The award places Fischer-Kreer among a select group of university researchers who, according to the organizers, have made important contributions to the transfer of innovations into business alongside their core duties in research and teaching. The initiative describes the honorees as outstanding researchers at their respective universities who represent the highest standards of research excellence in their fields. Collectively, the professors recognized by UNIPRENEURS have supported around 600 startups as mentors, advisory board members, or investors, and 90 percent of them have founded companies themselves. These figures underline the initiative’s central premise: that the most effective champions of university entrepreneurship are academics who combine scholarly credibility with direct, practical experience of the startup world.

For Fischer-Kreer, the recognition carries significance beyond personal achievement. “I am absolutely delighted about the UNIPRENEURS award. This will bring greater attention to the topics of spin-offs and entrepreneurship at our university, as well as to our commitment to promoting them. The award is a great honor,” she said following the ceremony. Her remarks point to a broader argument that she and the initiative share: that knowledge transfer deserves the same institutional weight as the two traditional pillars of academic life. “Transfer is another key task of a university, alongside research and teaching,” she emphasized, adding that she looks forward to further strengthening the entrepreneurial culture at the University of Bonn in the coming years together with her team and colleagues working in the field of transfer.

Fischer-Kreer’s path to the professorship reflects an unusual combination of academic training and industry experience. She studied industrial engineering before earning her doctorate at RWTH Aachen University, one of Germany’s leading technical institutions. She then spent two years in the strategy department of LANXESS, a specialty chemicals company headquartered in Cologne, gaining firsthand exposure to corporate strategy and innovation management. Rather than remaining in industry, she returned to the university environment with what the University of Bonn describes as great enthusiasm, determined to develop innovative and practical teaching formats that would bring entrepreneurial thinking to a much wider student audience than traditional business education typically reaches.

At the University of Bonn, Fischer-Kreer holds the professorship for Entrepreneurial Behaviour, a position focused on understanding and cultivating the psychological and behavioral foundations of entrepreneurship. A central aim of her teaching is to establish entrepreneurship education across faculties, extending it beyond business and economics students to the natural sciences, agriculture, medicine, and other disciplines where research-based spin-offs often originate. Her stated key goal is to empower students to believe in themselves and their abilities, and to view entrepreneurial thinking as a key future skill, whether they ultimately pursue self-employment or drive change within existing organizations. This framing treats entrepreneurship not merely as a career path but as a transferable mindset applicable across the modern economy.

Her pedagogical approach is notably experimental. Among the interdisciplinary formats she has introduced is the use of improvisational theatre in the lecture hall, a technique designed to build the spontaneity, communication skills, and tolerance for uncertainty that entrepreneurs need in practice. Such methods reflect a growing international consensus in entrepreneurship education that entrepreneurial competencies are best developed through experiential learning rather than purely theoretical instruction. By embedding these formats in a full university setting, Fischer-Kreer is working to normalize entrepreneurial learning as part of the standard academic experience at Bonn rather than an optional add-on for a small cohort of already-motivated students.

Her research agenda is similarly distinctive in its choice of settings. Fischer-Kreer explores entrepreneurship in unique and understudied environments, ranging from a sustainable municipality in southern India to the Vatican in Rome and the Archdiocese of Cologne, as well as entrepreneurial dynamics in the Global South. These contexts allow her to examine how entrepreneurial behavior emerges outside the familiar ecosystem of venture capital, incubators, and technology clusters that dominates much of the mainstream literature. Studying entrepreneurship in religious institutions and developing-region municipalities can reveal how motivation, community structures, and resource constraints shape the way individuals identify opportunities and mobilize support for new ventures, insights that feed directly back into her teaching.

Her commitment to fostering an entrepreneurial spirit has also been recognized at the industry level. In 2024, DEUTZ AG in Cologne honored her with the Nicolaus August Otto Award for her dedication to fostering entrepreneurship among business startups in the agriculture and food industries. This focus connects naturally with her institutional affiliations at the University of Bonn, where she is a member of the Transdisciplinary Research Area “Sustainable Futures” and the Cluster of Excellence “PhenoRob,” a research cluster dedicated to digitalization and robotics in crop production. The intersection of agricultural technology, sustainability, and entrepreneurship represents one of the most promising areas for research-based spin-offs, and her work positions the university to support founders in precisely this space.

Beyond teaching and research, Fischer-Kreer plays an active institutional role in the local startup ecosystem. As a board member of the Universitätsgesellschaft Bonn, the university’s support society, she supports and develops the regional startup landscape and promotes the transfer of knowledge and practical expertise between academia and business. She is also the founder of the UGB Transfer Award, which recognizes transfer achievements at the university. Her practical work extends into unusual formats, including the development of gaming and children’s book formats designed to promote an entrepreneurial mindset from an early age. She additionally acts as a VIP+ mentor for Sweethoven Biotech, a project that has developed a healthy sugar alternative based on fundamental research, demonstrating her hands-on involvement in guiding early-stage science-based ventures toward commercialization.

