Onicio Leal has dedicated 14 years to developing data-driven innovation projects focused on exponential technologies and Artificial Intelligence across 15 countries on all continents. He is a certified global faculty member at Singularity University, specializing in data and digital health. Onicio holds a PhD in Epidemiology and serves as a professor at the University of Arizona’s Department of Epidemiology and Biostatistics, researching and teaching both undergraduate and graduate students.
Throughout his career, Onicio has gained extensive experience working in various industries on topics such as data analytics, machine learning, crowdsourcing, and network science. His work spans numerous sectors, where he has successfully led projects that leverage these technologies to drive innovation and solve complex challenges, with a strong peer-reviewed scientific publication background in high-impact journals.
Previously, Onicio was a post-doctoral fellow at the University of Zurich’s Department of Economics and a senior researcher at ETH Zurich’s Department of Computer Science. He served as Director of Research and Development for the govtech Colab.re for two years and spent seven years as CEO of Epitrack.
Onicio has also excelled in deploying innovation projects in low-income settings, demonstrating a commitment to leveraging technology for social impact. His work with wearable technologies includes development, prototyping, and deployment across a diverse array of environments, showcasing his versatility and expertise in this cutting-edge field.
As a consultant and speaker, Onicio has collaborated with prominent national and international institutions, including Wired, Itaú, Braskem, SwissNext, Pernod Ricard, UNICEF, the Inter-American Development Bank the World Health Organization, the Pan American Health Organization, and the Institute for Scientific Interchange (Italy), among others. His broad experience and deep knowledge make him a sought-after expert in his field.
Data Analytics, Machine Learning, Design Thinking, Digital Transformation
We live in a data-driven world that needs to leverage organizations, results and impact with data. We also experience an excess of techniques and tools to reach this goal. More specifically, data visualization tools need to be more understood to avoid creating more difficulties in decision-making due to exaggeration and overload of information presented. In contrast, the trend of data stories has been the new way of building narratives with contextualized data, shortening the path to better decisions. In addition, in applying Machine Learning for data analysis and decision-making, one cannot ignore the Fairness and Bias aspects that these machines may have to avoid automating inequalities. In this session, which can have a lecture or workshop format, we address theneed to reframe how hypotheses and data products are built to reduce understanding asymmetries within organizations. Methodologies such as Data Product Canvas, Data Stories and Experiment Design are current tools that need to be part of the routine of analytical teams. So that fragile foundations do not compromise the leverage of data. Aspects of Explainable AIs (XAIs) are also addressed to tackle ethical challenges in implementing essential data analysis routines. The expected results of this session are the construction of a homogeneous level of understanding about these aspects of application methods, criticism, ethics and optimization of data routines.
Digital Biology, Personalized Medicine, Public Health, Regulation and Policy
What if a time machine allowed us to travel back 30 years to share this outlook with the health industry at that time, we bet that no one would have believed it. Understanding how these technologies became feasible and widely accepted helps make sense of the past and anticipate what might be coming in the future. However, what is the use of advances in these technologies if they need to be better distributed globally, especially for those who need them most? In this section, we will explore the following topics: (1) main unexpected applications of new data streams to population health; (2) understanding the importance of re-purposing technologies for the democratization of access to health; (3) point out the ethical and fairness challenges that artificial intelligence must consider in order not to automate inequalities; and (4) bring fresh perspectives on the global health challenges that lie ahead and what technologies will meet them.
Public Good, Crowdsourcing, Health, Participatory Policies
The power of collaboration and data sharing has demonstrated a paradigm shift towards building more integrated societies in recent decades. The technologies that serve as crowdsourcing platforms in several industries are already well accepted by a large part of the population, being part of many people's daily routines. In health, crowdsourcing has also shown essential contributions to improving people's lives, either through the collective construction of health scenarios or through decentralized information on regions with a higher risk of illness and consequent improvement in disease prevention. But what next? In addition to self-reported data, what technologies can benefit from crowdsourcing to expand the impact on the global population? How to seek people's collaboration and citizenship mindset around a social good? What are the challenges to engaging social machines in the data-sharing economy? These are some aspects that will be discussed in this session, shedding light on the role of crowdsourcing in the future of healthcare.