Cathy Wu

Cathy Wu

@cathywu.bsky.social

AI & Transportation | MIT Associate Professor Interests: AI for good, sociotechnical systems, machine learning, optimization, reinforcement learning, public policy, gov tech, open science. Science is messy and beautiful. http://www.wucathy.com

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This project was 4 years in the making and it's finally out! We found that controlling vehicle speeds to mitigate traffic across a city can cut carbon emissions between 11 and 22 percent. To do this, we used deep reinforcement learning to optimize one million eco-driving scenarios. 🚗🤖🧠

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Huge thanks to MIT JTL-Transit Lab for their leadership in forming this program, and for the contributing mentors from my group: Han Zheng Jung-Hoon Cho Yining Ma Wenbin Ouyang Tianyue Zhou

This summer program brings together faculty and mentors across MIT, UF, and Northeastern to support students interested in transportation, urban planning, machine learning, generative AI, and optimization for complex systems. The program will be held online and is completely free of charge.

Past participants have contributed to impactful research projects across urban systems, operations research, machine learning, and generative AI, with outcomes including top publications and admission to leading graduate programs.

If pure AI or pure search isn't working for you, consider learning-guided optimization! This approach combines the best of both worlds; our most recent work demonstrates this for coordinating agents in warehouse robotics.

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This wouldn't have been possible without an amazing team. Huge shoutout to Han Zheng (lead author), Yining Ma, Brandon Araki, Jingkai Chen, and Symbotic! #DeepRL #Robotics #WarehouseAutomation #AI

If pure AI or pure search isn't working for you, consider learning-guided optimization! This approach combines the best of both worlds; our most recent work demonstrates this for coordinating agents in warehouse robotics.

Deep RL learns high-level right-of-way priorities, while a fast search solver takes care of low-level path planning. This prevents gridlock on the warehouse floor, achieving ~25% more throughput than hand-crafted rules.

Cathy Wu reposted

Replying to @davidzipper.bsky.social

MIT prof @cathywu.bsky.social suspected that nav apps inaccurately calculate “time to arrive,” esp re: parking. In a new paper, she and colleagues built a model to direct urban drivers to garages that best balance proximity and likelihood of an open spot. Result: Drivers could save up to 35 min.

news.mit.eduParking-aware navigation system could prevent frustration and emissionsBy minimizing the need to drive around looking for a parking spot, this technique can save drivers up to 35 minutes — and give them a realistic estimate of total travel time.

What's an energy-efficient way to shovel snow? Introducing eco-shoveling*, a two-stage technique to handle snow accumulation, while conserving energy in preparation for more shoveling later... Built to withstand three winter storms.

Cathy Wu reposted

​New research from @cathywu.bsky.social et al. confirms something I've long suspected: Navigation apps could save their users a lot of time — and tilt travel decisions toward transit and biking — if they showed users how long it takes to find parking.

news.mit.eduParking-aware navigation system could prevent frustration and emissionsBy minimizing the need to drive around looking for a parking spot, this technique can save drivers up to 35 minutes — and give them a realistic estimate of total travel time.

Have you noticed how navigation apps include walking & waiting for public transit, but excludes parking & walking for driving? After being late a few times 😅, we finally did. We got curious: what if these apps account for parking?

Our resulting paper, Probability-Aware Parking Selection, shows that doing so can improve travel time estimates, save drivers time by routing them to parking, and make for fairer comparisons across transportation modes! And this work is featured today on the MIT Home Page!

