University Research Collaboration

𝗔𝟮𝗔 (𝗔𝗴𝗲𝗻𝘁-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁) 𝗮𝗻𝗱 𝗠𝗖𝗣 (𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) are two emerging protocols designed to facilitate advanced AI agent systems, but they serve distinct roles and are often used together in modern agentic architectures. 𝗛𝗼𝘄 𝗧𝗵𝗲𝘆 𝗪𝗼𝗿𝗸 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿 Rather than being competitors, 𝗔𝟮𝗔 𝗮𝗻𝗱 𝗠𝗖𝗣 𝗮𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝗿𝘆 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 that address different layers of the agent ecosystem: • 𝗔𝟮𝗔 is about agents collaborating, delegating tasks, and sharing results across a distributed network. For example, an orchestrating agent might delegate subtasks to specialized agents (analytics, HR, finance) via A2A25. • 𝗠𝗖𝗣 is about giving an agent (often an LLM) structured access to external tools and data. Within an agent, MCP is used to invoke functions, fetch documents, or perform computations as needed.    𝗧𝘆𝗽𝗶𝗰𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: • A user submits a complex request. • The orchestrating agent uses 𝗔𝟮𝗔 to delegate subtasks to other agents. • One of those agents uses 𝗠𝗖𝗣 internally to access tools or data. • Results are returned via A2A, enabling end-to-end collaboration25.    𝗗𝗶𝘀𝘁𝗶𝗻𝗰𝘁 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 • 𝗔𝟮𝗔 𝗲𝘅𝗰𝗲𝗹𝘀 𝗮𝘁:   Multi-agent collaboration and orchestration   Handling complex, multi-domain workflows   Allowing independent scaling and updating of agents   Supporting long-running, asynchronous tasks54 • 𝗠𝗖𝗣 𝗲𝘅𝗰𝗲𝗹𝘀 𝗮𝘁:   Structured tool and data integration for LLMs   Standardizing access to diverse resources   Transparent, auditable execution steps   Single-agent scenarios needing a precise tool    𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗔𝗻𝗮𝗹𝗼𝗴𝘆 • 𝗠𝗖𝗣 is like a 𝘶𝘯𝘪𝘷𝘦𝘳𝘴𝘢𝘭 𝘤𝘰𝘯𝘯𝘦𝘤𝘵𝘰𝘳 (USB-C port) between an agent and its tools/data. • 𝗔𝟮𝗔 is like a 𝘯𝘦𝘵𝘸𝘰𝘳𝘬 𝘤𝘢𝘣𝘭𝘦 connecting multiple agents, enabling them to form a collaborative team.    𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 • 𝗔𝟮𝗔 introduces many endpoints and requires robust authentication and authorization (OAuth2.0, API keys). • 𝗠𝗖𝗣 needs careful sandboxing of tool calls to prevent prompt injection or tool poisoning. Both are built with enterprise security in mind.    𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 • 𝗔𝟮𝗔: Google, Salesforce, SAP, LangChain, Atlassian, Cohere, and others are building A2A-enabled agents. • 𝗠𝗖𝗣: Anthropic (Claude Desktop), Zed, Cursor AI, and tool-based LLM UIs.   Modern agentic systems often combine both: 𝗔𝟮𝗔 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿-𝗮𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻, 𝗠𝗖𝗣 𝗳𝗼𝗿 𝗶𝗻𝘁𝗿𝗮-𝗮𝗴𝗲𝗻𝘁 𝘁𝗼𝗼𝗹 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻. This layered approach supports scalable, composable, and secure AI applications.

A 12-year-old girl in Vietnam has been declared cancer-free after fighting an aggressive form of leukemia - a victory that would have seemed impossible just years ago. She had exhausted conventional treatments, including a bone marrow transplant. When options ran out, hope came from a cutting-edge therapy: CAR-T, a “living drug” that transforms a patient’s own immune system into a precision weapon against cancer. What makes this breakthrough even more extraordinary is global collaboration. Her immune cells were harvested locally, sent across borders to Taiwanese specialists for genetic reprogramming, and returned to her body to hunt down the disease. Science, expertise, and care converged across countries - and lives were changed. This story is a lesson for healthcare and leadership alike: bold innovation, relentless perseverance, and seamless cooperation can turn the impossible into reality. In our work, in our teams, in our lives, the greatest breakthroughs come when we combine vision with courage, and knowledge with collaboration. #Healthcare #Innovation #GlobalHealth #Oncology #Leadership #Collaboration #Breakthroughs #PatientCare

