https://seancarver.org/index.php?title=Sean_G._Carver%27s_Current_Research_and_Data_Science_Projects&feed=atom&action=historySean G. Carver's Current Research and Data Science Projects - Revision history2024-03-29T07:35:43ZRevision history for this page on the wikiMediaWiki 1.28.2https://seancarver.org/index.php?title=Sean_G._Carver%27s_Current_Research_and_Data_Science_Projects&diff=3936&oldid=prevCarver at 15:45, 17 July 20212021-07-17T15:45:34Z<p></p>
<table class="diff diff-contentalign-left" data-mw="interface">
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<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 15:45, 17 July 2021</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l10" >Line 10:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Conference Proceeding: I presented this work at the Joint Statistical Meeting (JSM), Baltimore, August 2017.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Conference Proceeding: I presented this work at the Joint Statistical Meeting (JSM), Baltimore, August 2017.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* '''Markov Baseball:''' I have invented a Markov Game based on the Markov Chain Model of Baseball.  It captures many of the subtleties of <del class="diffchange diffchange-inline">the game</del>.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* '''Markov Baseball:''' I have invented a Markov Game based on the Markov Chain Model of Baseball.  It captures many of the subtleties of <ins class="diffchange diffchange-inline">true Baseball</ins>.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Unprecedented Half-innings:'''  Labeling the states of the Markov Chain Model of Baseball, I have come up with a label for each half-inning, then using a derived SQL database discovered which labels have appeared in major league play and which haven't.  I have received encouragement from many Baseball fans who find the statistics fascinating.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Unprecedented Half-innings:'''  Labeling the states of the Markov Chain Model of Baseball, I have come up with a label for each half-inning, then using a derived SQL database discovered which labels have appeared in major league play and which haven't.  I have received encouragement from many Baseball fans who find the statistics fascinating.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* '''Motor Control:'''  With my student collaborators, I have been looking at continuation tapping, an experimental paradigm involving a metronome, and subject tapping to the beat.  After a sufficient duration of time, the metronome stops, and the subject must keep the same rhythm.  In an effort to perform system identification of the internal clock for motor control, we found that the inter-tap intervals are fit equally well by the Normal and the Inverse Gaussian distributions, and both fit much better than the Laplace distribution.  Contrary to this finding, the only relevant study in the literature we discovered, a review paper, reported that inter-tap intervals have a Laplace distribution.  This review paper did not provide many details about how the data were collected and analyzed.  I <del class="diffchange diffchange-inline">am working </del>with Daniel Scanlan to explore, through simulations, when models of continuation tapping produce data that fit the Inverse Gaussian/Normal distributions (these two are almost identical at our parameters) and when they produce data that fit the Laplacian distribution.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* '''Motor Control:'''  With my student collaborators, I have been looking at continuation tapping, an experimental paradigm involving a metronome, and subject tapping to the beat.  After a sufficient duration of time, the metronome stops, and the subject must keep the same rhythm.  In an effort to perform system identification of the internal clock for motor control, we found that the inter-tap intervals are fit equally well by the Normal and the Inverse Gaussian distributions, and both fit much better than the Laplace distribution.  Contrary to this finding, the only relevant study in the literature we discovered, a review paper, reported that inter-tap intervals have a Laplace distribution.  This review paper did not provide many details about how the data were collected and analyzed.  I <ins class="diffchange diffchange-inline">worked </ins>with <ins class="diffchange diffchange-inline">undergrauate student </ins>Daniel Scanlan to explore, through simulations, when models of continuation tapping produce data that fit the Inverse Gaussian/Normal distributions (these two are almost identical at our parameters) and when they produce data that fit the Laplacian distribution.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborators: (former) Daniel Scanlan, (former) Wasim Ashshowaf, and (former) Alexander Spinos</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborators: (former) Daniel Scanlan, (former) Wasim Ashshowaf, and (former) Alexander Spinos</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Server administration, data collection, data storage, and data analysis:''' I set up a [http://stat370.com web server] in my home to be used by my students in a recent statistics class I taught.  I had also wanted to let my students run background processes collecting data from the web for analysis (either through APIs or through scraping and crawling the web).  It became clear that the best way for students to collect data in this way is to have them rent their own server from Amazon Web Services (AWS).  This summer I am going to work with Jennifer Schaffer, a student from the class, to collect, archive, then analyze a large volume of tweets concerning the U.S. politics using AWS.  We plan to use MongoDB to store the tweets, but we have not determined how they will be analyzed.  