The UNIPRENEURS initiative itself was created by Matthias Hilpert and Martin Schilling with the explicit task of fostering spin-offs at German universities. The Stifterverband für die Deutsche Wissenschaft, a foundation dedicated to science and higher education, is responsible for organizing and implementing the joint initiatives, while the German Startups Association, Bitkom, and AddedVal.io support the program as partners. The award is presented every three years and is described as Germany’s highest honor for outstanding commitment to promoting entrepreneurship at universities. The Federal Ministry of Research, Technology, and Space and the Federal Ministry for Economic Affairs and Energy have jointly assumed the patronage of the initiative, a signal of the strategic importance that German policy attaches to translating academic research into commercial innovation.

Political leaders at the ceremony framed the award in terms of Germany’s broader economic future. “With the UNIPRENEURS initiative, entrepreneurial activity at German universities is being fostered by raising the profile of professors who play a key role,” said Dorothee Bär, Federal Minister of Research, Technology, and Space. “The award recipients make a significant contribution to creating a culture of entrepreneurship at universities and are thus also important drivers of Germany’s capacity for innovation and long-term competitiveness.” Katharina Reiche, Federal Minister for Economic Affairs and Energy, echoed that assessment: “UNIPRENEURS represents the strong link between academic excellence and entrepreneurial courage at our universities. The professors honored make an important contribution to Germany’s competitiveness as an innovation hub.” For the University of Bonn, the recognition of Fischer-Kreer affirms a strategy of embedding entrepreneurship across the institution, and for the honoree herself, it provides a platform from which she intends to keep building that culture in the years ahead.

Subject of Research: National award for university entrepreneurship education and startup promotion in Germany

Article Title: UNIPRENEURS honors Denise Fischer-Kreer

Article References: UNIPRENEURS honors Denise Fischer-Kreer. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: UNIPRENEURS, Denise Fischer-Kreer, University of Bonn, entrepreneurship education, academic spin-offs, knowledge transfer, startup ecosystem, Germany, Stifterverband, entrepreneurial behaviour, innovation, higher education

The study surveyed 292 STEM faculty members across five Hong Kong universities, combining the measurement of perceived benefits and barriers to engagement with indicators of actual collaboration with industry and government. The analytical framework is unusual and ambitious. The authors fused Self-Determination Theory, the psychological account of motivation built around three basic needs—autonomy, competence, and relatedness—with the classical Mertonian sociology of science, which holds that scientific work is governed by norms such as communalism, universalism, disinterestedness, and organized skepticism. The bridge between these two traditions is a concept the authors call normative alignment: the degree to which an engagement arrangement simultaneously satisfies a professor’s psychological needs and upholds the professional norms of science.

The headline finding is what the authors describe as a gateway mechanism. Using partial least squares structural equation modeling, they tested whether perceived benefits and barriers influence government collaboration directly. They do not. Instead, those perceptions predict engagement with industry, and industry engagement fully mediates the relationship—meaning the entire effect of perceived rewards and obstacles on government collaboration flows through prior work with companies. Prior industry engagement was the single strongest predictor of subsequent government collaboration in the model. In plain terms, professors do not leap from motivation to public-sector partnership; they arrive there by way of the marketplace.

Why would commercial experience function as a prerequisite for civic collaboration? The authors point to the transferable capabilities that industry projects cultivate. Working with a company teaches a researcher how to scope a problem defined by an external partner, how to negotiate intellectual property and timelines, how to communicate findings to non-specialists, and how to operate within institutional constraints that differ from those of the laboratory. It also builds credibility: a track record of delivering for a corporate client signals to a government agency that the academic can be trusted with public resources and sensitive policy questions. And it builds networks—professional contacts that later surface as advisory appointments, committee invitations, and joint grant applications.

The mediating structure of the results is striking because it inverts a common policy intuition. Many governments, including Hong Kong’s, have invested heavily in schemes designed to push universities toward knowledge transfer, on the implicit model that incentives and structural reform will move faculty directly into both commercial and public engagement. The Hong Kong study suggests that these two channels are not parallel alternatives competing for a professor’s limited time, as much of the existing literature has treated them, but sequential stages of a developmental trajectory. A faculty member who has never engaged with industry is, according to the model, largely unreachable by government collaboration no matter how attractive the perceived benefits, because the capabilities and confidence that make such collaboration feasible have not yet been built.

