Who should I follow to learn about AI for Science?

If you want to understand **AI for Science (AI4Science)** - where AI is used to accelerate discovery in biology, chemistry, physics, climate, materials, and mathematics - I’d follow a mix of **researchers, labs, and communicators** rather than only general AI influencers. AI4Science spans everything from scientific machine learning to foundation models for molecules, proteins, weather, and experiments. [How AI is Transforming Scientific Discovery While Keeping Humans at the Center | Stanford HAI](https://hai.stanford.edu/news/how-ai-is-transforming-scientific-discovery-while-keeping-humans-at-the-center?utm_source=chatgpt.com) ## Core AI4Science researchers to follow - Demis Hassabis - Follow for protein structure prediction, scientific agents, and large-scale AI-driven discovery. His work around Google DeepMind’s scientific efforts is central to modern AI biology. - Pushmeet Kohli - Important for AI applied to biology, mathematics, and scientific reasoning. - David Baker - Essential if you care about AI + biology. His lab pioneered computational protein design and has been deeply involved in the AI-driven protein revolution. - Yoshua Bengio - Follow for broader AI foundations and emerging ideas around AI systems that can reason and assist science. [Home | Yoshua Bengio](https://yoshuabengio.org/en?utm_source=chatgpt.com) - Christopher Bishop - A strong voice on using machine learning for scientific discovery across chemistry, physics, and biology. [Microsoft's Christopher Bishop: Scientific discovery is AI's killer application](https://www.ft.com/content/ed2acfa7-7b7f-4e3d-af28-720b6154dd02?utm_source=chatgpt.com) ## Scientific machine learning (SciML) If you’re interested in physics, engineering, climate, and simulations: - Anima Anandkumar - Neural operators, physics-informed ML, and scientific simulations. - Rose Yu - AI for spatiotemporal systems and scientific discovery. [Rose Yu Homepage](https://roseyu.com/?utm_source=chatgpt.com) - Ben Moseley - Good to follow for practical SciML work, physics-informed methods, and open educational material. [About - Ben Moseley](https://benmoseley.blog/?utm_source=chatgpt.com) ## AI + chemistry / materials / drug discovery - Regina Barzilay - AI for medicine, molecular discovery, and clinical applications. - Adji Bousso Dieng - Works on AI methods for scientific discovery, including molecules and materials. [Dieng wins NSF CAREER award for research on a new AI paradigm for scientific discovery | CS](https://www.cs.princeton.edu/news/dieng-wins-nsf-career-award-research-new-ai-paradigm-scientific-discovery?utm_source=chatgpt.com) - Marinka Zitnik - AI for biomedical discovery and network biology. ## Physics, mathematics, and AI reasoning - Kyle Cranmer - One of the best people to follow for AI + physics. - Michael Bronstein - Important for geometric deep learning and scientific representations. - Tanya Berger-Wolf - AI applied to ecological and biological science. ## Labs and groups worth following - Google DeepMind - AlphaFold, weather models, mathematics, and AI scientists. - Microsoft Research AI for Science - Large-scale AI methods for scientific problems. [Microsoft's Christopher Bishop: Scientific discovery is AI's killer application](https://www.ft.com/content/ed2acfa7-7b7f-4e3d-af28-720b6154dd02?utm_source=chatgpt.com) - Stanford Institute for Human-Centered Artificial Intelligence - Good source for AI + science discussions and research summaries. [How AI is Transforming Scientific Discovery While Keeping Humans at the Center | Stanford HAI](https://hai.stanford.edu/news/how-ai-is-transforming-scientific-discovery-while-keeping-humans-at-the-center?utm_source=chatgpt.com) - Arc Institute - Interesting intersection of AI, biology, and experimental science. ## Newsletters / channels (less technical) - Andrej Karpathy - Great for understanding modern AI systems, though not exclusively AI4Science. - Andrew Ng - Useful for ML fundamentals and industry context. - Ethan Mollick - Broader AI trends and practical implications. ## A good starter reading path 1. Read the review **“Scientific discovery in the age of artificial intelligence”** for the landscape of AI-assisted discovery. [Scientific discovery in the age of artificial intelligence](https://ai.stanford.edu/~jure/pubs/discovery-nature23.pdf?utm_source=chatgpt.com) 2. Learn the foundations: - deep learning - transformers - graph neural networks - generative models - reinforcement learning 3. Pick a science domain: - Biology → AlphaFold, protein design, genomics - Chemistry → molecular generation, synthesis planning - Physics → simulation, particle discovery - Climate → weather and Earth-system models - Materials → battery and catalyst discovery If I had to pick **10 people to start with**, I’d choose: **Demis Hassabis, David Baker, Anima Anandkumar, Kyle Cranmer, Christopher Bishop, Pushmeet Kohli, Regina Barzilay, Rose Yu, Yoshua Bengio, and Andrew Ng**.