We also plan to look at Twitter's follower network, using a Neo4j database.  Finally, I plan to migrate the web server from my own machine to a (separate) AWS host, and keep it active for students, past, present, and future, to use.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Server administration, data collection, data storage, and data analysis:''' I set up a [http://stat370.com web server] in my home to be used by my students in a recent statistics class I taught.  I had also wanted to let my students run background processes collecting data from the web for analysis (either through APIs or through scraping and crawling the web).  It became clear that the best way for students to collect data in this way is to have them rent their own server from Amazon Web Services (AWS).  This summer I am going to work with Jennifer Schaffer, a student from the class, to collect, archive, then analyze a large volume of tweets concerning the U.S. politics using AWS.  We plan to use MongoDB to store the tweets, but we have not determined how they will be analyzed.  We also plan to look at Twitter's follower network, using a Neo4j database.  Finally, I plan to migrate the web server from my own machine to a (separate) AWS host, and keep it active for students, past, present, and future, to use.</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborator <del class="diffchange diffchange-inline">(just graduated, but still working with me)</del>: Jennifer Schaffer</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborator: Jennifer Schaffer</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Twitter and Viral Tweats:'''  I worked with one of my students on posing a project for analysis.  Using word embeddings, we attempted to quantify tweats on a ''love/fear'' axis, and we planned to see how that correlated with retweats.  The project never reached conclusion but we learned a lot in the process.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Twitter and Viral Tweats:'''  I worked with one of my students on posing a project for analysis.  Using word embeddings, we attempted to quantify tweats on a ''love/fear'' axis, and we planned to see how that correlated with retweats.  The project never reached conclusion but we learned a lot in the process.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Cashflow:''' As a fun project to learn more about SQL and Regular Expressions, I set up my computer to ingest data from my bank and credit card company.  I plan to archive it in a database, and provide regular reports.  The code is presently in a private repository.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Cashflow:''' As a fun project to learn more about SQL and Regular Expressions, I set up my computer to ingest data from my bank and credit card company.  I plan to archive it in a database, and provide regular reports.  The code is presently in a private repository.</div></td></tr>
</table>Carverhttps://seancarver.org/index.php?title=Sean_G._Carver%27s_Current_Research_and_Data_Science_Projects&diff=3935&oldid=prevCarver at 15:37, 17 July 20212021-07-17T15:37:24Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 15:37, 17 July 2021</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l12" >Line 12:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Markov Baseball:''' I have invented a Markov Game based on the Markov Chain Model of Baseball.  It captures many of the subtleties of the game.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Markov Baseball:''' I have invented a Markov Game based on the Markov Chain Model of Baseball.  It captures many of the subtleties of the game.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* '''Unprecedented Half-innings:'''  Labeling the states of the Markov Chain Model of Baseball, I have come up with a label for each half-inning, then using a derived SQL database discovered which labels have appeared in major league play and which haven't.  I have received encouragement from many Baseball fans who find the statistics <del class="diffchange diffchange-inline">interesting</del>.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* '''Unprecedented Half-innings:'''  Labeling the states of the Markov Chain Model of Baseball, I have come up with a label for each half-inning, then using a derived SQL database discovered which labels have appeared in major league play and which haven't.  I have received encouragement from many Baseball fans who find the statistics <ins class="diffchange diffchange-inline">fascinating</ins>.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Motor Control:'''  With my student collaborators, I have been looking at continuation tapping, an experimental paradigm involving a metronome, and subject tapping to the beat.  After a sufficient duration of time, the metronome stops, and the subject must keep the same rhythm.  In an effort to perform system identification of the internal clock for motor control, we found that the inter-tap intervals are fit equally well by the Normal and the Inverse Gaussian distributions, and both fit much better than the Laplace distribution.  Contrary to this finding, the only relevant study in the literature we discovered, a review paper, reported that inter-tap intervals have a Laplace distribution.  This review paper did not provide many details about how the data were collected and analyzed.  I am working with Daniel Scanlan to explore, through simulations, when models of continuation tapping produce data that fit the Inverse Gaussian/Normal distributions (these two are almost identical at our parameters) and when they produce data that fit the Laplacian distribution.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Motor Control:'''  With my student collaborators, I have been looking at continuation tapping, an experimental paradigm involving a metronome, and subject tapping to the beat.  