Daniele de Rigo is an Italian scientist in the field of computational modelling for environment (Earth science and ecology) who has worked at the Politecnico di Milano, Italy (Engineering degree, 2002; PhD in information technology, on planning and management of environmental systems, 2015) on water resources. Since 2010, he is working to support the European institutions (in particular the European Commission, Joint Research Centre, JRC) at the science-policy interface, mainly on how vegetation, forest and soil resources interact with wildfires, pests, climate change and how this may put people at risk.
ʀᴇᴀʟɪᴛʏ ᴘʀᴇᴠᴀɪʟs ᴏᴠᴇʀ ᴛʜᴇᴏʀʏ ― He has long practiced the crafting and durable evolution of complex environmental modelling (but complexity demands some humility: likely, one shouldn't venerate latest hype & "precisely wrong" coloured maps...). His particular focus is on how to derive the solution of scientific problems from their “physical” semantics, and from the robust integration of multiple systems’ dimensions - lest a "solution" neglects the magnitude and (often surprising) interplay of their joint uncertainty.
Computational modelling at multiple space/time scales - collaborating with many teams, typically each with different disciplinary expertise - has a distinctly advanced technical nature. This routinely includes data-transformation models (D-TM) to process uneven data with custom methods, whose mathematical design (caring for multiplicity, uncertainty, semantics) often makes the difference in terms of scalable, robust policy support. Reality's structure matters, not trendy silver bullets.
ɪɴ sɪᴍᴘʟɪsᴛɪᴄ ᴡᴏʀᴅs ― Multiplicity and irreducible uncertainty may challenge our mind - as complexity adds mental load. Our innate cognitive biases do not help: science is also about mitigating their influence with awareness and training – humbly acknowledging we may only partly succeed. Sometimes, "for the sake of simplicity", we disregard 𝘰𝘣𝘫𝘦𝘤𝘵𝘪𝘷𝘦 complexity (i.e. actually out there) in favour of allegedly faster workarounds, resulting in uncontrollable spills of complicacy popping up too late. Complicacy and misjudgment are set to emerge precisely because oversimplified workarounds were attempted at first, "for Nature cannot be fooled". In scientific modelling, notably when findings get refined for years and not dropped hastily, this is a well know paradox: the trade-off between facing actual complexity, or else self-inflicted complicacy. Preventing anti-patterns may pay off.
ᴍᴇᴛʜᴏᴅs ― In many research collaborations, he contributed new models to integrate the available uneven arrays of data into usable approximated answers, using Free Software in GNU/Linux. He systematically guides D-TMs through their semantics not to lose sight of the meaning behind technicalities (Semantic Array Programming paradigm [https://goo.gl/GZHch9, http://doi.org/cm2x], which he began developing in 2005 with the Mastrave modelling library, http://mastrave.org). As uncertainty may deeply affect decision making, raising awareness of it is critical.
ᴇxᴘᴇʀɪᴇɴᴄᴇ ― After having worked at CESI (Italy), he worked at Politecnico di Milano on water resources management and planning. Since June 2010 he worked as a research scientist at the JRC, on modelling forest tree species current distribution and suitability at the European scale, under climate change scenarios. He collaborates as a JRC external consultant also in supporting the integrated modelling of forest disturbances (wildfires, forest pests: both being https://w.wiki/5SoG) also considering their effects on soil resources (soil erosion, landslides) and the transdisciplinary multiplicity of factors affecting natural disaster analysis, mitigation and management. Besides, he collaborates as a volunteer in several open-science projects, to freely support worthy Community Science.
ꜰᴏᴄᴜs ― He is interested in how to facilitate synergies and scalable integration of natural resources (forests, soil, water) modelling and management to better support policymaking (under so much pressure to understand uncertainty in a changing world, and the real questions); and to help to move research towards stronger robustness to uncertainty, reproducibility and cooperation. As accountability and formal aspects are also important for a sustainable, powerful, and transparent contribution of computational science for policy, these aspects are never neglected - from licensing of software, data, and other research output; to moral rights and authors' responsibility, science ethics and integrity.
ᴘᴜʙʟɪᴄᴀᴛɪᴏɴs ― de Rigo authored more than 100 research works. Below, a small selection: more complete lists may be found in Google Scholar, or ResearchGate (see links on the left).
[As with most researchers, other inaccurate lists exist, automatically generated by many third-party services. Alas, as with most researchers, I lack the time to fix these "mushroom-lists"; and I'm not my homonyms online!]