**If you want to learn about AI for Science, follow Demis Hassabis (DeepMind, AlphaFold), Fei-Fei Li (Stanford HAI), and Andrew Ng (DeepLearning.AI). They consistently share insights at the intersection of AI and scientific discovery.**[Top 50 People in AI to Follow in 2026 (X, LinkedIn, Websites)](https://www.aitraining2u.com/blog/top-50-ai-people-to-follow-2026.html)[Top 10 AI Influencers to Follow on LinkedIn in 2026 | GenAI.Works](https://genai.works/insights/top-10-ai-influencers-to-follow-on-linkedin-in-2026)[Top 10 AI Influencers of 2026 - KDnuggets](https://www.kdnuggets.com/top-10-ai-influencers-of-2026)[Top 10 AI Influencers You Should Follow in 2026](https://thedevify.com/blog/ai/top-10-ai-influencers-2026) ## 🌟 Key Figures in AI for Science Name| Affiliation| Why Follow| Platforms ---|---|---|--- **Demis Hassabis**| CEO, Google DeepMind| Nobel laureate for AlphaFold; leads Gemini multimodal models; strong focus on AI for biology & scientific discovery.| X, LinkedIn, DeepMind site[Top 10 AI Influencers of 2026 - KDnuggets](https://www.kdnuggets.com/top-10-ai-influencers-of-2026) **Fei-Fei Li**| Professor, Stanford; Co-Director, Stanford HAI| Creator of ImageNet; advocates human-centered AI; active in AI for healthcare & scientific ethics.| LinkedIn, Stanford HAI[Top 10 AI Influencers to Follow on LinkedIn in 2026 | GenAI.Works](https://genai.works/insights/top-10-ai-influencers-to-follow-on-linkedin-in-2026)[Top 10 AI Influencers You Should Follow in 2026](https://thedevify.com/blog/ai/top-10-ai-influencers-2026) **Andrew Ng**| Founder, DeepLearning.AI; Co-founder Coursera| One of the most influential AI educators; teaches practical ML workflows; focuses on agentic AI and applied science.| LinkedIn, DeepLearning.AI[Top 10 AI Influencers to Follow on LinkedIn in 2026 | GenAI.Works](https://genai.works/insights/top-10-ai-influencers-to-follow-on-linkedin-in-2026)[Top 10 AI Influencers You Should Follow in 2026](https://thedevify.com/blog/ai/top-10-ai-influencers-2026) **Yann LeCun**| Chief AI Scientist, Meta| Turing Award winner; shares deep learning fundamentals and opinions on AGI; relevant for theoretical underpinnings of AI in science.| X, LinkedIn[Top 50 People in AI to Follow in 2026 (X, LinkedIn, Websites)](https://www.aitraining2u.com/blog/top-50-ai-people-to-follow-2026.html) **Geoffrey Hinton & Yoshua Bengio**| Foundational researchers| Both Turing Award winners; focus on deep learning and AI safety, critical for scientific applications.| X, LinkedIn[Top 50 People in AI to Follow in 2026 (X, LinkedIn, Websites)](https://www.aitraining2u.com/blog/top-50-ai-people-to-follow-2026.html) **Timnit Gebru**| Founder, DAIR Institute| Advocates ethical AI and fairness in scientific applications; important for responsible AI in research.| X, LinkedIn[Top 10 AI Influencers You Should Follow in 2026](https://thedevify.com/blog/ai/top-10-ai-influencers-2026) ## 🔑 Why These Voices Matter * **Demis Hassabis** bridges _AI and biology_, showing how models like AlphaFold transform drug discovery and protein science. * **Fei-Fei Li** emphasizes _human-centered AI_, ensuring scientific applications remain ethical and impactful. * **Andrew Ng** provides _practical workflows_ that scientists can adopt without needing to be ML experts. * **LeCun, Hinton, Bengio** give you the _foundational theory_ behind modern AI, essential for understanding how scientific models evolve. * **Gebru** ensures you stay aware of _bias and fairness_ in scientific datasets and applications. ## ⚠️ Risks & Considerations * **Hype vs. Reality**: Some influencers focus on visionary AGI discussions rather than practical science applications. Balance your feed with educators like Ng and practitioners like Hassabis. * **Bias in Science**: Following only corporate leaders (e.g., Altman, Pichai) may skew your perspective toward product strategy rather than scientific rigor. * **Ethics & Safety**: AI for science often involves sensitive data (genomics, healthcare). Voices like Gebru and Li are critical to avoid blind spots. 👉 To tailor your feed: * If you’re interested in **biology/medicine** , prioritize Hassabis and Li. * For **practical ML workflows in labs**, follow Ng. * For **theoretical grounding**, add LeCun, Hinton, and Bengio. * For **responsible AI in science**, include Gebru. Would you like me to curate a **starter list of their most recent talks, podcasts, and papers** so you can dive deeper into their work right away?

