Computer Science

Thesis

Sorelle A. Friedler. Geometric Algorithms for Objects in Motion. Dissertation committee: Prof. David Mount (chair), Prof. William Gasarch, Prof. Samir Khuller, Prof. Steven Selden, Prof. Amitabh Varshney. Defense date: July 30, 2010. [PDF] [presentation]

Papers

Sorelle A. Friedler and David M. Mount. Spatio-temporal range searching over compressed kinetic sensor data. In Proc. of the European Symposium on Algorithms (ESA), pages 386-397, 2010. [PDF (preprint) | link] [TR]
     2nd Workshop on Massive Data Algorithmics, 2010 [PDF]
     Fall Workshop on Computational Geometry, 2009 [PDF]

Sorelle A. Friedler and David M. Mount. Realistic compression of kinetic sensor data. Technical Report CS-TR-4959, University of Maryland, College Park, 2010. [PDF | TR]

Sorelle A. Friedler and David M. Mount. Approximation algorithm for the kinetic robust k-center problem. Computational Geometry: Theory and Applications, 2010. (doi: 10.1016/j.comgeo.2010.01.001). [PDF (preprint) | link]

Sorelle A. Friedler and David M. Mount. Compressing kinetic data from sensor networks. In Proc. of the 5th International Workshop on Algorithmic Aspects of Wireless Sensor Networks (AlgoSensors), pages 191 - 202, 2009. [PDF (preprint) | link] [TR]

I am interested in the ways in which technology can aid teachers, and thus aid students, by making the tedious parts of teaching disappear while simultaneously granting teachers more insight into students’ progress. By changing the nature of what it means to be a teacher, by removing the secretarial aspects of the job, I believe this would also increase the professionalism of the field, allowing teachers to focus on creative curriculum planning and student learning and driving the best and the brightest to consider joining the profession. Specifically, I’m interested in creating high quality software that will grade assessments automatically, create dynamic visualizations of per-topic partial credit grades, allow “drag and drop” style creation of assessments from a database of existing problems, and automatically create summary paragraphs describing student progress that can be sent to students, parents, and administrators, translating if necessary. Not only will these abilities change the current nature of teaching, but they will unlock other possibilities related to student learning and assessment, e.g., the creation of tests, designed per-student based on a personalized understanding of their strengths and weaknesses, that can be graded automatically for immediate feedback. By using template matching and active learning techniques, these assessments can be taken on paper and involve creative answers that go beyond multiple choice, such as circuit diagrams, Tangram arrangements, or short-answer math problems.

Previously, I've worked with Ben Shneiderman, Yee Lin Tan, and Nir J. Peer to visualize grades so that teachers can identify individual student strengths and weaknesses. Interviews with sixteen expert teachers across multiple disciplines indicated that the software was successful at providing teachers a useful tool to understand their students' progress and encourage reflective practice. The previous project page, including links to the sourcecode, help videos, and journal paper can be found here.

Papers

Sorelle A. Friedler, Yee Lin Tan, Nir J. Peer, and Ben Shneiderman. Enabling teachers to explore grade patterns to identify individual needs and promote fairer student assessment. Computers & Education, 51(4):1467-1485, December 2008. [PDF (preprint) | link]

Our model currently achieves a 93% success rate when choosing between four possible outcome states. We will continue this work by increasing the size and reach of the data set and building a recommendation system that will suggest future potential successful experiments.

Grants

NSF CDMR grant (proposal submitted for consideration 11/1/2012): The Dark Reaction Project: a machine learning approach to materials discovery. Sorelle Friedler, Joshua Schrier, and Alexander Norquist.

When trying to locate a person inside a building using only a cell phone, GPS cannot be relied upon and so other phone sensors must be used instead. These sensors were not designed to be used for location purposes and measurements collected by them tend to be very noisy. These issues combine to create a hard machine learning problem that can be solved by making use of probabilistic graphical models and sensor fusion to locate a person indoors.

I was named on two provisional patent applications filed.