Pillar
Sustainability
Reducing the energy, carbon, and resource costs of artificial intelligence.
Our Mission
Open Engineering for Ambitious Research
AI For Tomorrow is an open-source research lab. We build shared infrastructure, conduct original research, and support high-impact projects led by others.
Our engineers and researchers create reusable platforms, benchmarks, datasets, and tools that make ambitious work easier to start, evaluate, and scale.
We use that infrastructure to pursue our own questions across AI sustainability, safety, and science, with an emphasis on transparent methods and reproducible results.
We also contribute technical expertise and operational support to aligned projects led by researchers and institutions in the broader community.
See the latest challenges we're tackling here.
Our History
From Dorm Rooms to Global Impact
AI For Tomorrow was started in casual dorm-room conversations and extra-curricular hacking at the University of Pennsylvania in 2024 between close friends. Since then, we have formalized our organization, expanding our partnerships and team post-graduation.
Today, we are a 100% volunteer open-source lab of engineers and researchers with broad interests across AI safety, sustainability, science, and research infrastructure.
Our Focus
Four Pillars
Our work is organized around four connected tracks spanning research, engineering, and shared infrastructure.
Pillar
Safety
Advancing dependable AI systems that are developed and used responsibly.
Pillar
Science
Using AI to accelerate rigorous discovery in fields with meaningful public impact.
Pillar
Research Infrastructure
Building open systems and shared tools that accelerate discovery in high-impact fields.
Selected Work
Projects We Build and Support
Carbon Connect
U.S. NSF grant to rethink the future of sustainable computing through research on efficient algorithms and datacenter operations.
Parallex
A democratized, incentivized, community-driven scientific computing framework powered by idle compute.
MLCommons Algorithmic Efficiency
Infrastructure for measuring improvements in neural-network training algorithms in collaboration with MLCommons.
Meet Our Leadership
Andy Liu
Co-Founder, Executive Director
Andy obtained his undergraduate degrees in electrical engineering and business from the University of Pennsylvania's Jerome Fisher Program in Management & Technology. His professional background spans roles at Google and DeepMind, and he currently works on neural networks for trading in NYC. His broader research interests focus on developing efficient, scalable AI systems.
Matthew Dong
Co-Founder
Matthew graduated from the University of Pennsylvania with degrees in Computer Science and Operations, and currently works as a quantitative trader in NYC. He has a longstanding interest in technology education and utilizing technology for social good. His past experiences include researching AI applications in medical imaging and health inequity along with leading student teams to build software solutions for nonprofits at Hack4Impact Penn.
Andrew Yang
Science Lead
Andrew is a medical student at The Warren Alpert Medical School of Brown University and a computational researcher. He earned an Sc.B. in Applied Mathematics and Computer Science and an A.B. in Biology from Brown, and his research uses statistics and machine learning to draw insights from biomedical data. His broader interests include surgery and oncology.