The Research Experience for Undergraduates (REU) funded by the National Science Foundation (NSF) ran for three years from 2014-2016.
REU students will receive a $5,000 stipend for the 10 week REU Summer session.
Travel costs will also be covered (up to $800) for students who live some distance from Siena College.
Participants will reside in housing on the Siena campus at no cost to the student.
Meal plans are also provided.
This is a federally funded program and eligibility requirements are as follows:
U.S. Citizens or Permanent Residents of the U.S. or its territories.
Students in good standing in an undergraduate degree program who will not have graduated by the end of this REU program. (This includes students graduating from a 2-year institution that have been accepted to and are enrolled in a 4-year degree program.)
At least 18 years old by June 1, 2016.
Women, members of traditionally underrepresented groups and students at a primarily undergraduate institutions and community colleges are particularly encouraged to apply.
Each year we are faced with new and complex environmental dilemmas. In confronting these, we use a variety of opportunities for public participation to potentially shape and inform policy and regulations. But much of this public input is difficult to catalog and process: in the end it is much less useful than it should be. At Siena we have developed an automated approach to process and “understand” public input to the environmental review process. We have focused on the public comments for potential regulation of natural gas extraction using hydraulic fracturing (fracking) in New York State. This summer’s project will build on previous student work by using computational techniques to better understand and interpret attitudes towards fracking contained in hundreds of thousands of pages of public comments contained in over 10GB of data.
Computational Linguistics, is a growing field of interest in artificial intelligence, especially in the area of Information Retrieval. According to a report from the The Second Strategic Workshop on Information Retrieval in Lorne (published in the SIGIR Forum, June 2012): “Future information retrieval systems must anticipate user needs and respond with information appropriate to the current context without the user having to enter an explicit query... In a mobile context such a system might take the form of an app that recommends interesting places and activities based on the user’s location, personal preferences, past history, and environmental factors such as weather and time... ” For example, imagine a group of information retrieval researchers with a November evening to spend in beautiful Gaithersburg, Maryland. A contextual suggestion system might recommend a beer at the Dogfish Head Alehouse (www.dogfishalehouse.com), dinner at the Flaming Pit (www.flamingpitrestaurant.com), or even a trip into Washington on the metro to see the National Mall (www.nps.gov/nacc). This project will participate in the TREC 2015 Contextual Suggestion track (trec.nist.gov), whose goal is to provide a venue for the evaluation of such systems which try to respond to the all-encompassing request “Entertain Me”.
An Implicit Association Tests (IAT) is used by psychologies to measure an individual’s unconscious bias on issues such as race, gender, and age. If a participant is aware of how an IAT works, they can “cheat” by deliberately delaying their response time to hide their implicit reaction. As awareness of IATs increases, the chance of encountering cheaters increases which makes IATs less valuable to psychologists. A solution is to develop an automated, accurate and accessible mechanism to detect cheating. The goal of this project is to (a) design a sound mechanism to produce examples of IAT cheaters and (b) develop a supervised learning algorithm that can reasonable identify cheating based on these examples. HTML5 and recent enhancements of JavaScript provide the functionality and precision to make an accurate IAT tool that can be widely accessible to collect data and help practitioners identify cheaters.
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