University Research Collaboration

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.

Collaborating on Credentials The future of the workforce and the future of education lie in collaborative models where industry and academia work together to create relevant, practical learning experiences. Whether through advisory boards, design challenges and projects, or comprehensive microcredential programs, these partnerships are reshaping how we prepare talent for tomorrow's workforce. On a recent podcast, sie.ag/443UxN, I connected with Michael J. Readey and Christy Bozic, PhD, PMP, CPEM to discuss the transformative power of industry-academia partnerships. Together, we have been collaborating on credentials and sustainability to improve the circular economy digital mindset. Here are some insights we discussed that every education and industry leader should consider: The Traditional Model is Evolving: The "degree-only" mindset is shifting as we recognize the growing importance of continuous, skills-based learning. With the majority of credential-seekers being full-time professionals, the demand for flexible, targeted upskilling is clear. Industry-Academia Partnerships Matter: We must continue to invest in partnerships that bridge the critical gap between classroom theory and rapidly changing workplace demands. Together, we can enable faster identification of emerging skill needs and create timely real-world learning opportunities through immersive experiences. This provides learners with early and direct industry exposure. The Rise of Microcredentials: We're seeing a trend of professionals who actively seek, learn, and collect badges and microcredentials for career progression. Agile learning formats offer just-in-time education and experience for quick adaptation to industry needs, and flexible learning paths can address immediate and targeted skill application. Learn more about what hiring managers look for, how to build industry-relevant learning pathways, and what the future holds for collaborative academic-industry relations. I remember when I started in this industry, the focus was on how we could break down the walls between CAD and CAM. There are still walls between academia and industry we must break down. The collaboration we experienced with Michael, Christy, and the University of Colorado Boulder gives me hope for a new path forward. Listen to the full episode and share your perspective below: sie.ag/443UxN.

The current lull in news about the financial position of universities does not mean that the UK’s higher education sector is not under immense pressure. Rising costs, frozen tuition fees, the decline in international students, political demands for “value for money,” and shifting workforce needs driven by AI and sustainability are all colliding to create a system that is no longer sustainable in its current form. “Radical collaboration”, a new report from KPMG and Mills & Reeve, offers a clear message: universities can’t simply do more with less. They must rethink how they work together, moving beyond institutional pride and financial firefighting to embrace bold, long-term collaboration. This isn’t about bailing out failing institutions but about reshaping the sector to deliver research excellence, broader access, stronger regional impact, and long-term resilience and the report outlines key recommendations for the future: ➡️ Recognise the current HE model is no longer sustainable ➡️ Focus collaboration on outcomes, not just cost-cutting ➡️ Define clear purpose and objectives for any partnership ➡️ Prioritise long-term strategic leadership over institutional pride ➡️ Consider a range of models from alliances to full mergers ➡️ Create the right conditions: strong leadership, aligned values, clear communication ➡️ Address cultural and regulatory barriers early ➡️ Ensure government provides enabling support (legal, financial, regulatory) ➡️ Treat radical collaboration as a proactive strategy for sector sustainability The question now is whether the sector has the leadership, political will, and strategic clarity to act or whether it will continue to delay the inevitable. #HigherEducation #UniversityStrategy #Leadership #Collaboration #Policy #HEReform #RadicalCollaboration #FutureOfHE #Universities #PublicValue

As Harvard faces deep research funding cuts, a private equity firm has stepped in with a $39M commitment to support a Harvard research lab. Could this signal a new future for how academic science is funded? The investment comes from Turkish firm İş Private Equity, which typically backs high-growth small and medium-sized enterprises (SMEs). The funding recipient is the lab of Professor Gökhan Hotamışlıgil at the Harvard T.H. Chan School of Public Health, whose research aims to develop therapies for obesity and other metabolic diseases. Broader context Private equity (PE) rarely funds basic university research directly, as it doesn’t align with traditional return-focused models. But that’s changing. New structures are emerging where PE capital supports translational or applied academic science: ▫️ New startup - İş Private Equity launched Enlila, a new biotech company created to fund Hotamışlıgil’s lab over the next 10 years. Enlila will also invest in translating the lab’s discoveries into therapeutic products. ▫️ Joint ventures - Since 2017, Deerfield Management has created university partnerships to advance early-stage therapeutics, providing capital and helping universities evaluate projects toward Investigational New Drug (IND) readiness. Recent examples include: - Hyde Park Discovery with University of Chicago ($130M, 2025) - VeritaScience with Washington University in St. Louis ($130M, 2024) ▫️ Royalty monetization - In 2023, Purdue Research Foundation received over $100M from Blue Owl Capital by selling a portion of its royalty interest in Pluvicto, a prostate cancer therapy. Yale University executed a similar deal for the drug Yervoy, turning future royalties into immediate research capital. Takeaway As the research funding landscape evolves, the capital stack for science is becoming increasingly complex. I think we’ll likely see more private equity, venture capital, and philanthropy stepping in to support bold, high-risk science in new and unexpected ways. Curious to hear your thoughts: Should private equity be stepping into early-stage science? Which research areas could benefit most from this approach?