The Most Overlooked Partnership in Higher Education: Financial Aid + Registrar/Advising Too often, institutions operate in silos yet student success lives in the spaces between our departments. One of the most critical, and most overlooked, partnerships is between the Financial Aid Office and the Registrar/Academic Advising. When this relationship is strong, students thrive. When it’s weak, students struggle. Here’s some of the reasons why this connection matters: 1. Enrollment Status = Aid Eligibility Credits dropped? Program changed? Withdrawals? The registrar and advising know first, but financial aid is responsible for recalculating eligibility, Pell, loans, SAP, R2T4, and compliance. Real-time communication protects students and the institution. 2. Proactive Advising Prevents Financial Crisis Advisors guide academic paths. Financial aid sees the funding horizon. Together, they can warn students before a change impacts aid, debt, or completion. 3. Timely Data = Timely Disbursement If course loads or program structures aren’t aligned, disbursements get delayed. Students don’t see departments, they see “My school didn’t give me my aid on time.” Integrated processes = student trust and institutional credibility. 4. Shared Ownership of SAP and Retention SAP isn’t just a financial aid policy, it’s an academic performance metric. Advisors help students get back on track. Financial aid ensures compliance and access. Success happens when both offices wrap support around the student. 5. Completion and Graduation Depend on Us Working Together Registrar verifies degree progress. Advising keeps students on path. Financial aid helps them afford to stay on the path. Access without completion is not enough, our collaboration is the bridge. When Financial Aid, Registrar, and Academic Advising operate as one student success ecosystem, we don’t just process paperwork, we change lives. We move beyond transactions into transformation. We don’t just enroll students, we graduate them and we do it with accuracy, empathy, and integrity. Because student success isn’t a department. It’s a partnership.

🗞️ Just out! Latest from our NATO Strategic Communications Centre of Excellence ! “Democratising Data Integration” 🔹Examines the need for standardised data integration and communication protocols in NATO’s strategic information environment. 🔹 Core argument : while advanced data processing tools exist, the lack of standardised integration protocols limits efficiency, security, and rapid decision-making. 🔹Highlights the challenges of fragmented data systems, interoperability issues, and inconsistent data-sharing methodologies across allied organisations. Key Challenges 1. Metadata Standardisation – Inconsistencies in metadata structures lead to misinterpretations and operational inefficiencies. 2. Security Classifications – Differing classification methods create access restrictions, limiting data-sharing effectiveness. 3. Institutional Divergence – NATO allies use various data-sharing protocols, impeding interoperability. 4. Technical Expertise Gaps – The shortage of skilled personnel slows the adoption of modern integration frameworks. 5. Resource Constraints – Budgetary limitations restrict the transition to scalable and secure data systems. 6. Privacy and Compliance Issues – Conflicting regulations (e.g., GDPR) create legal and operational barriers. Proposed Solutions 🔹The report proposes adopting standardised communication protocols to ensure seamless interoperability. Frameworks like Federated Mission Networking (FMN) and VAULTIS are highlighted as potential models for structured data sharing. AI-driven solutions, automated classification systems, and improved governance mechanisms are recommended to enhance operational efficiency. Standardisation would lead to: 🔹Improved Strategic Communications – Faster, more reliable data-driven decision-making. 🔹Operational Efficiency – Reduced manual processing, better crisis response. 🔹Cost-Effectiveness – Lower integration costs through streamlined interoperability.

Don't go it alone - collaborate to deliver global impact with your research! Delighted to share findings from our newly published pilot-scale study on CO₂ capture heat integration. It's exciting not only because of new approach to reducing the reboiler duty by 6% and cooling duty by 24%, resulting in operating cost savings of CO2 capture. It's exciting because it proves that collaboration is essential for credible, impactful research. Our team brought together multi-institutional expertise, industrial partners, and real-world site access on a coal-fired power plant. This work was possible because this collaboration enabled: - Access to infrastructure - Operating a mobile pilot on a live power plant requires partnerships beyond any single lab. - Data rigour - Validating marginal energy gains demanded cross-disciplinary expertise, including thermodynamics, advanced data reconciliation, and process engineering. - Industrial validation - Co-developing with site operators built credibility and practical insight from day one. - Diverse expertise - Chemistry + engineering + simulation + field operations. Individual researchers miss insights that teams can easily identify. The lesson: Impact = great ideas + rigorous execution + real-world validation. Collaboration is how you deliver all three. If you're pursuing energy research with genuine traction, treat collaboration as a core strategy, not optional. Build networks early. Your best work will come from teams you haven't yet assembled. #science #research #scientist #researcher #professor #phd #CCUS #engineering