After a sufficient duration of time, the metronome stops, and the subject must keep the same rhythm.  In an effort to perform system identification of the internal clock for motor control, we found that the inter-tap intervals are fit equally well by the Normal and the Inverse Gaussian distributions, and both fit much better than the Laplace distribution.  Contrary to this finding, the only relevant study in the literature we discovered, a review paper, reported that inter-tap intervals have a Laplace distribution.  This review paper did not provide many details about how the data were collected and analyzed.  I am working with Daniel Scanlan to explore, through simulations, when models of continuation tapping produce data that fit the Inverse Gaussian/Normal distributions (these two are almost identical at our parameters) and when they produce data that fit the Laplacian distribution.</div></td></tr>
</table>Carverhttps://seancarver.org/index.php?title=Sean_G._Carver%27s_Current_Research_and_Data_Science_Projects&diff=3934&oldid=prevCarver at 15:30, 17 July 20212021-07-17T15:30:21Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 15:30, 17 July 2021</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l9" >Line 9:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborators: Rebeca Berger (graduated 2017), Jake Berberian (Class of '22), Kingsley Iyawe Masters Student expected to graduate May 2020.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborators: Rebeca Berger (graduated 2017), Jake Berberian (Class of '22), Kingsley Iyawe Masters Student expected to graduate May 2020.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Conference Proceeding: I presented this work at the Joint Statistical Meeting (JSM), Baltimore, August 2017.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Conference Proceeding: I presented this work at the Joint Statistical Meeting (JSM), Baltimore, August 2017.</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* '''Markov Baseball:''' I have invented a Markov Game based on the Markov Chain Model of Baseball.  It captures many of the subtleties of the game.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* '''Unprecedented Half-innings:'''  Labeling the states of the Markov Chain Model of Baseball, I have come up with a label for each half-inning, then using a derived SQL database discovered which labels have appeared in major league play and which haven't.  I have received encouragement from many Baseball fans who find the statistics interesting.</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Motor Control:'''  With my student collaborators, I have been looking at continuation tapping, an experimental paradigm involving a metronome, and subject tapping to the beat.  After a sufficient duration of time, the metronome stops, and the subject must keep the same rhythm.  In an effort to perform system identification of the internal clock for motor control, we found that the inter-tap intervals are fit equally well by the Normal and the Inverse Gaussian distributions, and both fit much better than the Laplace distribution.  Contrary to this finding, the only relevant study in the literature we discovered, a review paper, reported that inter-tap intervals have a Laplace distribution.  This review paper did not provide many details about how the data were collected and analyzed.  I am working with Daniel Scanlan to explore, through simulations, when models of continuation tapping produce data that fit the Inverse Gaussian/Normal distributions (these two are almost identical at our parameters) and when they produce data that fit the Laplacian distribution.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Motor Control:'''  With my student collaborators, I have been looking at continuation tapping, an experimental paradigm involving a metronome, and subject tapping to the beat.  After a sufficient duration of time, the metronome stops, and the subject must keep the same rhythm.  In an effort to perform system identification of the internal clock for motor control, we found that the inter-tap intervals are fit equally well by the Normal and the Inverse Gaussian distributions, and both fit much better than the Laplace distribution.  Contrary to this finding, the only relevant study in the literature we discovered, a review paper, reported that inter-tap intervals have a Laplace distribution.  This review paper did not provide many details about how the data were collected and analyzed.  I am working with Daniel Scanlan to explore, through simulations, when models of continuation tapping produce data that fit the Inverse Gaussian/Normal distributions (these two are almost identical at our parameters) and when they produce data that fit the Laplacian distribution.</div></td></tr>
</table>Carverhttps://seancarver.org/index.php?title=Sean_G._Carver%27s_Current_Research_and_Data_Science_Projects&diff=3933&oldid=prevCarver at 15:24, 17 July 20212021-07-17T15:24:16Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 15:24, 17 July 2021</td>
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<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">'''Much of my current research involves projects related to the statistical analysis of models using simulated data:'''</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"></del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Recent Projects:'''</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Recent Projects:'''</div></td></tr>
