Aniruddha Belsare is a disease ecologist with a background in veterinary medicine, interspecific transmission, pathogen modeling and conservation research. Aniruddha received his Ph.D. in Wildlife Science (Focus: Disease Ecology) from the University of Missouri in 2013 and subsequently completed a postdoctoral fellowship there (University of Missouri, May 2014 – June 2017). He then was a postdoctoral fellow in the Center for Modeling Complex Interactions at the University of Idaho (June 2017 - March 2019) and later a Research Associate with the Boone and Crockett Quantitative Wildlife Center, Michigan State University (March 2019 - Jan 2021). He is currently a Computational Ecologist in the Civitello Lab at Emory University.
My research interests primarily lie at the interface of ecology and epidemiology, and include host-pathogen systems that are of public health or conservation concern. I use ecologic, epidemiologic and model-based investigations to understand how pathogens spread through, persist in, and impact host populations. Animal disease systems that I am currently working on include canine rabies, leptospirosis, chronic wasting disease, big horn sheep pneumonia, raccoon roundworm (Baylisascaris procyonis), and Lyme disease.
My core research interest is to understand how humans and other living creature perceive and behave; respond and act upon their environment and how this dynamic interplay shapes us into who we are. In recognition of the broad scope of this question I am a strong believer in the need for inter- and multi-disciplinary approaches and have worked at research groups in a wide range of departments and institutions, including university departments of Physics as well as Psychology, a bio-medical research lab, a robotics research laboratory and most recently the RIKEN Brain Science Institute. Though my work has primarily taken the form of computational neuroscience I have also performed psychophysical experiments with healthy human subjects, been involved in neural imaging experiments and contributed towards the development of a humanoid robot.
Based on the philosophy of ‘understanding through creating’ I believe that bio-mimetic and biologically inspired computational and robotic engineering can teach us not only how to build more flexible and robust tools but also how actual living creatures deal with their environment. I am therefore a strong believer in the fertile information exchange between scientific as well as engineering research disciplines.
Dr. Lilian Alessa, University of Idaho President’s Professor of Resilient Landscapes in the Landscape Architecture program, is also Co-Director of the University of Idaho Center for Resilient Communities. She conducts extensive research on human adaptation to environmental change through resilient design at landscape scales. Much of her work is funded by the National Science Foundation, including projects awarded the Arctic Observing Network, Intersections of Food, Energy and Water Systems (INFEWS) and the Dynamics of Coupled Natural Human Systems programs. Canadian-born and raised, Alessa received her degrees from the University of British Columbia. She also uses her expertise in social-ecological and technological systems science to develop ways to improve domestic resource security for community well-being, particularly through the incorporation of place-based knowledge. Her work through the Department of Homeland Security’s Center of Excellence, the Arctic Domain Awareness Center, involves developing social-technological methods to monitor and respond to critical environmental changes. Lil is a member of the National Science Foundation’s Advisory Committee for Environmental Research and Education and is on the Science, Technology and Education Advisory Committee for the National Ecological Observing Network (NEON). Professor Alessa also teaches a university landscape architecture capstone course: Resilient Landscapes with Professor Andrew Kliskey. Professor Alessa’s collaborative grant activity with Professor Andrew Kliskey, since coming to the university in 2013, exceeds 7 million USD to date. She has authored over a 100 publications and reports and has led the development of 2 federal climate resilience toolbox assessments, the Arctic Water Resources Vulnerability Index (AWRVI) and the Arctic Adaptation Exchange Portal (AAEP).
Name: Dr. Julia Kasmire
Position: Post-doctoral Research Fellow
Where: UK Data Services and Cathie Marsh Institute at the University of Manchester.
2004 - BA in Linguistics from the University of California in Santa Cruz, including college honours, departmental honours and one year of study at the University of Barcelona.
2008 - MSc in the Evolution of Language and Cognition from the University of Edinburgh, with a thesis on the effects of various common simulated population features used when modelling language learning agents.
2015 - PhD from Faculty of Technology, Policy and Management at the Delft University of Technology under the supervision of Prof. dr. ig. Margot Wijnen, Prof. dr. ig. Gerard P.J. Dijkema, and Dr. ig. Igor Nikolic. My PhD thesis and propositions can be found online, as are my publications and PhD research projects (most of which addressed how to study transitions to sustainability in the Dutch horticultural sector from a computational social science and complex adaptive systems perspective).
Many of the NetLogo models I that built or used can be found here on my CoMSES/OpenABM pages.
My ResearchGate profile and my Academia.org profile provide additional context and outputs of my work, including some data sets, analytical resources and research skills endorsements.
My LinkedIn profile contains additional insights into my education and experience as well as skills and knowledge endorsements.
I try to use Twitter to share what is happening with my research and to keep abreast of interesting discussions on complexity, chaos, artificial intelligence, evolution and some other research topics of interest.
You can find my SCOPUS profile and my ORCID profile as well.
Complex adaptive systems, sustainability, evolution, computational social science, data science, empirical computer science, industrial regeneration, artificial intelligence