Martin Ho is a Postdoctoral Fellow at the Department of Engineering at University of Cambridge. His research develops quantitative approaches for studying technological change and innovation systems using multilayer networks, large-scale publication, patent and funding datasets, and machine-assisted semantic analysis. Taking inspiration from systems engineering, his work applies network science to study how innovation emerges from interactions across three interconnected layers: knowledge and technological artifacts (\u201cthings\u201d), innovators and teams (\u201cpeople\u201d), and organizations and institutions (\u201cplaces\u201d). Using network science, Martin\u2019s research examines phenomena ranging from innovation trajectories in emerging technologies and team-science spillovers to technological forecasting and roadmapping. At Cambridge, he collaborates with policymakers, funders, and industry on technology intelligence, innovation strategy, and the design of R&D portfolios. Originally trained in genetic engineering and immunology, he brings an interdisciplinary perspective that integrates innovation management, systems engineering, and science-of-science methods to develop scalable tools for understanding technological change.<\/p>\n"}],"event_agenda":false,"event_photo_gallery":false,"event_presentations":false,"event_custom_heading":[{"event_custom_title":"Abstract","event_custom_details":"
Mission-oriented R&D programs such as those at DARPA and ARPA-E increasingly shape national innovation portfolios, yet their design and evaluation are typically inferred from aggregate outputs \u2014 publications, patents, and spinouts which reveal little about how programs actually organize and coordinate innovation. This talk develops a network-science framework for analyzing the structural organization of challenge-led R&D programs. Representing programs as typed networks linking researchers, organizations, and knowledge outputs (\u201cpeople, places, and things\u201d), I apply motif-based graph analysis to recover the local coordination structures assembled by program directors and to test longstanding hypotheses in innovation management about how ARPA-style programs assemble capabilities, coordinate projects, and generate spillovers across innovation ecosystems.<\/p>\n
<\/p>\n
Using all publicly available ARPA-E project impact records from its first decade (23 programs and 61 projects), I reconstruct networks linking over 1,000 researchers, 300 institutions, and nearly 2,000 innovation artifacts through funding, collaboration, and citation relationships. The structural analysis reveals three empirical patterns. First, citation-based knowledge clusters appear significantly more frequently than expected under degree-preserving null models, indicating that many programs generate internally coherent knowledge communities rather than isolated outputs. Second, cross-program connectivity is mediated primarily through recurring institutional anchors \u2014 such as major universities and national laboratories \u2014 rather than widespread performer mobility. Third, programs exhibit distinct structural \u201cmotif fingerprints\u201d that align with ARPA-E\u2019s thematic program categories, suggesting systematic variation in portfolio design and managerial strategy. By making these structural signatures observable, network motifs provide a reproducible empirical language for evaluating mission-driven R&D programs retrospectively and informing the design of new research portfolios prospectively. More broadly, the framework contributes a scalable methodological approach for analyzing innovation systems and understanding how challenge-led R&D programs shape technological ecosystems.<\/p>\n"}],"event_enquiry_details":{"event_enq_full_name":"","event_enq_department":"","event_enq_email":"","event_enq_telephone":"","event_enq_website":""}}]