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</table>Carverhttps://seancarver.org/index.php?title=Sean_G._Carver%27s_Current_Research_and_Data_Science_Projects&diff=3932&oldid=prevCarver at 15:23, 17 July 20212021-07-17T15:23:45Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Recent Projects:'''</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Recent Projects:'''</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>At Data Machines Corp. I worked on a project involving automation of data science tasks through a DARPA funded competition.  Different performers submitted up to 20 automatically generated data science pipelines each for solving various data science problems.  I was asked to evaluate whether "diverse" collections of primitives were better than similar primitives with different hyperparameters.  Using heirarchical regression, I found a statistically significant result concluded that the effect size was small enough that it did not warrent changing the instructions given to performers.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">* '''Effectively Automating Data Science:'''  </ins>At Data Machines Corp. I worked on a project involving automation of data science tasks through a DARPA funded competition.  Different performers submitted up to 20 automatically generated data science pipelines each for solving various data science problems.  I was asked to evaluate whether "diverse" collections of primitives were better than similar primitives with different hyperparameters.  Using heirarchical regression, I found a statistically significant result concluded that the effect size was small enough that it did not warrent changing the instructions given to performers.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Overlapping software projects:''' [https://github.com/seancarverphd/klir KLI-R] (R/Github/Git) and [https://bitbucket.org/seancarverphd/kli/ KLI] (Python/Bitbucket/Mercurial).  KLI stands for "Kullback-Leibler Interactive."  These projects involve, among other things, computing the number of samples needed to reject an alternative model with the likelihood ratio test, in favor of a true model that produces the data.   </div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Overlapping software projects:''' [https://github.com/seancarverphd/klir KLI-R] (R/Github/Git) and [https://bitbucket.org/seancarverphd/kli/ KLI] (Python/Bitbucket/Mercurial).  KLI stands for "Kullback-Leibler Interactive."  These projects involve, among other things, computing the number of samples needed to reject an alternative model with the likelihood ratio test, in favor of a true model that produces the data.   </div></td></tr>
</table>Carverhttps://seancarver.org/index.php?title=Sean_G._Carver%27s_Current_Research_and_Data_Science_Projects&diff=3931&oldid=prevCarver at 15:22, 17 July 20212021-07-17T15:22:31Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 15:22, 17 July 2021</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Much of my current research involves projects related to the statistical analysis of models using simulated data:'''</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Much of my current research involves projects related to the statistical analysis of models using simulated data:'''</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">'''Recent Projects:'''</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">At Data Machines Corp. I worked on a project involving automation of data science tasks through a DARPA funded competition.  Different performers submitted up to 20 automatically generated data science pipelines each for solving various data science problems.  I was asked to evaluate whether "diverse" collections of primitives were better than similar primitives with different hyperparameters.  Using heirarchical regression, I found a statistically significant result concluded that the effect size was small enough that it did not warrent changing the instructions given to performers.</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Overlapping software projects:''' [https://github.com/seancarverphd/klir KLI-R] (R/Github/Git) and [https://bitbucket.org/seancarverphd/kli/ KLI] (Python/Bitbucket/Mercurial).  KLI stands for "Kullback-Leibler Interactive."  These projects involve, among other things, computing the number of samples needed to reject an alternative model with the likelihood ratio test, in favor of a true model that produces the data.   </div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Overlapping software projects:''' [https://github.com/seancarverphd/klir KLI-R] (R/Github/Git) and [https://bitbucket.org/seancarverphd/kli/ KLI] (Python/Bitbucket/Mercurial).  KLI stands for "Kullback-Leibler Interactive."  These projects involve, among other things, computing the number of samples needed to reject an alternative model with the likelihood ratio test, in favor of a true model that produces the data.   </div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Server administration, data collection, data storage, and data analysis:''' I set up a [http://stat370.com web server] in my home to be used by my students in a recent statistics class I taught.  I had also wanted to let my students run background processes collecting data from the web for analysis (either through APIs or through scraping and crawling the web).  It became clear that the best way for students to collect data in this way is to have them rent their own server from Amazon Web Services (AWS).  This summer I am going to work with Jennifer Schaffer, a student from the class, to collect, archive, then analyze a large volume of tweets concerning the U.S. politics using AWS.  We plan to use MongoDB to store the tweets, but we have not determined how they will be analyzed.  We also plan to look at Twitter's follower network, using a Neo4j database.  Finally, I plan to migrate the web server from my own machine to a (separate) AWS host, and keep it active for students, past, present, and future, to use.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Server administration, data collection, data storage, and data analysis:''' I set up a [http://stat370.com web server] in my home to be used by my students in a recent statistics class I taught.  