To learn about **AI for Science (AI4Science)** - the intersection of machine learning and scientific discovery (biology, chemistry, materials science, physics) - following the right researchers, labs, and platforms is essential. Here are the top people, organizations, and resources to follow: ### 1\. Pioneers & Leading Researchers * **Demis Hassabis & John Jumper (Google DeepMind):** Co-creators of AlphaFold, which revolutionized structural biology. Following DeepMind’s announcements and papers gives you a direct window into the state-of-the-art of AI in biology and materials discovery. * **Max Welling (University of Amsterdam / Microsoft Research):** A pioneer in geometric deep learning and machine learning for physics and chemistry (symmetry-based neural networks). * **Anima Anandkumar (Caltech / formerly NVIDIA):** Known for her work on neural operators and AI for solving complex partial differential equations (PDEs) in fluid dynamics and weather forecasting (e.g., FourCastNet). * **Regina Barzilay & Tommi Jaakkola (MIT):** Leading figures in machine learning for drug discovery and molecular design. * **Alan Aspuru-Guzik (University of Toronto / Vector Institute):** A massive voice in AI-driven materials science, quantum computing, and automated chemistry (self-driving laboratories). ### 2\. Top Research Labs & Institutes * **Google DeepMind (AI for Science team):** Continues to push boundaries from protein folding (AlphaFold) to materials discovery (GNoME). * **Microsoft Research AI for Science:** Based in labs globally, they publish heavily on AI for molecular simulation, quantum chemistry, and atmospheric modeling. * **The Vector Institute & Mila:** Major Canadian hubs driving foundational AI research with a strong focus on scientific applications. * **Broad Institute (MIT and Harvard):** Excellent for learning about the practical intersection of machine learning, genomics, and biomedicine. ### 3\. Key Learning Communities & Newsletters * **DeepLearning.AI / Andrew Ng:** Regularly features updates, short courses, and breakdowns on specialized AI trends, including applications in healthcare and science. * **The Gradient & arXiv Sanity / AlphaXiv:** Excellent platforms to track breakthrough preprints in fields like q-bio (Quantitative Biology) and physics. * **Substack/X Accounts to Watch:** Follow researchers and science journalists focusing on computational biology and biopharma tech (such as Lila Ibrahim or various lab heads at OpenAI and Anthropic who work on frontier science benchmarks). Are you interested in a specific branch of AI for Science, such as drug discovery, materials science, or climate modeling?

To learn about **AI for Science** - the intersection of machine learning and scientific discovery like biology, chemistry, and physics - you should follow **pioneering researchers, institutional labs, and cross-disciplinary leaders** who regularly share breakthroughs, papers, and insights. Key Researchers and Leaders to Follow * * * ** _Demis Hassabis_** (CEO of Google DeepMind): Co-creator of AlphaFold, leading the charge on using AI to solve complex biological and structural protein challenges. * * * **_Max Welling_** (Professor at University of Amsterdam / Fellow at Qualcomm): A pioneer in machine learning for physics, chemistry, and geometric deep learning. * * * **_Anima Anandkumar_** (Professor at Caltech / former chief AI scientist at NVIDIA): Specializes in AI for neural operators, climate modeling, and accelerating scientific simulations. * * * **_Max Tegmark_** (Professor at MIT): Co-founder of the Future of Life Institute, working extensively on the convergence of physics and machine learning. * * * **_Andrew Ng_** (Founder of DeepLearning.AI): Excellent for fundamental machine learning concepts and tracking how broad AI capabilities expand into specialized sectors. [I tried 50 AI Courses. Here are Top 5](https://www.youtube.com/watch?v=cB95gevRLrU&t=471)[Demis Hassabis (@demishassabis) / X](https://x.com/demishassabis?lang=en) Essential Organizations and Labs * * **Google DeepMind** : The research group behind AlphaFold and GNoME (materials discovery), frequently posting major scientific milestone updates. * **Microsoft Research AI for Science** : A dedicated division focusing on molecular simulation, catalysis, and quantum chemistry breakthroughs using AI. * **PNNL (Pacific Northwest National Laboratory) AI** : US National Laboratory showcasing operational, real-world applications of AI in chemistry, grid science, and biology. * If you have a specific scientific domain in mind - such as **structural biology, climate science, or quantum chemistry** - let me know and I can share more targeted accounts or foundational reading lists.