May 14, 2024 – MIT Healthcare Innovation Ecosystem
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Five MIT faculty elected to the National Academy of Sciences for 2024

The National Academy of Sciences has elected 120 members and 24 international members, including five faculty members from MIT. Guoping Feng, Piotr Indyk, Daniel J. Kleitman, Daniela Rus, and Senthil Todadri were elected in recognition of their “distinguished and continuing achievements in original research.” Membership to the National Academy of Sciences is one of the highest honors a scientist can receive in their career.Among the new members added this year are also nine MIT alumni, including Zvi Bern ’82; Harold Hwang ’93, SM ’93; Leonard Kleinrock SM ’59, PhD ’63; Jeffrey C. Lagarias ’71, SM ’72, PhD ’74; Ann Pearson PhD ’00; Robin Pemantle PhD ’88; Jonas C. Peters PhD ’98; Lynn Talley PhD ’82; and Peter T. Wolczanski ’76. Those elected this year bring the total number of active members to 2,617, with 537 international members.The National Academy of Sciences is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — with the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations.Guoping FengGuoping Feng is the James W. (1963) and Patricia T. Poitras Professor in the Department of Brain and Cognitive Sciences. He is also associate director and investigator in the McGovern Institute for Brain Research, a member of the Broad Institute of MIT and Harvard, and director of the Hock E. Tan and K. Lisa Yang Center for Autism Research.His research focuses on understanding the molecular mechanisms that regulate the development and function of synapses, the places in the brain where neurons connect and communicate. He’s interested in how defects in the synapses can contribute to psychiatric and neurodevelopmental disorders. By understanding the fundamental mechanisms behind these disorders, he’s producing foundational knowledge that may guide the development of new treatments for conditions like obsessive-compulsive disorder and schizophrenia.Feng received his medical training at Zhejiang University Medical School in Hangzhou, China, and his PhD in molecular genetics from the State University of New York at Buffalo. He did his postdoctoral training at Washington University at St. Louis and was on the faculty at Duke University School of Medicine before coming to MIT in 2010. He is a member of the American Academy of Arts and Sciences, a fellow of the American Association for the Advancement of Science, and was elected to the National Academy of Medicine in 2023.Piotr IndykPiotr Indyk is the Thomas D. and Virginia W. Cabot Professor of Electrical Engineering and Computer Science. He received his magister degree from the University of Warsaw and his PhD from Stanford University before coming to MIT in 2000.Indyk’s research focuses on building efficient, sublinear, and streaming algorithms. He’s developed, for example, algorithms that can use limited time and space to navigate massive data streams, that can separate signals into individual frequencies faster than other methods, and can address the “nearest neighbor” problem by finding highly similar data points without needing to scan an entire database. His work has applications on everything from machine learning to data mining.He has been named a Simons Investigator and a fellow of the Association for Computer Machinery. In 2023, he was elected to the American Academy of Arts and Sciences.Daniel J. KleitmanDaniel Kleitman, a professor emeritus of applied mathematics, has been at MIT since 1966. He received his undergraduate degree from Cornell University and his master’s and PhD in physics from Harvard University before doing postdoctoral work at Harvard and the Niels Bohr Institute in Copenhagen, Denmark.Kleitman’s research interests include operations research, genomics, graph theory, and combinatorics, the area of math concerned with counting. He was actually a professor of physics at Brandeis University before changing his field to math, encouraged by the prolific mathematician Paul Erdős. In fact, Kleitman has the rare distinction of having an Erdős number of just one. The number is a measure of the “collaborative distance” between a mathematician and Erdős in terms of authorship of papers, and studies have shown that leading mathematicians have particularly low numbers.He’s a member of the American Academy of Arts and Sciences and has made important contributions to the MIT community throughout his career. He was head of the Department of Mathematics and served on a number of committees, including the Applied Mathematics Committee. He also helped create web-based technology and an online textbook for several of the department’s core undergraduate courses. He was even a math advisor for the MIT-based film “Good Will Hunting.”Daniela RusDaniela Rus, the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, is the director of the Computer Science and Artificial Intelligence Laboratory (CSAIL). She also serves as director of the Toyota-CSAIL Joint Research Center.Her research on robotics, artificial intelligence, and data science is geared toward understanding the science and engineering of autonomy. Her ultimate goal is to create a future where machines are seamlessly integrated into daily life to support people with cognitive and physical tasks, and deployed in way that ensures they benefit humanity. She’s working to increase the ability of machines to reason, learn, and adapt to complex tasks in human-centered environments with applications for agriculture, manufacturing, medicine, construction, and other industries. She’s also interested in creating new tools for designing and fabricating robots and in improving the interfaces between robots and people, and she’s done collaborative projects at the intersection of technology and artistic performance.Rus received her undergraduate degree from the University of Iowa and her PhD in computer science from Cornell University. She was a professor of computer science at Dartmouth College before coming to MIT in 2004. She is part of the Class of 2002 MacArthur Fellows; was elected to the National Academy of Engineering and the American Academy of Arts and Sciences; and is a fellow of the Association for Computer Machinery, the Institute of Electrical and Electronics Engineers, and the Association for the Advancement of Artificial Intelligence.Senthil TodadriSenthil Todadri, a professor of physics, came to MIT in 2001. He received his undergraduate degree from the Indian Institute of Technology in Kanpur and his PhD from Yale University before working as a postdoc at the Kavli Institute for Theoretical Physics in Santa Barbara, California.Todadri’s research focuses on condensed matter theory. He’s interested in novel phases and phase transitions of quantum matter that expand beyond existing paradigms. Combining modeling experiments and abstract methods, he’s working to develop a theoretical framework for describing the physics of these systems. Much of that work involves understanding the phenomena that arise because of impurities or strong interactions between electrons in solids that don’t conform with conventional physical theories. He also pioneered the theory of deconfined quantum criticality, which describes a class of phase transitions, and he discovered the dualities of quantum field theories in two dimensional superconducting states, which has important applications to many problems in the field.Todadri has been named a Simons Investigator, a Sloan Research Fellow, and a fellow of the American Physical Society. In 2023, he was elected to the American Academy of Arts and Sciences