I had also wanted to let my students run background processes collecting data from the web for analysis (either through APIs or through scraping and crawling the web).  It became clear that the best way for students to collect data in this way is to have them rent their own server from Amazon Web Services (AWS).  This summer I am going to work with Jennifer Schaffer, a student from the class, to collect, archive, then analyze a large volume of tweets concerning the U.S. politics using AWS.  We plan to use MongoDB to store the tweets, but we have not determined how they will be analyzed.  We also plan to look at Twitter's follower network, using a Neo4j database.  Finally, I plan to migrate the web server from my own machine to a (separate) AWS host, and keep it active for students, past, present, and future, to use.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborator (just graduated, but still working with me): Jennifer Schaffer</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborator (just graduated, but still working with me): Jennifer Schaffer</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* '''Twitter and Viral Tweats:'''  I worked with one of my students on posing a project for analysis.  Using word embeddings, we attempted to quantify tweats on a ''love/fear'' axis, and we planned to see how that correlated with retweats.  The project never reached conclusion but we learned a lot in the process.</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Cashflow:''' As a fun project to learn more about SQL and Regular Expressions, I set up my computer to ingest data from my bank and credit card company.  I plan to archive it in a database, and provide regular reports.  The code is presently in a private repository.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Cashflow:''' As a fun project to learn more about SQL and Regular Expressions, I set up my computer to ingest data from my bank and credit card company.  I plan to archive it in a database, and provide regular reports.  The code is presently in a private repository.</div></td></tr>
</table>Carverhttps://seancarver.org/index.php?title=Sean_G._Carver%27s_Current_Research_and_Data_Science_Projects&diff=3930&oldid=prevCarver at 15:08, 17 July 20212021-07-17T15:08:10Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>*'''Ion Channels in Neuroscience:'''  Ion channels provide much of the molecular basis for neural signaling. Models of ion channels are continuous time Markov chains with hidden states, far more complicated than any of the applications above.  Much of the KLI in python code involves these models of ion channels.  [[Media:Sfn2015.pdf|PDF of poster presented to Society of Neuroscience, 2015]]</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>*'''Ion Channels in Neuroscience:'''  Ion channels provide much of the molecular basis for neural signaling. Models of ion channels are continuous time Markov chains with hidden states, far more complicated than any of the applications above.  Much of the KLI in python code involves these models of ion channels.  [[Media:Sfn2015.pdf|PDF of poster presented to Society of Neuroscience, 2015]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>'''Other projects:'''</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>'''Other <ins class="diffchange diffchange-inline">past </ins>projects:'''</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Analysis of data from the whole brain larval zebrafish at cellular resolution.'''  A collaborator from Janelia Research Institute, [https://www.janelia.org/people/misha-ahrens Misha Ahrens] provided me with a data set that consisted of about 100,000 time series, one from each of 90% of the neurons in a larval zebrafish. The time series consisted of measurement of calcium from within their respective cells, using a calcium sensitive dye. The fish were involved in a closed-loop sensorimotor behavior, similar to swimming "like Neo in the matrix."  Specifically the fish were actually immobilize in a microscope, but were given a visual stimulus, as if they were swimming.  The harder their nervous system commanded the tail to wag, the faster the stimulus moved, in the appropriate direction.  Each time series had the same length, about 4000 samples.  I looked at the intrinsic dimension of the 4000 points embedded in 100,000 dimensions.  I found that the intrinsic dimension was about 15.  I plan to see if this result is consistent from fish to fish, and also look at the persistent cohomology of the data.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Analysis of data from the whole brain larval zebrafish at cellular resolution.'''  A collaborator from Janelia Research Institute, [https://www.janelia.org/people/misha-ahrens Misha Ahrens] provided me with a data set that consisted of about 100,000 time series, one from each of 90% of the neurons in a larval zebrafish. The time series consisted of measurement of calcium from within their respective cells, using a calcium sensitive dye. The fish were involved in a closed-loop sensorimotor behavior, similar to swimming "like Neo in the matrix."  Specifically the fish were actually immobilize in a microscope, but were given a visual stimulus, as if they were swimming.  The harder their nervous system commanded the tail to wag, the faster the stimulus moved, in the appropriate direction.  Each time series had the same length, about 4000 samples.  I looked at the intrinsic dimension of the 4000 points embedded in 100,000 dimensions.  I found that the intrinsic dimension was about 15.  I plan to see if this result is consistent from fish to fish, and also look at the persistent cohomology of the data.