Team

CHANDI WITHARANA | ARIAL Director | chandi.witharana@uconn.edu

Welcome to ARIAL …where the sky meets the ground…       I am the director of Advanced Remote sensing Imaging and Analytics Lab (ARIAL). I am housed at the Department of Natural Resources and the Environment at University of Connecticut (UConn). I am also affiliated faculty of Eversource Energy Center and the Institute for the Brain and Cognitive Sciences at UConn.  I lead UConn’s Remote Sensing and Geospatial Data Analytics Online Graduate Certificate. I am the director of ConnecticutView program.

A long story in short, I am a remote sensor with geoscience backbone. I earned a PhD in Remote Sensing and an MS in GIScience at the University of Connecticut, and a BSc in Geology at the University of Peradeniya, Sri Lanka. Prior to joining the UConn faculty, I was a Post-doctoral Research Associate at the SUNY Stony Brook.  I started my career as a Geospatial Analyst at the United Nations Office for the Coordination of Humanitarian Affairs.

My research efforts broadly capture the methodological developments and adaptations to unseal faster, deeper, and more accurate analysis of large volumes of high-resolution remote sensing data. I conduct interdisciplinary remote sensing research with high international visibility, speaking to the transformational uses of Earth observation in environmental, industrial, and humanitarian applications. My scope is global. Diversity is an integral part of myself, as well as my research. Thinking beyond its research and industrial merits, I always value the strengths of remote sensing in training our next generation . I am actively seeking creative ways, such as imagery-enabled lesson plans to harness remote sensing in K-12 STEM education.

I equally enjoy the scientific value of  remote sensing images as well as their artistic beauty.

RAJITHA UDAWALPOLA | Postdoctoral Scholar | mahendra.udawalpola@uconn.edu

By training, I am a Computer Scientist. My specialty is  algorithm optimization. I  earned my Ph.D. in Scientific Computing from Uppsala University, Sweden. I have a  B.Sc. in Computer Science and Engineering from the University of Peradeniya, Sri Lanka.  I work on the National Science Foundation’s Polar Program funded Project – Navigating the New Arctic tundra through Big Imagery, artificial intelligence, & cyberinfrastructure. I contribute to build high-performance satellite image analysis workflows coupled with AI-based algorithms to leverage the fundamental understanding of changing Arctic permafrost tundra.

NANCY MAREK | PhD Student | nancy.marek@uconn.edu

Nancy is currently a Ph.D. student at the University of Connecticut, Storrs. She received a Master of Forest Science from the Yale School of the Environment and a B.A. in biology from Mount Holyoke College. Her research centers on using unpiloted aerial systems imagery for mapping and monitoring non-native invasive plants in the deciduous forest understory.

AMIT HASSAN | PhD Student | amit.hassan@uconn.edu

I completed my undergrad in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology in 2017. I have worked as a data analyst and deep learning engineer at a Japanese tech company from 2018 to 2020. I am passionate about implementing deep learning-based image analysis in the field of Remote Sensing.
Currently, I am working on mapping ice-wedge polygonal Arctic tundra from satellite imagery.

HARSHANA WEDAGEDARA| MS Student | harshana.wedagedara@uconn.edu

I  completed my bachelor’s degree from the University of Peradeniya, Sri Lanka with the focus on Plantation Management and Forestry. I am currently pursuing a master’s degree in Natural Resources at the University of Connecticut, where I am studying the forestry and applications of GIS/Remote sensing in Forest Management. The pursuit of knowledge is one of my core values and I greatly look forward to one day leading my own research group and addressing critical issues relevant to forest science around the world. In my spare time, I love to go to different places and listen to music.