I am very excited to share a new preprint where we entirely de novo #designed proteins that are able to neutralise 🐍 #snake #venom #toxins in vivo. 📎 You can find the preprint here: https://lnkd.in/dzF-pyDN This is the result of a #collaboration with an amazing group of people. I want to particularly thank Susana Vazquez Torres and David Baker for their commitment to this project! However, many more brilliant scientists were key to this project's success: Melisa Benard Valle, PhD, Stephen Mackessy, Stefanie Menzies, Nicholas Casewell, Shirin Ahmadi, Nick J. B., Edin M., Isaac Sappington, Max Overath, Esperanza Rivera de Torre, Jann Ledergerber, Andreas Laustsen-Kiel, Kim Boddum, Asim Bera, Alex Kang, Evans Brackenbrough, Iara Aimê Cardoso, Edouard Crittenden, Rebecca Edge, Justin Decarreau, Robert Ragotte, Arvind Pillai, mohamad abedi, Hannah Han, Stacey Gerben, Analisa Murray, Rebecca Skotheim, Lynda Stuart MD PhD, Lance Stewart, and Thomas Fryer. It's amazing what can be done over the course of a year when you effectively collaborate across institutions (DTU Bioengineering, University of Washington, University of Northern Colorado, Liverpool School of Tropical Medicine, Massachusetts Institute of Technology), disciplines, and countries. 🌍🔬 It's incredible how #biologics discovery has been revolutionised by #AI tools such as #RFdiffusion. 🤖 Being able to detect high affinity, epitope specific, stable, and neutralising binders out of 100 or less tested designs, given nothing more than a target structure is game changing (we demonstrate that both experimental or AF2 predictions can work). This is just the first step towards unlocking the full potential that #generativeAI brings to therapeutics and in particular #neglected #diseases such as #snakebite. 🧬💉 Institute for Protein Design, University of Washington, DTU - Technical University of Denmark

What if today’s students didn’t just know how to use AI, but were AI-fluent, building with it, shaping it, and bringing those skills into the workforce? What if some of the world’s oldest and most extensive archival libraries were open to anyone, anywhere, in any language — whether they read the text or needed to hear it spoken? What if we started to think about how AI could unlock the freedom to learn and the freedom to access knowledge in new ways? These possibilities, including the freedom of knowledge and the freedom to learn, are being pursued by NextGenAI — a first-of-its-kind research consortium OpenAI launched today with 15 leading universities and research institutions across the US and abroad. The goal? Use AI to put tools in the hands of researchers to accelerate breakthroughs in science, medicine, and education that will help everyone As Dr. Benjamin Talton, Director of the Moorland-Spingarn Research Center of Howard University, one of the NextGenAI partners, put it: “The business of universities is planning for the future… You can’t plan unless you know the tools you’ll have at your disposal” That’s why as part of NextGenAI, OpenAI is committing $50 million in research grants, compute funding, and API access to support students, educators, and researchers as they push the frontiers of knowledge NextGenAI’s founding members—who reflect an incredible mosaic—include: Caltech, the California State University system, Duke University, the The University of Georgia, Harvard University, Howard University, Massachusetts Institute of Technology, University of Michigan, University of Mississippi, The The Ohio State University, University of Oxford, Sciences Po, Texas A&M University, as well as Boston Children's Hospital, the Boston Public Library, and OpenAI  NextGenAI will drive impact in three key areas:  1) Accelerate frontier research breakthroughs 2) Prepare the next generation of students and learners to be AI-literate 3) Empower universities and libraries with AI to enhance operations and learning possibilities As our CEO Sam Altman has written in his Three Observations, AI could be the next transistor — a breakthrough that scales across every sector of the economy, seeping into every corner of our lives. As we enter The Intelligence Age, I am excited to see how our NextGenAI partners use AI to make breakthroughs that will help improve the lives of all of us A huge thank you to NextGenAI founding partners, and my colleagues who made this possible — they are forces of nature and together a force multiplier when it comes to delivering on OpenAI’s purpose to build AI that serves everybody: Jasmyn Samaroo, Michelle (Bailhe) Fradin, James Donovan, Chris Orsinger, Brianna Bower, adam koppel, Elizabeth Wilner, Amy Custalow Wendling, Dani Westbrook, Andy Brown, Austin Bates, Leah Belsky, and Brad Lightcap https://lnkd.in/eng7ikVU

