Lily Xu

Initiative is the brainchild of @daviddao.org and run through @gainforest.earth!

With @milliechapman.bsky.social, Maria João Sousa, Livia Kalossaka, and Sejal Rekhan

We particularly encourage proposals from
◆ researchers in the Global South
◆ practitioners with on-the-ground experience in public goods funding
◆ independent researchers
◆ those from non-traditional backgrounds

Recerts Recerts Journal of Mechanism Design for Public Goods

📜 Call for proposals (max 1,000 words) open Nov 17
🚄 Fast, four-week turnaround for review

Learn more: recerts.org/ai4pg2025

Apply as a reviewer (by Nov 14): docs.google.com/forms/d/e/1F...

Launching AI for Public Goods Fast Grants!

We'll distribute $150k to advance critical work connecting AI and public goods.

💰 $10k per project
💰 $800 reviewer compensation

PUBLIC GOODS := open source, ecosystem services, climate, urban infrastructure, communication, education, science, and more.

Nature in Numbers: Why Biodiversity Analytics Matters Now Introducing Columbia University’s  Master of Science in Biodiversity Data AnalyticsHow do we measure biodiversity? How do we bring nature data…

Event details and registration: sps.columbia.edu/events/natur...

Also with Tony Eggers, Shahid Naeem, Alex Logan, Viorel Popescu, and Jessica Thurston

Nature in Numbers: Why Biodiversity Data Analytics Matters Now

Columbia University is launching a new MS in Biodiversity Data Analytics to start in fall 2026!

Join the launch event to discuss analytics and data science for nature in business, investment, and policy.

Wed, Nov 5
6–8pm
@columbiauniversity.bsky.social in NYC or remote

Open Rank Faculty Position in Industrial Engineering and Operations Research Columbia University in the City of New York: The Fu Foundation School of Engineering and Applied Science: Industrial Engineering and Operations Research

Come be our colleague at Columbia IEOR! Open-rank (tenure-track or tenured) position with a priority deadline of November 21.

Candidates with application-oriented research are especially encouraged to apply.

apply.interfolio.com/175698

Link to application: apply.interfolio.com/172496

Applications will be evaluated on a rolling basis, with priority given to those received before October 20.

Columbia is hiring a tenure-track asst. prof. in computational ecology and evolution!

If you use or develop computational tools—AI, data science, stats—to deepen our understanding of eco/evo, evaluate nature-based solutions, or inform and manage impacts on human wellbeing, please apply!

Very happy to be part of this new paper brilliantly led by @calebscoville.bsky.social on new opportunities for stakeholder participation with dynamic management compared to static management of natural resources! 🌳🌏🙋

My lab at UCLA is hiring 1-2 PhD students this cycle!

Join us to work at the intersection of cognitive science and AI applied to pressing societal challenges like climate change.

More info about me: rachit-dubey.github.io

My lab: ucla-cocopol.github.io

Please help repost/spread the word!

The call for applications has just been released for #CV4Ecology2026!! This three-week intensive program trains ecologists and conservation practitioners to develop their own AI tools for their own data.

When: Jan 12-30, 2026
Where: SCBI @smconservation.bsky.social

Dave Thau, @sarameghanbeery.bsky.social, and I are organizing biodiversity webinar talks for @itu.int AI for Good.

Next week, I'll chat with Iadine Chades (Monash) on the promise and impact of decision AI for biodiversity.

Please share questions you'd love to ask Iadine or our upcoming speakers!

Congratulations to you Rajanie and the team! ☀️

PDF of advert, for sharing: lily-x.github.io/files/invasi...

I'm hiring for a machine learning data scientist & research assistant for summer 2025! Join me in working on a project on invasive species management, by predicting species risk and planning capture strategies. This work will be in partnership with an innovative startup that is working directly to capture environmentally destructive invasive species in the US. This project will be working with a US-based conservation partner to build predictive machine learning models and design harvest strategies for the removal of invasive animal species. The primary goal is to develop usable ML models and optimization tools to inform practical environmental decision-making on the ground. There will also be opportunities to extend this work into a research publication for a top-tier AI venue. The project would be: Paid, full-time internship for summer 2025 Possibility of extension beyond the summer (part-time or full-time) Remote, but candidates should be authorized to work in the US Supervised by Lily Xu, assistant professor at Columbia University Ideal candidate background will include: Strong background in CS, data science, and/or applied math Experience developing machine learning models Excellent coding skills in Python Excellent writing and interpersonal communication skills Genuine interest in conservation/sustainability Nice to have: background in optimization methods Nice to have: experience with GIS and geospatial data Candidates would ideally have already completed an undergraduate degree, but exceptional undergrads will also be considered. How to apply: Please send a CV and 3–5 paragraphs of your background/interests via email to <lily.x@columbia.edu> with the subject line "Application: ML for invasive species management". Applications will be reviewed on a rolling basis.

I'm hiring for a machine learning data scientist & research assistant for summer 2025!