Join us in our mission to use 𝗔𝗜/𝗠𝗟 𝘁𝗼 𝘁𝗮𝗰𝗸𝗹𝗲 𝗵𝗮𝗿𝗱 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 and to enable 𝗯𝗲𝘁𝘁𝗲𝗿, 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲-𝗱𝗿𝗶𝘃𝗲𝗻 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗶𝗻 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘀𝗼𝗰𝗶𝗼𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. 🗓️ Applications are reviewed 𝗼𝗻 𝗮 𝗿𝗼𝗹𝗹𝗶𝗻𝗴 𝗯𝗮𝘀𝗶𝘀, starting immediately. 🔗 Details + how to apply: cathywu.github.io/prospective

cathywu.github.ioProspective MembersFaster, evidence-based decisions for sociotechnical systems

This project was 4 years in the making and it's finally out! We found that controlling vehicle speeds to mitigate traffic across a city can cut carbon emissions between 11 and 22 percent. To do this, we used deep reinforcement learning to optimize one million eco-driving scenarios. 🚗🤖🧠

View full thread

This work would not have been possible without lead author @vindulaj.bsky.social and collaborators Baptiste Freydt, Ao Qu, Cameron Hickert, Edgar Sanchez, Catherine Tang, Mark Taylor, Blaine Leonard, as well as the financial support of UDOT, NSF, and Amazon AWS, and support from MIT SuperCloud.

This project was 4 years in the making and it's finally out! We found that controlling vehicle speeds to mitigate traffic across a city can cut carbon emissions between 11 and 22 percent. To do this, we used deep reinforcement learning to optimize one million eco-driving scenarios. 🚗🤖🧠

Cathy Wu reposted

A new study led by Prof. Cathy Wu and colleagues reveals that eco-driving measures, such as dynamically adjusting vehicle speeds to reduce stopping and excessive acceleration, can cut carbon emissions between 11 and 22 percent. news.mit.edu/2025/eco-dri...

news.mit.eduEco-driving measures could significantly reduce vehicle emissionsImplementing co-driving techniques, like the use of intelligent speed controls to mitigate congestion at traffic lights, can significantly reduce intersection carbon dioxide emissions without…

We’ve got an awesome workshop tomorrow at RLC on real-world RL! Also, I’m moderating the panel, so let me know if you have questions about applying RL. 🤖🧠🤖

RL for Real Systems @RLC2025@rl4rsworkshop.bsky.social

📍 The RL4RS Workshop is happening tomorrow at University of Alberta, as part of @rl-conference.bsky.social. Join us for a focused day on real-world applications of Reinforcement Learning. 🗓️ Full Schedule: rl4rs.github.io/RL4RS/schedu... We hope to see you there.

Cathy Wu reposted

Hiring a postdoc to scale up and deploy RL-based planning onto some self-driving cars! We'll be building on arxiv.org/abs/2502.03349 and learn what the limits and challenges of RL planning are. Shoot me a message if interested and help spread the word please! Full posting to come in a bit.

arxiv.orgRobust Autonomy Emerges from Self-PlaySelf-play has powered breakthroughs in two-player and multi-player games. Here we show that self-play is a surprisingly effective strategy in another domain. We show that robust and naturalistic drivi...

This is soooooo fun and so blazingly fast.

Obsidian@obsidian.md

Obsidian 1.9.2 (early access) is available with improvements for Bases: - Formula syntax is more expressive and powerful. - Added number of results in the current view. - Table cells with long text expand to show the entire content when selected. - Operator dropdown for filters is now searchable.

Feeling like a fish learning about water. Insightful thread

D. Hicks@danhicks.bsky.social

Because science typically has practical aims, the standards of good science need to go *beyond* truth. Just exemplifying those epistemic values at the top of the thread isn't good enough. Science should be "fit-for-purpose." 6/ link.springer.com/10.1007/s131...

I’m pretty shocked. I was just appointed to a serve on a standing committee in April. TRB has definitely helped me find my footing in the field.

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Science is a journey not a destination.

Berna Devezer@devezer.bsky.social

Lastly to round up the 🧵: Science works doesn't mean every scientific study produces a fact ready for public consumption. Scientific work is incremental, iterative, nonlinear, slow, imperfect, relentless. Continuous improvement upon what was. Science works not despite but because of many failures.

Great advice in here. “Aim to have such a high growth rate that you cringe looking back at yourself a year ago.”

This will mean 75% fewer NSF grants to award…

Dan Garisto@dangaristo.bsky.social

Looks like they'll achieve that in part by funding about a quarter of the grants they are currently funding. 7% success rate is harrowing stuff. If you're not at a top school, will you even bother applying?