HackAging.ai Hackathon: Building Tools for Aging Research

AI for science isn’t just a technical challenge—it’s a social one as well! Longevity Hacks are thrilled to partner with Hugging Face, the world’s leading open-source platform for machine learning, to bring practical AI tools directly into scientific research 🚀 Join our upcoming workshop to discover how to build lightweight applications and interactive demos that accelerate collaboration in longevity and life sciences. Led by Georgia Channing, ML for Science Engineer at Hugging Face, this hands-on session will show you how to: ⚡ Quickly discover, load and fine-tune state-of-the-art models 🤖 Access scientific datasets with minimal overhead 🔨 Build and share ML-powered demos to showcase your research This free, virtual workshop is part of our Women x Longevity Hackathon and is open to participants worldwide 🌍 No prior deep learning expertise required. 🔗 Sign up for the workshop: https://luma.com/pd5cu3r0 🔗 Join the Women x Longevity Hackathon: https://luma.com/wkht62b5 Thomas Wolf #AIForGood #WomenLongevity #HuggingFace #LongevityHacks

We didn’t win the hackathon. But we built something academia desperately needs. At HackAging.ai — Global Longevity Hackathon Series, our team set out to fix a quiet crisis: researchers are buried under PDFs - not insight. We built an AI pipeline that processes 100,000 papers, extracts structured data, and feeds it into LLMs for analysis. 16 days. Four people. No sleep, but a working system. We didn’t get the trophy, but we left with proof that automation can make research human again. You can’t automate curiosity, but you can accelerate discovery. Losing fast still beats waiting slow. Academia doesn’t need more papers, it needs better pipelines. Sometimes the real win is building something that should already exist. What’s one system you’d rebuild if “winning” wasn’t the goal? __________________________________________________________________ Team: Ágoston Paless, Regina, Eduard and me Track Leader: Dmitrii Kriukov Professor Support: Peter Lidsky Organized by: Alexey Strygin Vita Stepanova Ph.D. Computational Sponsor: Nebius Special credit to Ira Evdokimova, who told me about the hackathon 💕 Later this week, I’ll share a detailed breakdown of how we built the Automated Literature Analysis Pipeline — from 100K PDFs to structured insights.

Deadline extended! Submissions for the Accessibility Bias Bounty Challenge are now due November 14 at 11:59 PM ET. With Design and Data Science tracks and a $6,000 prize pool, participants are invited to build AI tools that prioritize impacted communities and join a growing community of practice dedicated to solving technical bias challenges. In partnership with the CoNA Lab at Virginia State University and Valence AI, this global challenge invites students, researchers, technologists, and people with lived experience to evaluate and redesign video conferencing and emotion AI with neurodivergent experiences in mind. Learn more: https://lnkd.in/gfdtHTij

I’m noting arXiv’s decision to stop accepting unvetted computer science preprints after a surge of low-effort, AI-generated submissions. This is a signal: uncurated information flows damage trust, derail discovery, and increase verification costs for product teams that rely on preprints for rapid innovation. For leaders, the actionable implications are clear: enforce provenance and quality gates, combine human expertise with automated triage, invest in metadata and reproducibility checks, and define KPIs that measure signal-to-noise and downstream validation costs. Treat research pipelines like product pipelines—prioritize curation, scalable governance, and feedback loops that convert raw output into reliable inputs for R&D. That approach preserves agility while protecting against wasted effort and reputational risk. I can translate this into a concrete roadmap for platform governance and research intake.

Explained simply: Biology's data revolution is here! With tools like PUPS & MORPH, we're on the brink of understanding life's programs at a new level. Causal insights meet ML innovation. This just happened: https://lnkd.in/gMHZt8gf

What if your data-driven surrogate model starts to predict poorly? We have developed a method to robustly switch back to the underlying (but expensive) physics-based model. The work "Mixing Data-Driven and Physics-Based Constitutive Models Using Uncertainty-Driven Phase Fields" is a joint work with WaiChing Sun, Iuri Rocha and Frans van der Meer. I would also like to thank TU Delft | AI for sponsoring my 3-month visit to Columbia University in New York that enabled this collaboration. The paper is open source, and all the code is available on GitHub - linked in the comments.

Predicting failure before it happens. That is the future of maintenance and the challenge now on the table. TII’s PHM Data Challenge is calling top talent to design algorithms that can uncover hidden risks, extend the life of complex systems, and keep critical assets running. Big ideas, bold coding, real impact. Visit the website to learn more: https://lnkd.in/dAVVP9dv #TIICrowdChallenge #AIInnovation #PredictiveMaintenance #PHMChallenge

For years. Researchers have dedicated countless hours to our cause. Today, we’ve made it official with a partnership with the University of Cincinnati! The AI Bio Lab, led by Kelly Cohen implements Fuzzy Logic Systems to improve responsible , explainable, and transparent AI in a world where safety has taken a second seat. We plan to lead the nonprofit world and families in need through this disruptive transformation with assurance, careful research, and input from survivors of digital harm. This partnership shows our commitment to integrating the latest research into digital safety tools and impact workaround the globe. And we’re just getting started 😎 Check out the Announcement! https://lnkd.in/gPMCQ-AM

A New Partnership to Help Vulnerable Families The AI Bio Lab is partnering with the Plunk Foundation to pilot a consent-first coordination platform that helps society’s most vulnerable access services faster, safer, and with dignity. This collaboration brings together cutting-edge AI research and community-driven innovation to address a persistent challenge: fragmented service delivery for families in crisis

For Multiply Labs, the UR Forum wasn’t just helpful, it saved them serious time. 💬 “Other people had already asked the questions we were looking for answers to, so it was really quick to get ramped up.” That’s the power of a strong community: speeding up learning, problem-solving, and innovation. Curious how they did it? Case study here 👇 https://lnkd.in/dywUckR8