Daavid has a passion for advancing humanity through personalized education, applying evidence-based decision making and innovative technology tools. During his undergraduate studies in Finland, his PhD in theoretical astrophysics in France and subsequent postdoctoral research in the United States, he has applied numerical model building and advanced quantitative/qualitative data analysis methods on a variety of scientific and educational projects adaptable for all ages. In collaboration with nonprofit organizations such as TEACH (Toward Educating America’s CHildren, primarily operating in rural areas of Guatemala), he has applied his expertise outside of academia and contributed to improving accessibility to high-quality education worldwide while addressing demographic and geographic challenges. Among other things, he is also a certified group fitness instructor and has volunteered as a teaching assistant for Y.O.G.A. for Youth, a nonprofit organization providing urban youth with tools of self-discovery that foster hope, discipline and respect for the self, others and community. Whenever possible, he also enjoys rock climbing, gardening and other outdoor activities. He firmly believes that by acting together, our persistent efforts will be able to transfer our optimistic ideas into positive realities for all people. For more information, please see his LinkedIn page.
Neutrino Propagation in Supernovae with Non-Standard Interactions (Beyond the Standard Model of Elementary Particles) »
Project: Citizen Science Project: Deeper Learning with Leaves
Goal of the project:
Create a universal, interactive database for leaf identification while providing education on computational thinking skills.
Summary of work:
A user can take a picture with a smartphone (or upload an image) of a leaf to possibly identify the plant, if already known (see representative image). The image is processed by a pre-trained neural network, which will display its results in the output page. Simultaneously, a hyperlink with image information is emailed to a network of human verifiers, who have been pre-approved by passing a plant identification test. The verified images are added to a databin. The output page provides links to more information related to the functionality of the web form together with information on how a user can contribute or learn more, such as by becoming an official verifier or by becoming a Wolfram certified instructor.
Results and future work:
The final result is a successful creation of a web-deployable human-machine interface that can also be easily adjusted for other use cases, such as other identification objects or by implementing different methods of identification. Future work includes optimization of the neural network and plant identification test. In addition, the plan is to offer related workshops and create adaptive lesson plans for informal education, K–12, college/university and professional training, and to also provide the services in other languages.