Welcoming Kayan Patel to the Pillar VC team! Kayan is joining the team as Event & Operations Associate for Encode: AI for Science, by Pillar VC, bringing with him a blend of scientific expertise, community building and design. Before joining Pillar, he.... 🚀 Worked in R&D across several deeptech startups - developing technologies from biofuels to cultivated meat. 🌟 Co-founded Farrago, a grassroots community supporting hundreds of London-based artists with tools and a platform to grow their careers. ✏️ Was the inaugural MSc Bio-Integrated Design scholar at UCL's Bartlett School of Architecture, having earned a BSc in Biochemistry. We're thrilled to have him as our third full-time employee on the Pillar UK team - alongside Leah Elizabeth Morris and Leone Baron.
Encode: AI for Science, by Pillar VC
Venture Capital and Private Equity Principals
A flagship Pillar VC initiative for the next generation of founders, scientists, and leaders in AI for Science.
About us
A Pillar VC initiative, we believe the next AI revolution is science. Pillar VC is a seed-stage venture capital firm. We invest at inception in companies solving the world's greatest challenges. 50% of our portfolio has roots in university research. The Encode Fellowship, powered by the Advanced Research + Invention Agency (ARIA) and Department for Science, Innovation and Technology (DSIT), brings together the brightest minds in AI with pioneering scientific research across the UK. Over 12 months, Encode Fellows are embedded in leading labs to advance frontier research, apply AI to accelerate discovery, and help launch new ventures or research organisations rooted in deep tech breakthroughs. Register early interest in Cohort 2: https://encode.pillar.vc/ Subscribe: https://substack.com/@encodeaiforscience
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https://encode.pillar.vc/
External link for Encode: AI for Science, by Pillar VC
- Industry
- Venture Capital and Private Equity Principals
- Company size
- 11-50 employees
- Founded
- 2025
Updates
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Encode: AI for Science, by Pillar VC reposted this
Climate models often have dozens, if not hundreds, of parameters that control the model's behaviour. Tuning these parameter to ensure the model's behaviour is realistic is a bit of an art form in itself. In our new paper "Calibration of climate model parameterizations using Bayesian experimental design", we show how to tune climate models in a statistically principled way using ideas from Bayesian experimental design: https://lnkd.in/epBZtVhe. Work done with the amazing Tom Rainforth and Duncan Watson-Parris. Quick summary: A popular workflow to explore the parameter space of climate models is to run a perturbed parameter ensemble (PPE) with latin hypercube sampling (LHS) and then train a cheap emulator model on the PPE data. The cheap emulator model can then be used to investigate what parts of the parameter space are consistent with observational constraints. However, using LHS to explore the parameter space can be wasteful because it is ignoring all information about the observational constraints. Framing the calibration process as a Bayesian experiment design (BED) problem allows us to derive principled algorithms for finding climate model parameters that leverages the observational data. We demonstrate that BED algorithms can more quickly narrow down the set of plausible input parameters that is consistent with observational data compared to traditional LHS sampling. There's lots more work to be done to have principled workflows to calibrate climate models to observational data but Bayesian experimental design provides a rigorous foundation for model calibration! Link for code to reproduce the experiments in the comments. Please reach out if you're interested in using it!
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Encode: AI for Science, by Pillar VC reposted this
The final paper from my PhD has been published in The Lancet Digital Health! This mixed-methods study assessed the impact of our AI clinical decision support on clinicians’ antibiotic prescribing decisions in a simulated environment. My biggest takeaways on implementing AI in healthcare are: - Trust and ease of use are the main drivers behind adoption, with trust built primarily through evidence and experience 🤝 - AI explanations often play a minor role and are frequently ignored at the point of use 👀 - Understanding impact and behaviour change is critical, as biases can easily be amplified and effects unexpected ⚠️ You can read the paper here: https://lnkd.in/gpF-YRJ4 When I started building this project, coding agents weren’t really a thing, so I taught myself some front-end dev for the prototype. It’s amazing how far AI capabilities have evolved since early 2023. Following this work, we progressed the research further, which led to the launch of Steward.ai. We hope to continue to progress this technology towards real-world use with the Fleming Initiative. Thanks to Timothy Miles Rawson, Mark Gilchrist, Alison Holmes, Pantelis Georgiou, Imperial College London, Imperial College Healthcare NHS Trust, AI for Healthcare Centres (AI4Health) and all those who participated for their support. #DigitalHealth #AIinHealthcare #ClinicalDecisionSupport #AntimicrobialStewardship #HealthTech #MedicalAI #AI #ArtificialIntelligence #ClinicalResearch
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📸 Conference Postcard: AGU 2025 Encode Fellow Philine Lou Bommer shared her work on an open source toolbox for informed risk assessment for solar radiation management strategies using DL and XAI. Part of her AI for Safe Climate Cooling project, Philine's poster session was 4 hours of feedback, discussion and meeting new collaborators. Learn more about Philine's project here: https://lnkd.in/eHPCMZFx
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Welcoming Chris Anderson as an Encode Advisor - Chris brings a rare mix of engineering, ecosystem and editorial expertise. Former Editor-in-Chief of WIRED, founder of The Linux Foundation's Dronecode and the DIY Drones and DIY Robocars communities, and CTO at Kittyhawk after scaling 3D Robotics, he’s built platforms where ambitious hardware and software meet. A Senior Advisor at Renaissance Philanthropy, Chris is now focused on funding AI for Science and building at the intersection of AI and Advanced Manufacturing - aligning with our mission to take scientific discovery from lab to public. He's already had a large impact on our first cohort, we're excited for what's next. Welcome, Chris 🚀
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Our Winter Salon had a core conviction: discovery must lead to construction. Bringing together 150 founders, scientists, builders, and policymakers at the British Library, the night featured mulled wine, postcards from 2025, our Encode Fellows and Faculty, and a resounding thank you to the Encode community. Built by Pillar VC, powered by Advanced Research + Invention Agency (ARIA) and Department for Science, Innovation and Technology - view our year in review below.