This is where normative alignment does its explanatory work. The authors argue that engagement decisions are filtered through a double test. First, does the arrangement satisfy the researcher’s needs for autonomy—the sense of directing one’s own work—competence, and relatedness to others? Second, does it preserve the Mertonian norms that define what it means to be a scientist: openness, universal standards, the pursuit of knowledge for its own sake, and skeptical scrutiny? An industry consultancy that requires publication secrecy, for example, may pay well but violate the norm of communalism, producing internal resistance that no financial incentive can overcome. A government advisory role that respects scholarly independence may satisfy both tests—but only for researchers who have already developed the practical competence to take it on.

The study’s methodological choices reflect a careful attempt to isolate these mechanisms. The authors used concise Likert-type scales, citing classic psychometric work showing that response formats beyond a few points do not systematically improve reliability, in order to reduce respondent burden in a busy academic population. They checked for nonresponse bias using established procedures, conducted power analyses to justify their sample, and applied bootstrap-based significance testing within the PLS-SEM framework, an approach increasingly common in higher education research when the goal is to test mediated pathways in complex motivational models. Ethics approval was obtained in April 2019, and the survey procedures followed the Declaration of Helsinki.

The practical implications reach well beyond Hong Kong. If engagement follows a developmental sequence, then universities that want faculty advising government agencies should not start by dangling advisory appointments at researchers with no external experience. They should build the on-ramp first: structured, low-stakes industry projects that let academics acquire boundary-spanning skills while their scholarly identity remains intact. The authors are explicit that the answer is not simply more money. Rather than suggesting that financial incentives alone can secure participation, they point to structural protections—template agreements that reduce negotiation friction, institutional buffers that shield researchers from liability and mission drift, and evaluation systems that count engagement as legitimate scholarship rather than a distraction from it.

That last point connects the study to a broader and often anxious debate about academic identity. Sociologists have long worried that commercialization corrodes the norms of science, and recent surveys of researchers across fields document widespread tension between Mertonian values and the counternorms of the entrepreneurial university. The Hong Kong findings offer a more hopeful, micro-level account: engagement does not necessarily erode scholarly identity if the arrangements are designed to preserve it. When a professor can consult for a company without surrendering publication rights, or advise an agency without abandoning peer review, the psychological needs and the scientific norms align, and engagement becomes not a betrayal of the academic role but an extension of it. Heterogeneous faculty responses to identical rewards, the authors argue, are exactly what this filtering process should produce.

There are, of course, limits to what one cross-sectional survey of 292 faculty in a single city can establish. The authors acknowledge sample-size constraints, and a snapshot design cannot fully prove the developmental sequence it implies; longitudinal tracking of individual careers would be the decisive test. Hong Kong’s compact, internationally oriented university system may also shape engagement patterns in ways that do not travel cleanly to larger or more decentralized systems. Still, the gateway mechanism is a genuinely useful addition to the science-policy toolkit. It reframes the question that universities and governments have been asking for years. The puzzle is not how to motivate academics to engage, but how to build the first rung of the ladder—and how to make sure that climbing it does not cost researchers the identity that brought them to science in the first place.

Subject of Research: Motivational mechanisms and developmental pathways of faculty engagement with industry and government in higher education

Article Title: Normative alignment in academic engagement: industry experience as gateway to government collaboration

Article References: Wu, Y., & Wu, X. (2026). Normative alignment in academic engagement: industry experience as gateway to government collaboration. Higher Education. https://doi.org/10.1007/s10734-026-01769-0

Image Credits: AI Generated

DOI: 10.1007/s10734-026-01769-0

Keywords: academic engagement, normative alignment, Self-Determination Theory, Mertonian norms, gateway mechanism, university-industry collaboration, government collaboration, higher education, faculty motivation, STEM faculty, knowledge transfer, Hong Kong universities

The field of evolutionary multitasking grew out of the recognition that many real-world optimization tasks are interrelated. Rather than running a separate evolutionary algorithm for every problem, multitasking algorithms solve several tasks simultaneously within a single framework, allowing candidate solutions to migrate between task-specific populations. When the tasks are similar, this exchange can dramatically accelerate convergence, because a solution that performs well on one problem may already be halfway to a good solution on another. The foundational work on multifactorial evolution by Gupta, Ong, and Feng in 2016 demonstrated the promise of this approach, and a growing body of research has since refined how knowledge moves between tasks.

The catch is negative transfer. When two tasks differ substantially in the structure of their search spaces or the shape of their objective functions, blindly importing solutions from a source task can pull a target population away from its own promising regions. The result is wasted computational effort and, in the worst cases, worse final solutions than a task would achieve on its own. Researchers have proposed a variety of remedies, from adaptive transfer probabilities based on population distribution statistics to explicit mapping techniques that translate solutions between task domains. The new algorithm, called MOMFEA-MTL, combines two complementary mechanisms to attack the problem from both directions: deciding when to transfer, and deciding how to transfer.