Scientists develop an affordable sensor for lead contamination

Engineers at MIT, Nanytang Technological University, and several companies have developed a compact and inexpensive technology for detecting and measuring lead concentrations in water, potentially enabling a significant advance in tackling this persistent global health issue.The World Health Organization estimates that 240 million people worldwide are exposed to drinking water that contains unsafe amounts of toxic lead, which can affect brain development in children, cause birth defects, and produce a variety of neurological, cardiac, and other damaging effects. In the United States alone, an estimated 10 million households still get drinking water delivered through lead pipes.“It’s an unaddressed public health crisis that leads to over 1 million deaths annually,” says Jia Xu Brian Sia, an MIT postdoc and the senior author of the paper describing the new technology.But testing for lead in water requires expensive, cumbersome equipment and typically requires days to get results. Or, it uses simple test strips that simply reveal a yes-or-no answer about the presence of lead but no information about its concentration. Current EPA regulations require drinking water to contain no more that 15 parts per billion of lead, a concentration so low it is difficult to detect.The new system, which could be ready for commercial deployment within two or three years, could detect lead concentrations as low as 1 part per billion, with high accuracy, using a simple chip-based detector housed in a handheld device. The technology gives nearly instant quantitative measurements and requires just a droplet of water.The findings are described in a paper appearing today in the journal Nature Communications, by Sia, MIT graduate student and lead author Luigi Ranno, Professor Juejun Hu, and 12 others at MIT and other institutions in academia and industry.The team set out to find a simple detection method based on the use of photonic chips, which use light to perform measurements. The challenging part was finding a way to attach to the photonic chip surface certain ring-shaped molecules known as crown ethers, which can capture specific ions such as lead. After years of effort, they were able to achieve that attachment via a chemical process known as Fischer esterification. “That is one of the essential breakthroughs we have made in this technology,” Sia says.In testing the new chip, the researchers showed that it can detect lead in water at concentrations as low as one part per billion. At much higher concentrations, which may be relevant for testing environmental contamination such as mine tailings, the accuracy is within 4 percent.The device works in water with varying levels of acidity, ranging from pH values of 6 to 8, “which covers most environmental samples,” Sia says. They have tested the device with seawater as well as tap water, and verified the accuracy of the measurements.In order to achieve such levels of accuracy, current testing requires a device called an inductive coupled plasma mass spectrometer. “These setups can be big and expensive,” Sia says. The sample processing can take days and requires experienced technical personnel.While the new chip system they developed is “the core part of the innovation,” Ranno says, further work will be needed to develop this into an integrated, handheld device for practical use. “For making an actual product, you would need to package it into a usable form factor,” he explains. This would involve having a small chip-based laser coupled to the photonic chip. “It’s a matter of mechanical design, some optical design, some chemistry, and figuring out the supply chain,” he says. While that takes time, he says, the underlying concepts are straightforward.The system can be adapted to detect other similar contaminants in water, including cadmium, copper, lithium, barium, cesium, and radium, Ranno says. The device could be used with simple cartridges that can be swapped out to detect different elements, each using slightly different crown ethers that can bind to a specific ion.“There’s this problem that people don’t measure their water enough, especially in the developing countries,” Ranno says. “And that’s because they need to collect the water, prepare the sample, and bring it to these huge instruments that are extremely expensive.” Instead, “having this handheld device, something compact that even untrained personnel can just bring to the source for on-site monitoring, at low costs,” could make regular, ongoing widespread testing feasible.Hu, who is the John F. Elliott Professor of Materials Science and Engineering, says, “I’m hoping this will be quickly implemented, so we can benefit human society. This is a good example of a technology coming from a lab innovation where it may actually make a very tangible impact on society, which is of course very fulfilling.”“If this study can be extended to simultaneous detection of multiple metal elements, especially the presently concerning radioactive elements, its potential would be immense,” says Hou Wang, an associate professor of environmental science and engineering at Hunan University in China, who was not associated with this work.Wang adds, “This research has engineered a sensor capable of instantaneously detecting lead concentration in water. This can be utilized in real-time to monitor the lead pollution concentration in wastewater discharged from industries such as battery manufacturing and lead smelting, facilitating the establishment of industrial wastewater monitoring systems. I think the innovative aspects and developmental potential of this research are quite commendable.”Wang Qian, a principal research scientist at the Institute of Materials Research in Singapore, who also was not affiliated with this work, says, “The ability for the pervasive, portable, and quantitative detection of lead has proved to be challenging primarily due to cost concerns. This work demonstrates the potential to do so in a highly integrated form factor and is compatible with large-scale, low-cost manufacturing.”The team included researchers at MIT, at Nanyang Technological University and Temasek Laboratories in Singapore, at the University of Southampton in the U.K., and at companies Fingate Technologies, in Singapore, and Vulcan Photonics, headquartered in Malaysia. The work used facilities at MIT.nano, the Harvard University Center for Nanoscale Systems, NTU’s Center for Micro- and Nano-Electronics, and the Nanyang Nanofabrication Center.