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--- ongoing [2011/05/09 20:10] – bil
+++ ongoing [2013/04/19 20:29] (current) – mb
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-====== Ongoing projects ======
+====== Previous projects ======
+====== Comparison of Finger Tracking systems ======
+**Student**:
+**Supervisor**: Wolfgang Stuerzlinger
+**Required Background**: C/C++ coding
+__Description__
+This project compares the Leap Motion and the 3Gear System against each other in a comparative Fitts' law study
+__Completed__
+Winter, 2013
+====== Imputation of missing values in microarray data ======
+**Student**:  Michael Larin
+**Supervisor**:  S. Datta
+__Description__
+Microarrays are a relatively new technology that have had tremendous
+impact on many areas within biology and bioinformatics.  Microarray
+technology enables researchers to study the behaviour of many genes
+and/or conditions in a single experiment.
+Due to technological limitations and experiment design issues,
+microarray data sets typically have several missing values.  It has been
+shown that imputation of these values improves the accuracy of
+different processing tasks, including clustering, that are typically
+done on these data sets.  Therefore, good imputation algorithms are
+required.
+In this project, we will explore fast and accurate imputation algorithms
+for microarray data.  The student will first read the papers assigned
+and write a short summary of them.  Then, he will study the performance
+a few algorithms from the literature (many algorithms are already
+implemented but 1 - 2 may need to be implemented).  Finally, he will
+work with the supervisor on the design of better algorithms for the
+problem being studied.  He will use publicly available data sets to
+compare the performance (accuracy and speed) of the new algorithm(s) to
+the GMCImpute algorithm and several other existing ones.
+Throughout the course, the student is required to maintain a course
+website to report any progress and details about the project.
+====== An Open Source Structural Equation Modeling Graph Drawing Application ======
+**Student**:  Doug Scheurich
+**Supervisor**:  J. Edmonds
+__Description__
+Structural equation modeling (SEM) is a statistical technique that is becoming increasingly popular in the sciences. SEM allows researchers to test the validity of hypothesized models involving complex relationships among multiple variables. These models can include latent variables, which are not measured directly but are constructs inferred by observed variables. Structural equation models can be represented visually by graphs. To generate such graphs currently in R would require over 80 lines of code which has no reusability and has to be re written each time a new graph has to be developed or analyzed (R is a UNIX based command line only program, however it is a very powerful analytic research tool).
+Collected data is used to estimate the parameters of the equations and assessing the fit of the model. There are several SEM software options available to researchers, however all have serious limitations (Windows only, Unix only, expensive licensing fees, text based or command line only, no GUI, etc).
+We propose developing an application which will allow the user to load observed variables from a data file to create graphs, or allow using an intuitive graphical interface, and convert the graphs into a text based model specification file (ie generate the code required so the graph can be used in other programs such as R).  This text file can then be used as input for the sem() function in R.  The application will be implemented in Java, which can then be used with any OS.  Later versions may include the ability to call R functions directly from within the application and provide options for more advanced structural equation modeling techniques.
 ====== Exploring the notion of Variability in Business Process Modeling (and its relationship with Goals) ======