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* '''Server administration, data collection, data storage, and data analysis:''' <del class="diffchange diffchange-inline">[2018] </del>I set up a [http://stat370.com web server] in my home to be used by my students in a recent statistics class I taught.  I had also wanted to let my students run background processes collecting data from the web for analysis (either through APIs or through scraping and crawling the web).  It became clear that the best way for students to collect data in this way is to have them rent their own server from Amazon Web Services (AWS).  This summer I am going to work with Jennifer Schaffer, a student from the class, to collect, archive, then analyze a large volume of tweets concerning the U.S. politics using AWS.  We plan to use MongoDB to store the tweets, but we have not determined how they will be analyzed.  We also plan to look at Twitter's follower network, using a Neo4j database.  Finally, I plan to migrate the web server from my own machine to a (separate) AWS host, and keep it active for students, past, present, and future, to use.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* '''Server administration, data collection, data storage, and data analysis:''' I set up a [http://stat370.com web server] in my home to be used by my students in a recent statistics class I taught.  I had also wanted to let my students run background processes collecting data from the web for analysis (either through APIs or through scraping and crawling the web).  It became clear that the best way for students to collect data in this way is to have them rent their own server from Amazon Web Services (AWS).  This summer I am going to work with Jennifer Schaffer, a student from the class, to collect, archive, then analyze a large volume of tweets concerning the U.S. politics using AWS.  We plan to use MongoDB to store the tweets, but we have not determined how they will be analyzed.  We also plan to look at Twitter's follower network, using a Neo4j database.  Finally, I plan to migrate the web server from my own machine to a (separate) AWS host, and keep it active for students, past, present, and future, to use.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborator (just graduated, but still working with me): Jennifer Schaffer</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborator (just graduated, but still working with me): Jennifer Schaffer</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Cashflow:''' As a fun project to learn more about SQL and Regular Expressions, I set up my computer to ingest data from my bank and credit card company.  I plan to archive it in a database, and provide regular reports.  The code is presently in a private repository.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Cashflow:''' As a fun project to learn more about SQL and Regular Expressions, I set up my computer to ingest data from my bank and credit card company.  I plan to archive it in a database, and provide regular reports.  The code is presently in a private repository.</div></td></tr>
</table>Carverhttps://seancarver.org/index.php?title=Sean_G._Carver%27s_Current_Research_and_Data_Science_Projects&diff=3929&oldid=prevCarver at 15:06, 17 July 20212021-07-17T15:06:36Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 15:06, 17 July 2021</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Analysis of data from the whole brain larval zebrafish at cellular resolution.'''  A collaborator from Janelia Research Institute, [https://www.janelia.org/people/misha-ahrens Misha Ahrens] provided me with a data set that consisted of about 100,000 time series, one from each of 90% of the neurons in a larval zebrafish. The time series consisted of measurement of calcium from within their respective cells, using a calcium sensitive dye. The fish were involved in a closed-loop sensorimotor behavior, similar to swimming "like Neo in the matrix."  Specifically the fish were actually immobilize in a microscope, but were given a visual stimulus, as if they were swimming.  The harder their nervous system commanded the tail to wag, the faster the stimulus moved, in the appropriate direction.  Each time series had the same length, about 4000 samples.  I looked at the intrinsic dimension of the 4000 points embedded in 100,000 dimensions.  I found that the intrinsic dimension was about 15.  I plan to see if this result is consistent from fish to fish, and also look at the persistent cohomology of the data.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Analysis of data from the whole brain larval zebrafish at cellular resolution.'''  A collaborator from Janelia Research Institute, [https://www.janelia.org/people/misha-ahrens Misha Ahrens] provided me with a data set that consisted of about 100,000 time series, one from each of 90% of the neurons in a larval zebrafish. The time series consisted of measurement of calcium from within their respective cells, using a calcium sensitive dye. The fish were involved in a closed-loop sensorimotor behavior, similar to swimming "like Neo in the matrix."  Specifically the fish were actually immobilize in a microscope, but were given a visual stimulus, as if they were swimming.  The harder their nervous system commanded the tail to wag, the faster the stimulus moved, in the appropriate direction.  Each time series had the same length, about 4000 samples.  I looked at the intrinsic dimension of the 4000 points embedded in 100,000 dimensions.  I found that the intrinsic dimension was about 15.  I plan to see if this result is consistent from fish to fish, and also look at the persistent cohomology of the data.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* '''Server administration, data collection, data storage, and data analysis:''' I set up a [http://stat370.com web server] in my home to be used by my students in a recent statistics class I taught.  