4 graduate assistantships available (Fall 2018)

We have 4 graduate teaching assistantships available starting Fall 2018. If you are interested in getting a  PhD in real-time GIS, real-time sensor data streaming, geosensor networks, and social sensing, please apply!


The University of Maine, School of Computing and Information Science is seeking students interested in pursuing advanced degrees in spatial informatics and/or computer science. The school offers MS degrees in Spatial Information Science and Engineering, Spatial Informatics, Information Systems and Computer Science as well as PhDs in Computer Science and Spatial Information Science and Engineering.

Funding for several general graduate research assistantships and teaching assistantships is available from the School starting in September 2018.

Faculty research interests cover a range of topics in geographic information science, data science, and computer science that include: human computer interaction, real-time sensor data streaming, geosensor networks, ontologies and semantic modeling, spatial databases, information policy, spatial and temporal reasoning, and context modeling, among others. The school seeks students with research interests that align with the above research topics. Funding is available through graduate teaching and research assistantships.

Interested applicants should have prior experience or an undergraduate degree in computer science, geographic information science or a closely related degree. On line applications can be accessed through the University of Maine Graduate School at the link https://umaine.edu/graduate/apply/

Interested candidates may contact Kate Beard for further information. Rolling applications are being accepted for Fall 2018 enrollment.

The University of Maine is an Affirmative Action/Equal Opportunity Employer and the University especially encourages applications from women and minorities.


“Real-Time Earthquake Monitoring with Spatio-Temporal Fields” paper published at “Spatiotemporal Computing” Symposium 2017

Today, J.C. Whittier and Iranga Subasinghe are presenting a paper titled Real-Time Earthquake Monitoring with Spatio-Temporal Fields at the Second International Symposium on “Spatiotemporal Computing“, taking place Aug 7-9 2017 at Harvard, MA.

Our objective was to bring together 3 components: a) realistic, publicly available, real-time sensor data streams, b) an open source data stream engine and c) the field data type model (Liang2016) to implement a realistic real-time analysis application using the field model, and explore its implementation using a open source data stream engine. We chose the  Southern California Integrated GPS Network (SCIGN), which provides real-time GPS streams along Southern California fault lines, and the Apache Spark environment (including Spark, Kafka and MongoDB) for implementation.  The live demo can be found at http://shakeviz.geosensornetworks.net.

The paper can be accessed here: ISSTC-2017-whittier_nittel.

Note: a few weeks after finishing our prototype, the SCIGN socket making the live stream available seems to have failed, and is currently under revision. The data currently shown in the demo is data that we archived; the demo loops over this data (April-May 2017 data).  Once the socket streams the data again, we will reconnect to the live data.

Screen Shot 2017-08-07 at 2.13.48 PM

US Community Energy Projects Database and Website

Over the last year, we have been involved in a project to develop a US Community Energy Projects Database and Website, in collaboration with Dr. Sharon Klein, Economics Department, University of Maine. Dr. Klein collected the initial data set, and Katrina Stinson, a CS undergraduate, built the database and website. We soft- launched this spring.

The US Community Energy Projects website is a centralized location for sharing information about community energy projects across the United States. It began as a compilation of publicly available information on renewable energy projects (solar, wind, biomass, geothermal, hydropower) with a “group” or “community” element. Now that the website and associated database are publicly available, the group hopes people with direct knowledge of projects will add/correct information to keep the database growing and current. CWRU renewable energy projects are listed.

Please feel free to share!



Python programming summer camp for Maine high school students

Today, we started teaching a gender-balanced group of 20 Maine high school students the beginnings of Python programming with little whale robots. Good fun!


Katrina Stinson presents capstone project on Visual Analytics for US Community Green Energy Projects

I had the pleasure of advising Katrina Stinson, in collaboration with Dr. Klein (Economics), for her senior capstone project over the last 2 semesters. This project entailed building a website with a database backend holding over 5000 community energy projects in the US that can now be accessed, queried and analyzed through the website. Users can register accounts and add new projects. Her capstone specifically focused on adding visual analytics to the community energy project data (statistics page).

The website can be found at http://communityenergyus.net.

Katrina also graduates as best Senior of her class (and she did the program in 3 instead of the usual 4-5 years).

Congratulations, Katrina!


COS120 – Computing that Matters

This spring, we taught a class with new experimental methods of teaching programming to non-CS majors. The class started with a Scratch module, then 4 weeks of Python, and after that a choice of Arduinos and Databases, GIS, robots, drones or virtual reality.

Katie Manzo, my CLA (computing learning assistant), and I oversaw the Arduino and Database group. Everyone received an Arduino starter kit, and worked their way through different circuit tutorials. We integrated sensor streaming from Arduino boards sensors to Python programs and SQL databases.

The group had 2 final projects: building a zeotrope regulated by a soft potentiometer, and a live streaming sensor station visualizing live data.
Here are some snapshots from the class.

The class material (for self study) is available at this link.


J.C. Whittier successfully defends dissertation proposal

Today, J.C. Whittier successfully defended his dissertation proposal “Towards an efficient, scalable stream operator framework for computing continuous fields”, and advanced to Ph.D. candidacy.

Congratulations, J.C.!


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