Join me on a project on invasive species management with an innovative startup doing on-the-ground removal of environmentally destructive invasive animals.

Paid, full-time w/ possibility to extend.

Tackling Climate Change with Machine Learning ICLR 2025 Workshop: Tackling Climate Change with Machine Learning

Monday
Hall 4 #3

Climate Change AI workshop, where we have:
🧪 @yisongyue.bsky.social keynote on AI for Science
🐘 Iadine Chades keynote on AI for Biodiversity

Panels
🌏 Opportunities for ML for Climate Action in Asia
☀️ Data-Centric ML in Climate Applications

www.climatechange.ai/events/iclr2...

Sunday @ 9am
Peridot 201 & 206

Invited talk at XAI4Science workshop, where I'll be speaking on "Barriers to explainability for sustainability, and a path towards more reliable models"

xai4science.github.io

[CCAI x FCL Global] Sustainability x AI Networking Forum This satellite event of ICLR 2025 brings together individuals working at the intersection of artificial intelligence and climate change.

Friday evening @ 6:30pm

@climatechangeai.bsky.social and Future Cities Laboratory (FCL) Global, Singapore-ETH Centre satellite event

I'll be a panelist on "Data-Centric Approaches to ML for Climate Action"

www.eventbrite.sg/e/ccai-x-fcl...

Paper:
Deep RL + mixed integer programming to plan for restless bandits with combinatorial (NP-hard) constraints.

with @brwilder.bsky.social, Elias Khalil, @milindtambe-ai.bsky.social

Poster #416 on Friday @ 3–5:30pm

bsky.app/profile/lily...

Thrilled to be here in Singapore for #ICLR2025!

Find me:

Friday @ 3–5:30pm
Poster #416
RL with combinatorial actions

Friday @ 6:30pm
Panelist at CCAI x ETH event

Sunday @ 9am
Invited talk @ XAI4Science workshop

Monday all day
Hall 4 #3
Organizing @climatechangeai.bsky.social workshop

Thrilled to be co-organizing the Tackling Climate Change with Machine Learning workshop at #ICLR2025 and bringing in these fantastic keynote speakers!

Join us April 28 (in Singapore or free livestream) to hear from @yisongyue.bsky.social on AI for science and Iadine Chades on AI for biodiversity.

Are you interested in boldly reimagining how we can finance #naturerecovery at scale, beyond mainstream market mechanisms?
We are advertising a one-year postdoctoral researcher at
@naturerecovery.bsky.social at Oxford. Closing date April 30th
tinyurl.com/muebaf86
@oxfordgeography.bsky.social

Measuring Development 2025: Biodiversity on Land and at Sea Human flourishing depends on biodiversity: the animals, plants, and other living organisms whose functions make Earth habitable and whose existence holds intrinsic value. New data and applications...

🌎 Join CEGA for #MeasureDev2025: Biodiversity on Land and at Sea on April 29 at The World Bank in Washington, DC!

Explore cutting-edge biodiversity measurement approaches with leading researchers & policymakers shaping conservation strategies. #datasky

Register: cega.berkeley.edu/event/measur...

I hope to see many of you in Singapore at ICLR — find me at my poster (April 25 @ 3–5:30pm SGT), at the @climatechangeai.bsky.social workshop (April 28), or please do reach out to grab a coffee!

🇸🇬☕️🦁🏙️👩🏻‍🔬

Tackling Climate Change with Machine Learning ICLR 2025 Workshop: Tackling Climate Change with Machine Learning

I'm also co-organizing the @climatechangeai.bsky.social workshop at #ICLR2025!

Join us April 28: climatechange.ai/events/iclr2...

We're thrilled to have keynotes by @yisongyue.bsky.social and Iadine Chades on path-defining work on AI for science and AI for conservation.

We'll be livestreaming!

Poster for Reinforcement learning with combinatorial actions for coupled restless bandits

Reinforcement learning with combinatorial actions for coupled restless bandits

Paper: arxiv.org/abs/2503.01919
Code: github.com/lily-x/combi...

Join our #ICLR2025 poster session!
Friday, Apr 25 @ 3–5:30pm SGT

Our SEQUOIA method significantly improves performance above baseline approaches, which are either myopic (neglecting the sequential nature of RMABs and other MDPs) or non-combinatorial (using heuristic, iterative approaches to solve the NP-hard combinatorial constraints).

Diagram of our SEQUOIA method. Part 1: training, to learn a Q-function approximator; part 2: evaluation to find the action that maximizes the expected Q-value.

Our method combines:
— DQN: deep Q-network to approximate the value function
— MILP solving: a mixed-integer program to embed combinatorial constraints over actions

The MILP solver implicitly conducts a forward pass through the neural network to compute the expected Q-value.

Sequential planning with per-timestep combinatorial (NP-hard) constraints is hard.

Existing RL for combinatorial planning focus on
(a) only combinatorial state spaces, or
(b) RL as a heuristic solver for combinatorial optimization — but in single-shot (non-sequential) planning.