I had also wanted to let my students run background processes collecting data from the web for analysis (either through APIs or through scraping and crawling the web).  It became clear that the best way for students to collect data in this way is to have them rent their own server from Amazon Web Services (AWS).  This summer I am going to work with Jennifer Schaffer, a student from the class, to collect, archive, then analyze a large volume of tweets concerning the U.S. politics using AWS.  We plan to use MongoDB to store the tweets, but we have not determined how they will be analyzed.  We also plan to look at Twitter's follower network, using a Neo4j database.  Finally, I plan to migrate the web server from my own machine to a (separate) AWS host, and keep it active for students, past, present, and future, to use.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* '''Server administration, data collection, data storage, and data analysis:''' <ins class="diffchange diffchange-inline">[2018] </ins>I set up a [http://stat370.com web server] in my home to be used by my students in a recent statistics class I taught.  I had also wanted to let my students run background processes collecting data from the web for analysis (either through APIs or through scraping and crawling the web).  It became clear that the best way for students to collect data in this way is to have them rent their own server from Amazon Web Services (AWS).  This summer I am going to work with Jennifer Schaffer, a student from the class, to collect, archive, then analyze a large volume of tweets concerning the U.S. politics using AWS.  We plan to use MongoDB to store the tweets, but we have not determined how they will be analyzed.  We also plan to look at Twitter's follower network, using a Neo4j database.  Finally, I plan to migrate the web server from my own machine to a (separate) AWS host, and keep it active for students, past, present, and future, to use.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborator (just graduated, but still working with me): Jennifer Schaffer</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborator (just graduated, but still working with me): Jennifer Schaffer</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Cashflow:''' As a fun project to learn more about SQL and Regular Expressions, I set up my computer to ingest data from my bank and credit card company.  I plan to archive it in a database, and provide regular reports.  The code is presently in a private repository.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Cashflow:''' As a fun project to learn more about SQL and Regular Expressions, I set up my computer to ingest data from my bank and credit card company.  I plan to archive it in a database, and provide regular reports.  The code is presently in a private repository.</div></td></tr>
</table>Carverhttps://seancarver.org/index.php?title=Sean_G._Carver%27s_Current_Research_and_Data_Science_Projects&diff=3784&oldid=prevCarver at 14:50, 18 September 20192019-09-18T14:50:51Z<p></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 14:50, 18 September 2019</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Overlapping software projects:''' [https://github.com/seancarverphd/klir KLI-R] (R/Github/Git) and [https://bitbucket.org/seancarverphd/kli/ KLI] (Python/Bitbucket/Mercurial).  KLI stands for "Kullback-Leibler Interactive."  These projects involve, among other things, computing the number of samples needed to reject an alternative model with the likelihood ratio test, in favor of a true model that produces the data.   </div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Overlapping software projects:''' [https://github.com/seancarverphd/klir KLI-R] (R/Github/Git) and [https://bitbucket.org/seancarverphd/kli/ KLI] (Python/Bitbucket/Mercurial).  KLI stands for "Kullback-Leibler Interactive."  These projects involve, among other things, computing the number of samples needed to reject an alternative model with the likelihood ratio test, in favor of a true model that produces the data.   </div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>::<del class="diffchange diffchange-inline">Future </del>Conference Proceeding: I <del class="diffchange diffchange-inline">will present </del>this work at the Joint Statistical Meeting (JSM) in Baltimore, August 2017.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>::Conference Proceeding: I <ins class="diffchange diffchange-inline">presented </ins>this work at the Joint Statistical Meeting (JSM) in Baltimore, August 2017.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Baseball:''' how many innings must be played by model of the Baltimore Orioles (fitted from actual Orioles home games) to reject the model that the  New York Yankees are playing?  This statistic provides an interpretable way of quantifying the similarity of models.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Baseball:''' how many innings must be played by model of the Baltimore Orioles (fitted from actual Orioles home games) to reject the model that the  New York Yankees are playing?  This statistic provides an interpretable way of quantifying the similarity of models.</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>::Student <del class="diffchange diffchange-inline">Collaborator </del>(<del class="diffchange diffchange-inline">just </del>graduated, <del class="diffchange diffchange-inline">but still working with me</del>)<del class="diffchange diffchange-inline">: Rebeca Berger</del>.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>::Student <ins class="diffchange diffchange-inline">Collaborators: Rebeca Berger </ins>(graduated <ins class="diffchange diffchange-inline">2017)</ins>, <ins class="diffchange diffchange-inline">Jake Berberian (Class of '22</ins>)<ins class="diffchange diffchange-inline">, Kingsley Iyawe Masters Student expected to graduate May 2020</ins>.