Very promising! A new open-source platform for research on Human-AI teaming from Duke University uses real-time human physiological and behavioral data such as eye gaze, EEG, ECG, across a wide range of test situations to identify how to improve Human-AI collaboration. Selected insights from the CREW project paper (link in comments): 💡 Comprehensive Design for Collaborative Research. CREW is built to unify multidisciplinary research across machine learning, neuroscience, and cognitive science by offering extensible environments, multimodal feedback, and seamless human-agent interactions. Its modular design allows researchers to quickly modify tasks, integrate diverse AI algorithms, and analyze human behavior through physiological data. 🔄 Real-Time Interaction for Dynamic Decision-Making. CREW’s real-time feedback channels enables researchers to study dynamic decision-making and adaptive AI responses. Unlike traditional offline feedback systems, CREW supports continuous and instantaneous human guidance, crucial for simulating real-world scenarios, and making it easier to study how AI can best align with human intentions in rapidly changing environments. 📊 Benchmarking Across Tasks and Populations. CREW enables large-scale benchmarking of human-guided reinforcement learning (RL) algorithms. By conducting 50 parallel experiments across multiple tasks, researchers could test the scalability of state-of-the-art frameworks like Deep TAMER. This ability to scale the study of the interaction of human cognitive traits with AI training outcomes is a first. 🌟 Cognitive Traits Driving AI Success. The study highlighted key human cognitive traits—spatial reasoning, reflexes, and predictive abilities—as critical factors in enhancing AI performance. Overall, individuals with superior cognitive test scores consistently trained better-performing agents, underscoring the value of understanding and leveraging human strengths in collaborative AI development. Given that Humans + AI should be at the heart of progress, this platform promises to be a massive enabler of better Human-AI collaboration. In particular, it can help in designing human-AI interfaces that apply specific human cognitive capabilities to improve AI learning and adaptability. Love it!

When #Technology Meets #Humanity: Research Making a Global Impact Imagine moving a robotic arm with nothing but your mind. It sounds like science fiction, but thanks to groundbreaking research—enabled by years of collaboration and dedication across institutions—it’s now a reality. At Chalmers University of Technology, we are proud to be at the forefront of this innovation in brain-computer interface (#BCI) technology. This research is not just restoring movement for individuals with paralysis—it’s restoring independence, dignity, and hope. This incredible achievement, featured in The Times and Le Monde, would not have been possible without the global research community’s collective efforts. It’s a testament to the power of science and collaboration to push boundaries and redefine what’s possible. We are also proud to announce that this work has been published in Science Magazine, marking a significant milestone in addressing some of humanity’s most pressing challenges. A heartfelt thanks to the dedicated researchers behind this breakthrough, including Dr. Giacomo Valle at Chalmers, and our incredible collaborators: University of Chicago: Ali H. Alamri, John E. Downey, Patrick M. Jordan, Anton R. Sobinov, Linnea J. Endsley, Dillan Prasad, Peter C. Warnke, Nicholas G. Hatsopoulos, Charles M. Greenspon, Sliman J. Bensmaia University of Pittsburgh: Robin Lienkämper, Michael L. Boninger, Jennifer L. Collinger, Robert A. Gaunt Northwestern University: Lee E. Miller This work exemplifies what we mean by "technology for a better world"—not innovation for its own sake, but innovation that transforms lives. Curious to learn more? Read the full article in The Times: https://lnkd.in/dFVvbg2M This is what science can achieve when curiosity and collaboration meet. What other areas of innovation do you believe will transform lives in the near future?

Here’s a truly impactful AI multi-agent application that I’m excited to share! Imagine a world where the boundaries of scientific research are pushed beyond traditional limits, not just by human intelligence but with the help of AI Agents. That's exactly what the Virtual Lab is doing! At the heart of this innovation lies large language models (LLMs) that are reshaping how we approach interdisciplinary science. These LLMs have recently shown an impressive ability to aid researchers across diverse domains by answering scientific questions. 𝐅𝐨𝐫 𝐦𝐚𝐧𝐲 𝐬𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭𝐬, 𝐚𝐜𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚 𝐝𝐢𝐯𝐞𝐫𝐬𝐞 𝐭𝐞𝐚𝐦 𝐨𝐟 𝐞𝐱𝐩𝐞𝐫𝐭𝐬 𝐜𝐚𝐧 𝐛𝐞 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐢𝐧𝐠. But with Virtual Lab, few Stanford Researchers turned that dream into reality by creating an AI human research collaboration. 𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: → The Virtual Lab is led by an LLM principal investigator agent. → This agent guides a team of LLM agents, each with a distinct scientific expertise. → A human researcher provides high level feedback to steer the project. → Team meetings are held by agents to discuss scientific agendas. → Individual agent meetings focus on specific tasks assigned to each agent. 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐚 𝐠𝐚𝐦𝐞𝐜𝐡𝐚𝐧𝐠𝐞𝐫? The Stanford team applied the Virtual Lab to tackle the complex problem of designing nanobody binders for SARSCoV2 variants. This requires expertise from biology to computer science. The results? A novel computational design pipeline that churned out 92 new nanobodies. Among these, two exhibit improved binding to new variants while maintaining efficacy against the ancestral virus. making them promising candidates for future studies and treatments. This is not just a theoretical exercise. It's a real-world application that holds significant promise for scientific discovery and medical advancements. AI isn't just a tool anymore; it's becoming a partner in discovery. Isn't it time we embrace the future of collaborative research? What do you think about the potential of AI in revolutionizing science? Let's discuss! Read the full research here: https://lnkd.in/eBxUQ7Zy #aiagents #scientificrevolution #artificialintelligence