The first mechanism is an adaptive knowledge transfer strategy that continuously monitors the historical evolution of each population. Instead of fixing the probability that a solution crosses from one task to another, the algorithm adjusts that probability dynamically based on how well past transfers have served the target task. If incoming solutions have recently improved the target population’s performance, the transfer probability rises; if they have degraded it, the probability falls. This feedback loop suppresses negative transfer without requiring any prior knowledge of how similar the tasks are, which is precisely the information that is hardest to obtain in practical settings where the geometry of the search landscape is unknown.

The second mechanism tackles the question of how solutions should be transformed when they do cross between tasks. Naive approaches simply copy decision variables from one task’s representation to another, an operation that only makes sense when the tasks share a common search space structure. MOMFEA-MTL instead constructs a mapping matrix using a manifold transfer method, an approach rooted in the observation that high-dimensional data often lie on or near a low-dimensional manifold. By learning a transformation that preserves the intrinsic geometric structure of the source population while aligning it with the target task’s distribution, the algorithm can move solutions across task boundaries in a way that respects the underlying shape of each problem’s search space.

Selecting which solutions deserve the cost of this transformation is handled by a K-means solution selection strategy. Clustering the population into groups and choosing representative solutions from those clusters ensures that the transferred knowledge captures the diversity of the source task rather than a narrow sample of its best-performing region. This matters because multi-objective optimization does not seek a single best solution but an entire Pareto front of trade-off solutions, and preserving spread across the front is as important as pushing toward it. Once selected, the solutions are mapped from the source task to the target task, where they join the target population and accelerate its evolution.

The combination is designed for multi-objective multitasking problems, where each task involves optimizing several conflicting objectives simultaneously. This setting compounds the difficulty of knowledge transfer: not only must solutions be useful, but the distribution of trade-offs must also remain balanced. The authors position their approach within the broader lineage of multiobjective multifactorial optimization, building on the original MO-MFEA framework and its successors such as MO-MFEA-II, which introduced cognizant multitasking, and on explicit transfer methods like those based on autoencoding and transfer component analysis. Manifold transfer learning itself has precedent in dynamic multiobjective optimization, where it was used to predict how Pareto sets shift as problems change over time; the new work adapts the idea to the multitasking setting, where the shift is between tasks rather than between time steps.

To evaluate the method, the team ran experiments on nine classical multi-objective multitasking test functions, comparing MOMFEA-MTL against established baselines from the literature. The results showed that the proposed algorithm achieved competitive performance across the benchmark suite, with the adaptive transfer strategy and manifold-based mapping working together to deliver gains where task relatedness could be exploited while limiting damage where it could not. The authors report that the algorithm demonstrates good competitiveness on the test problems, supporting the central claim that combining adaptive transfer control with structure-preserving mapping is an effective recipe for multitasking optimization.

The practical implications extend beyond benchmarks. Evolutionary multitasking has already been applied to problems such as vehicle routing with occasional drivers, sparse reconstruction, multi-task learning for modular learning machines, and, notably by members of the same group, the optimization of steel rolling schedules in industrial production. In each of these domains, multiple related optimization problems arise naturally, and the cost of solving them one at a time is substantial. An algorithm that can reliably harvest the similarities between tasks while shielding itself from their differences could translate into measurable savings in computation time and solution quality, particularly for expensive simulations where each function evaluation carries real cost.

The research also contributes to a conceptual shift in how the field thinks about transfer itself. Early multitasking algorithms treated knowledge transfer as a fixed structural feature, with a constant probability of inter-task mating or a static mapping between search spaces. The trend, exemplified by self-regulated multitasking, adaptive transfer based on population distributions, and transfer rank methods, is toward algorithms that learn from their own experience which transfers help. MOMFEA-MTL fits squarely in this tradition, adding the geometric perspective of manifold learning to the toolkit. As optimization problems in industry and science grow larger and more entangled, the ability to solve many tasks at once, safely and efficiently, may prove to be one of evolutionary computation’s most valuable exports, and this work offers a carefully engineered step in that direction.

Subject of Research: A multi-objective multitasking evolutionary optimization algorithm using adaptive knowledge transfer and manifold transfer learning to mitigate negative transfer.

Article Title: Multi-objective multitasking optimization based on manifold transfer learning

Article References: Zhang, K., Cheng, Y., Wang, S., Sun, H., Wei, L., & Hu, Z. (2026). Multi-objective multitasking optimization based on manifold transfer learning. International Journal of Machine Learning and Cybernetics, 17(9), Article 458. https://doi.org/10.1007/s13042-026-03300-4

Image Credits: AI Generated

DOI: 10.1007/s13042-026-03300-4

Keywords: evolutionary computation, multitasking optimization, multi-objective optimization, knowledge transfer, manifold learning, negative transfer, Pareto front, K-means clustering, evolutionary algorithms, transfer learning, optimization algorithms, machine learning