Advanced Undergraduate Research Opportunities Program (SuperUROP) this year. This year's SuperUROP program included students from the departments of Aeronautics and Astronautics, Biological Engineering, Chemical Engineering, and Civil and Environmental Engineering as well as from EECS. Moses recounts that the course professors -- Anantha Chandrasakan, the Vannevar Bush Professor of Electrical Engineering and EECS department head; Dennis (Denny) Freeman, the dean for undergraduate education; and Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science -- wanted students to gain these skills because they would "be important wherever we go," Moses says. Another mentor, postdoc Rabia Tugce Yazicigil, helped EECS senior Daniel Richman understand how to approach research, a process that can be as disorienting as wandering in a forest.
Emily Sheldon of the MIT Admissions Office won her award for devoting considerable time and energy to recruiting and enrolling a diverse array of students, in particular transfer students, veterans, and Reserve Officer Training Corps (ROTC) students. Her collaborative efforts to attract ROTC students have contributed to a five-fold increase in the number of students admitted to the MIT ROTC programs. Jake Livengood of the Global Education and Career Development Office was recognized for developing engaging improv workshops to help students improve their job search skills and enhance their self confidence. Katherine Wahl of the UAAP's Assistive Technology Information Center received the award for developing novel methods to meet the increased demand for accessibility evaluations.
When applied to previously-collected atmospheric samples and data, their findings support evidence that on average these bioaerosols globally make up less than 1 percent of the particles in the upper troposphere -- where they could influence cloud formation and by extension, the climate -- and not around 25 to 50 percent as some previous research suggests. While atmospheric and climate modeling suggests that bioaerosols, globally averaged, are not abundant and efficient enough at freezing to significantly influence cloud formation, research findings have varied significantly. The group leveraged the presence of phosphorus in the mass spectra to train the classification machine learning algorithm on known samples and then, primed, applied it to field data acquired from Desert Research Institute's Storm Peak Laboratory in Steamboat Springs, Colorado, and from the Carbonaceous Aerosol and Radiative Effects Study based in the town of Cool, California. Knowing that the principal atmospheric emissions of phosphorus are from mineral dust, combustion products, and biological particles, they exploited the presence of phosphate and organic nitrogen ions and their characteristic ratios in known samples to classify the particles.
MIT President L. Rafael Reif today attended a technology conference convened by the White House Office of American Innovation. Reif attended two of the small group discussions -- one titled "Analytics/Dashboard," focused on how to use data and metrics to improve government services, personnel and technology; and one titled "Future Trends," on how government can anticipate, integrate, and facilitate the development of emerging technologies, especially in fields such as machine learning and the internet of things (IoT). "As an institution with a mission of national service, and as a pioneer in many of the technologies under discussion at the White House -- from machine learning and AI to robotics and IoT -- MIT has an important role to play at the center of this conversation." In support of MIT's mission, President Reif has spoken in recent months, through letters to the MIT community and published opinion pieces, on the importance of continuing to attract global talent to the United States, sustaining federal financial support for advanced scientific research, and continuing global cooperation on climate change.
"Deep learning" computer systems, based on artificial neural networks that mimic the way the brain learns from an accumulation of examples, have become a hot topic in computer science. The result, Shen says, is that the optical chips using this architecture could, in principle, carry out calculations performed in typical artificial intelligence algorithms much faster and using less than one-thousandth as much energy per operation as conventional electronic chips. "The natural advantage of using light to do matrix multiplication plays a big part in the speed up and power savings, because dense matrix multiplications are the most power hungry and time consuming part in AI algorithms" he says. The research team also included MIT graduate students Scott Skirlo and Mihika Prabhu in the Research Laboratory of Electronics, Xin Sun in mathematics, and Shijie Zhao in biology, Tom Baehr-Jones and Michael Hochberg at Elenion Technologies, in New York, and Hugo Larochelle at Université de Sherbrooke, in Quebec.