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Looking back on 2025, one number matters most. 18 important scientific challenges, with 18 ambitious plans to tackle them, by 18 Encode Fellows From Salons to Hackathons, Summits to Workshops - in our first year we've connected a global community of builders in AI for Science. At core of this community are the 18 incredible AI / ML engineers who joined us full-time in September as Cohort 1. 📸 View their experience, and progress, via the link in the comments. From Pillar VC - thank you to the Advanced Research + Invention Agency (ARIA) and the Department for Science, Innovation and Technology for partnering with us, and to our network of Encode Advisors and Faculty for supporting these fellows.
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Encode: AI for Science, by Pillar VC reposted this
Merry Christmas! We are excited to release ProFam-1, our new open-source protein-family language model. ProFam generates protein variants conditioned on one or more example sequences. It produces sequences with low identity that nonetheless retain the predicted structure of the reference family. Crucially, ProFam conserves key functional residues (such as enzyme active sites) even when prompted with only a single sequence. Beyond generation, ProFam-1 excels at zero-shot fitness prediction. You can rank candidate sequences by their likelihood of function simply by prompting the model with a single/few known functional variants (no fine-tuning required). ProFam-1 outperforms much larger models like ESM on zero-shot fitness prediction, supports insertions and deletions, and is efficient enough to run on your laptop. We're releasing the full stack open source, including model, training and inference code and training data: 📖 Read the preprint: https://lnkd.in/e5Be94bd 💻 Code & Weights: https://lnkd.in/eAe4h6_A 📂 Data: https://lnkd.in/endN4RaD It was a great pleasure to work on this with these collaborators: Alex Hawkins-Hooker, Micha Livne, Weining Lin, David Miller, Christian Dallago, Nicola Bordin, Brooks Paige, Burkhard Rost, Christine Orengo & Michael Heinzinger
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Congratulations to Encode: AI for Science advisor Samuel G. Rodriques, and the FutureHouse / Edison team - including Pillar VC Partner Tony Kulesa!
Science is too slow. At Edison, we are integrating AI Scientists into the full stack of research, from basic discovery to clinical trials. We want cures for all diseases by mid-century. We have raised a $70M seed to get started. Join us. We need cracked software engineers who want to work on finding cures rather than selling ads and generating slop. If you’re reading this, you’re probably a candidate. We need brilliant AI researchers who want to figure out how AI will accelerate real-world science. We need scientists and researchers with deep expertise in biology, biotech, and pharma who want to figure out how to integrate AI deeply into scientific workflows, from ideation to experimentation, and how to measure success or failure. We need extraordinarily talented generalist operators across BD, sales, product management, and partnerships who can focus on getting our tools into the hands of pharmaceutical companies. If any of these roles sound like you, get in touch. ----- We are also expanding access to our platform. Our goal is to accelerate science writ large. To that end, we will continue to give academics and students 650 credits/mo indefinitely. I can’t promise we’ll keep this up forever, but we will try. Kosmos will still cost 200 credits, and the other agents (Analysis, Literature, etc.) will cost 1 or 2 credits. All paid users will have access to our regular agents, like our Analysis agent, Literature agent, and so on, for free via the UI. API access will still be paid, and users without a paid subscription will continue to get 10 credits per month for those agents. Our $200/mo subscription for 650 credits/mo is staying in place for now, but might be phased out at our next major product update. Along the lines of accelerating science, we’re also doing a major release of PaperQA today, our flagship open source literature agent, as part of our commitment to open science. In the short run, expect major improvements to Kosmos, including the ability to automatically access data, the ability to steer its exploration, and the ability to converse directly with its world model. In the long run, expect exponentially increasing rates of scientific discoveries, in biology and elsewhere. Our round is led by Triatomic Capital, Spark Capital, and a major US institutional biotech investor. We are also joined in this round by existing investors Pillar VC and Susa Ventures, two exceptional early-stage funds who backed us at founding, along with Striker Venture Partners, Hawktail VC, Olive VC, and a host of exceptional angels that includes famous AI researchers, the CEOs of multiple frontier AI labs, and leadership of major biotech and pharma companies. Try Kosmos on our platform: https://lnkd.in/g5h6bZ7B Check out some of our roles: https://lnkd.in/gaDHHdf4 If you don't see one that resonates but think you'd be a good fit, get in touch.
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