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!

What happens when an innovation studio works hand-in-hand with a university to mobilise knowledge? That’s the challenge Studio Zao is tackling. This UK-based innovation studio helps organisations unleash their internal entrepreneurial potential, building cultures of experimentation, validating ideas rapidly, and aligning innovation efforts with real-world user needs. Now, they’ve turned their attention to universities. In this article, “How Can We Bridge the Knowledge Exchange Gap Using Principles of Productive Innovation?”, Studio Zao reframes a problem many of us in higher education know too well: our research outputs often don’t make it to the communities and businesses who could benefit most. Not because they aren’t valuable, but because our systems for knowledge exchange are fundamentally mismatched with how innovation really happens. Their proposition is simple: if universities want to deliver real-world impact, we need to learn from how the best innovators work. Studio Zao proposes three shifts: 🔹 Foster intrapreneurs within academic staff - people empowered to turn insights into impact from the bottom up. 🔹Adopt an evidence-led approach that starts with use-cases and customers, not just curiosity. 🔹Prioritise stakeholder value by speaking the language of business and meeting real-world needs. In short, they recommend applying lean, customer-centric innovation principles inside universities before research is complete, not after. They argue that if we embed this approach in how we think, teach, and fund, universities could become unmatched engines of applied, inclusive innovation. This article invites us to imagine what could happen if we stopped pushing knowledge out and started pulling partners in. #KnowledgeExchange #Innovation #HigherEducation #UniversityImpact #StudioZao #EntrepreneurialUniversities #Intrapreneurship #AcademicInnovation #InnovationStudios 👉 https://lnkd.in/g5gc69MN

The idea that government-supported university commercialisation “doesn’t produce real-world ventures” is not supported by the evidence. The evidence shows Australia has the skills and the talent to compete globally. What holds us back is that we keep shifting the goal posts and sending the team back to pre-season before it has a chance to prove itself on the field. Australia’s public research system has produced some of the most important innovation success stories in the country’s history: Cochlear, ResMed, Gardasil and many others. The life sciences examples alone should end the argument that Australian university commercialisation does not produce real-world value. 🚀 Vicebio acquired by Sanofi $1.6B - UniQuest 🚀 Spinifex Pharmaceuticals acquired by Novartis ~$700M - UniQuest 🚀 Fibrotech Therapeutics acquired by Shire ~$557M - University of Melbourne 🚀 Hatchtech acquired by Dr Reddy's Labs ~$279M - University of Melbourne 🚀 Viralytics acquired by Merck $502M - University of Newcastle 🚀 Inflazome acquired by Roche $620M - UniQuest 🚀 ResApp Health acquired by Pfizer $179M - UniQuest 🚀 Elastagen acquired by Allergan ~$95M - University of Sydney More recently, we have seen a new generation of deep-tech companies emerge from universities and public research organisations: Liquid Instruments, Quantum Brilliance, Diraq, Q-CTRL, Hysata, Morse Micro, DeteQt, Sicona and others. Case Study: Liquid Instruments 👉 Valued over $400M 👉 $70m Series C 👉 Series C involved federal, state, private and local venture capital 👉 Globally competitive in a $25B test and measurement industry 👉 CEO was PhD and Professor from the Australian National University 👉 Core product replaces entire racks with a single software defined digital box 👉 High profile clients include: NVIDIA, BYD, Lockheed Martin and Blue Origin 👉 Explicitly used on NASA's Artemis II moon mission 👉 Years of ARC Linkage and ARC Centres of Excellence funding 👉 Total funding raised across 10 rounds, ~$116.8M The Survey of Commercialisation Outcomes from Public Research reported that Australian public research institutions secured more than $1 billion in private-sector research contracts in 2023, generated $290 million in commercialisation revenue, and had a record 380 active spinouts and startups founded on public research IP. KCA’s SCOPR 2024 summary also reported that, over five years, publicly funded research organisations achieved AU$1.4 billion in commercialisation deals in Australia and created hundreds of new companies across Australia and New Zealand. It is not Australia’s research that does not work. It is not the commercialisation teams inside Australian universities. It is Australia’s innovation policy that keeps failing to stay the course.