</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>::<del class="diffchange diffchange-inline">Future </del>Conference Proceeding: I <del class="diffchange diffchange-inline">will present </del>this work at the Joint Statistical Meeting (JSM), Baltimore, August 2017.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>::Conference Proceeding: I <ins class="diffchange diffchange-inline">presented </ins>this work at the Joint Statistical Meeting (JSM), Baltimore, August 2017.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Motor Control:'''  With my student collaborators, I have been looking at continuation tapping, an experimental paradigm involving a metronome, and subject tapping to the beat.  After a sufficient duration of time, the metronome stops, and the subject must keep the same rhythm.  In an effort to perform system identification of the internal clock for motor control, we found that the inter-tap intervals are fit equally well by the Normal and the Inverse Gaussian distributions, and both fit much better than the Laplace distribution.  Contrary to this finding, the only relevant study in the literature we discovered, a review paper, reported that inter-tap intervals have a Laplace distribution.  This review paper did not provide many details about how the data were collected and analyzed.  I am working with Daniel Scanlan to explore, through simulations, when models of continuation tapping produce data that fit the Inverse Gaussian/Normal distributions (these two are almost identical at our parameters) and when they produce data that fit the Laplacian distribution.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Motor Control:'''  With my student collaborators, I have been looking at continuation tapping, an experimental paradigm involving a metronome, and subject tapping to the beat.  After a sufficient duration of time, the metronome stops, and the subject must keep the same rhythm.  In an effort to perform system identification of the internal clock for motor control, we found that the inter-tap intervals are fit equally well by the Normal and the Inverse Gaussian distributions, and both fit much better than the Laplace distribution.  Contrary to this finding, the only relevant study in the literature we discovered, a review paper, reported that inter-tap intervals have a Laplace distribution.  This review paper did not provide many details about how the data were collected and analyzed.  I am working with Daniel Scanlan to explore, through simulations, when models of continuation tapping produce data that fit the Inverse Gaussian/Normal distributions (these two are almost identical at our parameters) and when they produce data that fit the Laplacian distribution.</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborators: (<del class="diffchange diffchange-inline">current</del>) Daniel Scanlan, (former) Wasim Ashshowaf, and (former) Alexander Spinos</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborators: (<ins class="diffchange diffchange-inline">former</ins>) Daniel Scanlan, (former) Wasim Ashshowaf, and (former) Alexander Spinos</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">::Future Paper: Daniel and I plan to submit this work for publication, probably in PLoS One.</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>*'''Ion Channels in Neuroscience:'''  Ion channels provide much of the molecular basis for neural signaling. Models of ion channels are continuous time Markov chains with hidden states, far more complicated than any of the applications above.  Much of the KLI in python code involves these models of ion channels.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>*'''Ion Channels in Neuroscience:'''  Ion channels provide much of the molecular basis for neural signaling. Models of ion channels are continuous time Markov chains with hidden states, far more complicated than any of the applications above.  Much of the KLI in python code involves these models of ion channels. <ins class="diffchange diffchange-inline"> [[Media:Sfn2015.pdf|PDF of poster presented to Society of Neuroscience, 2015]]</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Other projects:'''</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Other projects:'''</div></td></tr>
</table>Carverhttps://seancarver.org/index.php?title=Sean_G._Carver%27s_Current_Research_and_Data_Science_Projects&diff=2902&oldid=prevCarver at 03:15, 14 May 20172017-05-14T03:15:50Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Baseball:''' how many innings must be played by model of the Baltimore Orioles (fitted from actual Orioles home games) to reject the model that the  New York Yankees are playing?  This statistic provides an interpretable way of quantifying the similarity of models.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* '''Baseball:''' how many innings must be played by model of the Baltimore Orioles (fitted from actual Orioles home games) to reject the model that the  New York Yankees are playing?  This statistic provides an interpretable way of quantifying the similarity of models.</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborator (just graduated, but still working with me) Rebeca Berger.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>::Student Collaborator (just graduated, but still working with me)<ins class="diffchange diffchange-inline">: </ins>Rebeca Berger.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Future Conference Proceeding: I will present this work at the Joint Statistical Meeting (JSM), Baltimore, August 2017.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>::Future Conference Proceeding: I will present this work at the Joint Statistical Meeting (JSM), Baltimore, August 2017.</div></td></tr>
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</table>Carver