Way back to she will keep in mind, Cathy Wu ’12, MNG ’13 needed to search out methods to resolve issues to enhance folks’s lives. Her dad and mom had been Taiwanese immigrants, and her father had an extended commute to his job, which took him away from the household. On a good price range, the remainder of the household typically stayed house on a avenue that was too busy for taking part in open air. Wu and her siblings ended up enjoying plenty of pc video games.
Wu says her want to make the world a greater place, her dad’s day by day battle in opposition to site visitors, and the video games she performed, like “SimCity,” had been the seeds of her motivation to design secure, environment friendly transportation techniques.
Wu is an affiliate professor within the MIT Division of Civil and Environmental Engineering (CEE) and the Institute for Knowledge, Techniques, and Society (IDSS), and a principal investigator within the Laboratory for Data and Choice Techniques. Her analysis focuses on utilizing machine studying and reinforcement studying (RL) to advance dependable methods for enhancing a spread of complicated techniques, together with transportation.
“Designing transportation techniques consists of modeling and analyzing dozens, if not a whole bunch or 1000’s, of variants, which implies that an evidence-driven strategy to designing these techniques is solely not inside attain of at the moment’s instruments,” Wu says. “That is the position that RL performs. If profitable, it could free transportation researchers and allow their practitioner companions to design the techniques they need.”
Wu credit her older sister with instilling in her the will to enhance folks’s lives, and Wu’s curiosity in transportation matches neatly into that ultimate.
“I like transportation as a result of it connects everybody. All of us use it, all of us expertise it, all of us have points with it. So, at some stage, we’re all within the system being higher,” she says.
Wu received all in favour of making use of synthetic intelligence to transportation whereas incomes her undergraduate diploma at MIT, after attending a lecture on autonomous autos by the late professor Seth Teller. The lecture, which Teller gave throughout an Unbiased Actions Interval robotics competitors (that Wu truly gained), was the occasion that honed her specific strategy to transportation analysis, Wu says. She started working with Teller, and when he stopped concentrating on autonomous autos, he inspired Wu to switch to Professor Daniela Rus, who had carried out analysis on robotaxis.
“I’m very grateful to the individuals who helped me discover these pursuits and helped me turn out to be the individual I’m now,” she says, particularly naming Teller, Rus, and “my mates at Dropbox,” who invited her to do a second internship targeted on transportation points.
After her grasp’s diploma at MIT, Wu went on to earn her PhD on the College of California at Berkeley. Throughout that point, she noticed that transportation researchers had been spending years creating optimization strategies to mannequin and analyze a single new variant of a system. Her strategy as a pc scientist working to develop RL and optimization methodologies to deal with transportation challenges held the promise of exponentially improved effectivity.
In 2018, Wu’s final yr of her PhD at UC Berkeley, she efficiently utilized RL to a site visitors drawback: routinely analyzing the potential site visitors circulate affect of autonomous autos in a spread of various site visitors networks. The analysis went viral.
Whereas this might have been a “the remainder is historical past” second for Wu, RL turned out to be a flighty buddy. Wu labored on RL concept in a postdoc at Microsoft and got here again to MIT as school drawn, she says, by the sustainability focus of CEE, and IDSS’s emphasis on infusing information science into different disciplines.
But over the following two years, Wu’s additional makes an attempt to use RL to site visitors issues failed.
“That was tense,” Wu says, “it was unclear whether or not the issue was me (the advisor), my college students, the site visitors area, or RL itself.”
Nonetheless, the sooner analysis was a proof-of-concept demonstration that RL could possibly be utilized to transportation techniques.
And in 2022, she and her college students recognized that RL algorithms are so delicate that an algorithm that works on one drawback could not on even a intently associated one. A key outcome, which Wu says she is proudest of “as a result of it was like the sunshine on the finish of an extended tunnel of adverse outcomes,” got here in 2023. She and her group of researchers devised a solution to work across the sensitivity of RL. The group discovered that whereas RL could not prepare properly on 90 % of a bunch of issues, it could prepare fairly properly on 10 %. And by coaching RL fashions on these issues that remedy and generalize properly, the resultant fashions collectively carry out properly on a set of associated issues, even those who wouldn’t have been solved by way of direct coaching. The researchers designed an algorithm to find out which issues to make use of RL to coach, and that algorithm improved coaching effectivity by as much as 30 instances, that means that what would usually have required 100 coaching fashions could solely require three fashions.
“This work gave me again the boldness that reinforcement studying can play an essential position in fixing onerous optimization issues, together with in transportation,” Wu says. “Now, chunk of my group works on the subject of contextual RL, which is the setting the place RL seeks to resolve an area of associated issues.”
Wu’s newer analysis applies RL to resolve a tough transportation optimization drawback with essential coverage implications: the work exhibits that eco-driving measures by which automobile speeds are intelligently managed to scale back extreme stopping and beginning might scale back automobile emissions by between 11 and 22 %. The system gives proof that insurance policies instituting such measures might considerably enhance system effectivity, and is “an indication that RL can be utilized to tell transportation coverage on issues of sensible significance,” Wu says.
“I’m a giant fan of evidence-based coverage and consider it’s the idea for a thriving democratic society, but our societal techniques are so complicated,” Wu says. “Individuals can bicker endlessly about what’s higher or worse, however I do consider that there are questions we bicker about that may be analyzed systematically utilizing information and have goal solutions. A big a part of the explanation I’m in academia is to raised perceive how expertise can assist democratic societal decision-making.”
Wu says that a lot of the work she and her group have carried out during the last a number of years has produced algorithms “to streamline the event of solvers for onerous optimization issues, whether or not they’re associated to transportation or to different techniques, comparable to logistics, provide chains, manufacturing, and useful resource allocation.
“This alludes to my most popular model of labor,” Wu says, “which is known as use-inspired fundamental analysis,” explaining that such analysis addresses a sensible drawback, creating basic information that usually interprets to different sensible issues. Her college students begin by probing consequential issues starting from security to congestion to accessibility, figuring out the place present strategies fall brief, and permitting the issues themselves to form the course of the analysis.
On the identical time, Wu’s want to assist others on a extra private stage performs out in her instructing.
“I like working with college students, each within the classroom and analysis mentoring,” she says. “It makes my day when I’m able to educate somebody one thing — after I see that mild bulb go on in a pupil.”
Along with incomes educational honors, together with a 2023 Nationwide Science Basis School Early Profession Growth Award, Wu has additionally been formally celebrated for her instructing and mentoring, together with with the Ole Madsen Mentoring Award in 2025.
What does she inform college students confronting extraordinarily difficult issues?
“Be affected person. Begin small. Societal affect is a lifelong endeavor, not one thing to be achieved in just a few years,” Wu says. “It can take years to actually perceive what’s happening and the place the true issues are. Within the meantime, attempt to be useful. Be curious. Ask many questions.”
