Graduate, postdoctoral

Cofounded by postdoctoral associate Wen Shuhao and postdoctoral fellows Ma Jian and Lai Lipeng, biotech startup Xtalpi "combines AI, quantum physics, cloud computing and robotic automation to find novel molecules that could be developed into new medicines,” reports Zinnia Lee for Forbes. “Xtalpi has also recently expanded into discovering new chemical compounds for applications such as agriculture, cosmetics, healthcare, as well as petrochemicals and new materials for electric vehicle batteries,” writes Lee.

Writing for the Boston Globe, graduate student Sophie Hartley spotlights researchers and arborists battling beech leaf disease  a highly infectious disease caused by microscopic roundworms that “can disrupt a tree’s ability to photosynthesize and therefore survive." “Over the past decade, federal, state, and private agencies have shown up en masse to learn all they can about beech leaf disease, resulting in an extensive body of knowledge to inform policy,” explains Hartley. “By matching the worms’ seemingly unstoppable push with equally relentless research, experimentation, and community support, the hope is that all is not lost. Not yet.” 

Scientific American’s Nick Hilden reports on the influence that popular narratives have on our collective perceptions. Graduate student Pat Pataranutaporn notes: “why do we always imagine science fiction to be a dystopia? Why can’t we imagine science fiction that gives us hope?”

The Hill reporter Tobias Burns spotlights the efforts of a number of MIT researchers to better understand the impact of generative AI on productivity in the workforce. One research study “looked as cases where AI helped improved productivity and worker experience specifically in outsourced settings, such as call centers,” explains Burns. Another research study explored the impact of AI programs, such as ChatGPT, among employees. 

Forbes selects innovators for the list’s Healthcare & Science category, written by senior contributor Yue Wang. On the list is MIT PhD candidate Yuzhe Yang, who studies AI and machine learning technologies capability to monitor and diagnose illnesses such as Parkinson's disease.

An analysis by MIT researchers has identified “wide-ranging instances of AI systems double-crossing opponents, bluffing and pretending to be human,” reports Hannah Devlin for The Guardian. “As the deceptive capabilities of AI systems become more advanced, the dangers they pose to society will become increasingly serious,” says postdoctoral associate Peter Park. 

Researchers from MIT and elsewhere have found that “showing AI-generated images of a less car-reliant American city boosted support for sustainable transportation policies,” reports Linda Poon for Bloomberg. “Let’s help them imagine what it would actually be like to live in a car-less neighborhood, and a car-less city,” says postdoctoral associate Rachit Dubey. 

Scientific American reporter Riis Williams explores how MIT researchers created “smart gloves” that have tactile sensors woven into the fabric to help teach piano and make other hands-on activities easier. “Hand-based movements like piano playing are normally really subjective and difficult to record and transfer,” explains graduate student Yiyue Luo. “But with these gloves we are actually able to track one person’s touch experience and share it with another person to improve their tactile learning process.”

Nature reporter Amanda Heidt speaks with postdoctoral researcher Tigist Tamir about her experience using generative AI with attention-deficit hyperactivity disorder. “Whether I’m reading, writing or just making to-do lists, it’s very difficult for me to figure out what I want to say. One thing that helps is to just do a brain dump and use AI to create a boiled-down version,” Tamir explains. She adds, “I feel fortunate that I’m in this era where these tools exist.”

Postdoc Xuhai Xu and his colleagues have developed an AI program that can distribute pop-up reminders to help limit smartphone screen time, reports Jeremy Hsu for New Scientist. Xu explains that “a random notification to stop doomscrolling won’t always tear someone away from their phone. But machine learning can personalize that intervention so it arrives at the moment when it is most likely to work,” writes Hsu.

Graduate student Dhruv Gaur speaks with Boston.com reporter Lauren Daley about his viral message expressing support for Alex Trebek when he competed on “Jeopardy” in 2019, and his experience being invited back for the show’s first invitational tournament. “I listened to a ton of podcasts — old pop culture from the ‘70s ‘80s and ‘90s; history, Shakespeare,” says Gaur of how he prepared for the tournament. “I got really into quiz games on my phone or computer. I was shocked that nobody realized what I was doing because anytime I got a free second I’d be doing a quiz, or asking friends to go to bar trivia.”

A number of MIT faculty and alumni – including Prof. Daniela Rus, Prof. Regina Barzilay, Research Affiliate Haddad Habib, Research Scientist Lex Fridman, Marc Raibert PhD '77, former Postdoc Rana El Kaliouby and Ray Kurzweil '70 – have been named key figures “at the forefront of Boston’s AI revolution,” reports Wyndham Lewis for Boston Magazine. These researchers are “driving progress and reshaping the way we live,” writes Lewis.

MIT researchers have developed a new technique that uses a large language model to allow robots to self-correct after making a mistake, reports Brian Heater for TechCrunch. “Researchers behind the study note that while imitation learning (learning to do a task through observation) is popular in the world of home robotics, it often can’t account for the countless small environmental variations that can interfere with regular operation, thus requiring a system to restart from square one,” writes Heater. “The new research addresses this, in part, by breaking demonstrations into smaller subsets." 

Prof. Pulkit Agrawal and graduate student Gabriel Margolis speak with The Economist’s Babbage podcast about the simulation research and technology used in developing intelligent machines. “Simulation is a digital twin of reality,” says Agrawal. “But simulation still doesn’t have data, it is a digital twin of the environment. So, what we do is something called reinforcement learning which is learning by trial and error which means that we can try out many different combinations.”

Using the James Webb Space Telescope, postdoc Rohan Naidu will be studying “some of the particularly large and red galaxies, [called little red dots,] that appear much brighter and more massive than theorists have expected galaxies at this epoch to be,” reports Jonathan O’Callaghan for Scientific American. Naidu’s “program will seek to settle the debat about little red dots once and for all,” writes O’Callaghan.