Artificial Intelligence has become a foundational force reshaping how economies, institutions, and societies generate value. Projections that place AI’s impact in the trillions are not simply about automation or efficiency, but they point to a structural transformation across mobility, manufacturing, governance, healthcare, and education. In this context, universities can no longer function only as sites of knowledge transmission. Their relevance increasingly lies in their capacity to act as knowledge creators, solution architects, and ethical stewards of emerging technologies. Teaching AI is no longer sufficient. We must participate directly in shaping how intelligent systems are designed, deployed, and governed. This understanding underpins our recent collaboration between CHRIST (Deemed to be University) and HL Mando, a South Korea–based global leader in automotive and mobility systems, through the establishment of the IDEAL – Intelligent Data Engineering AI Lab. More than an infrastructure or symbolic partnership, this collaboration represents a shift toward sustained academic–industry co-creation, where theoretical rigour meets industrial-scale problem statements and real-world constraints. The outcomes we seek are deliberate and measurable: production-grade AI systems, applied research in automotive intelligence and next-generation mobility, patents, proofs of concept, and globally relevant publications. Equally central is the commitment to ethical AI and responsible innovation, ensuring that technological advancement remains aligned with human values and long-term societal impact. Such collaborations are essential if India is to transition from being a consumer of advanced technologies to a global contributor and leader in AI innovation. They allow universities and industry to move beyond short-term engagement toward the development of long-horizon research ecosystems and innovation capacity. The future of Artificial Intelligence will not be shaped by technology alone, but by Universities willing to assume intellectual, ethical, and collaborative responsibility for it.

Hidden in government laboratories across the UK sits a treasure trove of breakthrough innovations that could transform industries - but most never see the light of day. Ploughshare, the Ministry of Defence's commercialisation arm, has quietly been unlocking this potential for nearly two decades. From handheld devices detecting traumatic brain injury on rugby pitches to hydrophobic coatings now protecting consumer footwear, they're proving that military research has far broader applications than most imagine. The challenge isn't lack of innovation - it's bridging the gap between proof-of-concept and market reality. Government scientists typically focus on solving specific problems rather than commercial viability, leaving exceptional IP stranded in laboratories. Ploughshare's approach is methodical: identify promising inventions, assess their impact potential beyond defence, then either license to existing companies or create spin-outs. They've commercialised over 140 technologies, generated £126 million in economic value, and created 500+ jobs. What's particularly striking is the diversity of applications. Naval sonar research becomes underwater infrastructure monitoring. Chemical threat protection becomes waterproof footwear. Military camera technology transforms industrial inspection capabilities. The real opportunity lies in re-examining past research with fresh perspectives. AI is now helping revisit failed trials, uncovering why they failed and enabling successful redevelopment. For the UK's innovation ecosystem, this represents untapped potential at scale - taxpayer-funded research delivering broader economic and social impact. #DefenceInnovation #TechTransfer #UKInnovation #Commercialisation

Research universities are starting to build venture studios — and most of the ecosystem hasn't noticed yet. The model makes structural sense. Universities sit on massive reserves of IP, technical talent, and infrastructure. What they lack is a commercialization system that moves at market speed. Tech transfer offices license patents. Accelerators run cohorts. Neither builds companies. A venture studio fills that gap. The emerging structure is a public-private joint venture: the university contributes IP, talent pipelines, and physical infrastructure. The studio side provides governance, execution systems, and commercial discipline. Funding comes from tech transfer licensing revenue, operational budgets, or a combination — not traditional VC. The biggest barrier isn't capital. It's institutional understanding. University leadership often conflates studios with accelerators. Both involve startups. The similarities end there. Accelerators support existing founders with programming and mentorship. Studios create ventures from scratch — with dedicated teams, defined governance, and a thesis that maps to the university's research strengths. Getting that distinction right is the difference between a rubber-stamp innovation program and an engine that actually produces companies. The universities that figure this out first will have a structural advantage that's difficult to replicate.