<div dir="ltr"><div dir="ltr"><div class="gmail_default" style="font-size:small"><div class="gmail_default"><div style="color:rgb(80,0,80)"><font face="arial, sans-serif"><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b>    </font></font><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit">    Mon<span class="gmail_default">day, April 11th</span> at<b> <span style="background-color:rgb(255,255,0)">11:30 am CT</span></b></font></font></font></div><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;color:rgb(80,0,80);margin:0px"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b><span style="background-color:rgb(255,255,0)"><br></span></b></font></font></font></p><div class="gmail_default"><font style="color:rgb(0,0,0);font-family:arial,sans-serif;vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b>       </font></font><span style="font-family:arial,sans-serif"><font color="#000000">Zoom Virtual Talk </font><font color="#500050">(</font></span><b style="color:rgb(80,0,80);font-family:arial,sans-serif"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_TkyVf0mWQjWq0a6PjUZNPg" target="_blank"><font color="#0000ff">register in advance here</font></a></b><span style="color:rgb(80,0,80);font-family:arial,sans-serif">)</span><br></div><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><font color="#000000"><b>Who: </b> </font><font color="#500050">    </font><font color="#000000">    </font></font></font></font><span style="color:rgb(34,34,34)">Sam Buchanan, Columbia University</span></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><br></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><br></p><div style="color:rgb(80,0,80)"><b style="color:rgb(34,34,34)">Title:          </b><span style="color:rgb(34,34,34)">Deep Networks Through the Lens of Low-Dimensional Structure</span><br style="color:rgb(34,34,34)"><br style="color:rgb(34,34,34)"><b style="color:rgb(34,34,34)">Abstract: </b><span style="color:rgb(34,34,34)">I will describe two recent works that study the interactions between deep neural networks and data with low-dimensional geometric structure.  First, I will discuss the multiple manifold problem, a binary classification task that uses a deep fully-connected neural network to classify data drawn from two disjoint submanifolds of the unit sphere.  We obtain in this model the first end-to-end algorithmic result for guaranteed classification of low-dimensional nonlinear manifold data with deep neural networks, as well as essentially optimal rates of concentration for features and gradients in randomly-initialized ReLU networks.  Second, I will discuss a conceptual approach to deriving resource-efficient invariant neural network architectures for computing with visual data, and demonstrate proofs-of-concept on simple vision-inspired tasks.  Together, these results suggest a promising approach to studying questions of resource-efficiency and performance for deep neural networks, with implications for practical computation.</span><br style="color:rgb(34,34,34)"><br style="color:rgb(34,34,34)"><b style="color:rgb(34,34,34)">Bio: </b><span style="color:rgb(34,34,34)">Sam Buchanan is a Ph.D. candidate in the Electrical Engineering Department at Columbia University, advised by Prof. John Wright. His research interests include the theoretical analysis of deep neural networks, particularly in connection with structured data, and associated applications in machine learning and signal processing. He is a 2017 U.S. Department of Defense NDSEG Fellow. </span><br></div><div style="color:rgb(80,0,80)"><font face="arial, sans-serif"><b><span class="gmail_default"><br></span></b></font></div><div class="gmail_default" style="color:rgb(80,0,80)"><font face="arial, sans-serif"><b>Host: <a href="mailto:mcallester@ttic.edu" target="_blank">David McAllester</a></b></font></div><br></div><div class="gmail_default"><br></div><div class="gmail_default"><br style="color:rgb(80,0,80)"></div></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><span style="font-family:arial,helvetica,sans-serif;font-size:x-small">Mary C. Marre</span><br></div><div><div><font face="arial, helvetica, sans-serif" size="1">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1">6045 S. Kenwood Avenue</font></i></div><div><font size="1"><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL  60637</font></i><br></font></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif" size="1">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, Apr 11, 2022 at 10:25 AM Mary Marre <<a href="mailto:mmarre@ttic.edu">mmarre@ttic.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div dir="ltr"><div style="font-size:small"><div style="color:rgb(80,0,80)"><font face="arial, sans-serif"><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b>    </font></font><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit">    Mon<span class="gmail_default">day, April 11th</span> at<b> <span style="background-color:rgb(255,255,0)">11:30 am CT</span></b></font></font></font></div><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;color:rgb(80,0,80);margin:0px"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b><span style="background-color:rgb(255,255,0)"><br></span></b></font></font></font></p><div><font style="color:rgb(0,0,0);font-family:arial,sans-serif;vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b>       </font></font><span style="font-family:arial,sans-serif"><font color="#000000">Zoom Virtual Talk </font><font color="#500050">(</font></span><b style="color:rgb(80,0,80);font-family:arial,sans-serif"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_TkyVf0mWQjWq0a6PjUZNPg" target="_blank"><font color="#0000ff">register in advance here</font></a></b><span style="color:rgb(80,0,80);font-family:arial,sans-serif">)</span><br></div><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><font color="#000000"><b>Who: </b> </font><font color="#500050">    </font><font color="#000000">    </font></font></font></font><span style="color:rgb(34,34,34)">Sam Buchanan, Columbia University</span></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><br></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><br></p><div style="color:rgb(80,0,80)"><b style="color:rgb(34,34,34)">Title:          </b><span style="color:rgb(34,34,34)">Deep Networks Through the Lens of Low-Dimensional Structure</span><br style="color:rgb(34,34,34)"><br style="color:rgb(34,34,34)"><b style="color:rgb(34,34,34)">Abstract: </b><span style="color:rgb(34,34,34)">I will describe two recent works that study the interactions between deep neural networks and data with low-dimensional geometric structure.  First, I will discuss the multiple manifold problem, a binary classification task that uses a deep fully-connected neural network to classify data drawn from two disjoint submanifolds of the unit sphere.  We obtain in this model the first end-to-end algorithmic result for guaranteed classification of low-dimensional nonlinear manifold data with deep neural networks, as well as essentially optimal rates of concentration for features and gradients in randomly-initialized ReLU networks.  Second, I will discuss a conceptual approach to deriving resource-efficient invariant neural network architectures for computing with visual data, and demonstrate proofs-of-concept on simple vision-inspired tasks.  Together, these results suggest a promising approach to studying questions of resource-efficiency and performance for deep neural networks, with implications for practical computation.</span><br style="color:rgb(34,34,34)"><br style="color:rgb(34,34,34)"><b style="color:rgb(34,34,34)">Bio: </b><span style="color:rgb(34,34,34)">Sam Buchanan is a Ph.D. candidate in the Electrical Engineering Department at Columbia University, advised by Prof. John Wright. His research interests include the theoretical analysis of deep neural networks, particularly in connection with structured data, and associated applications in machine learning and signal processing. He is a 2017 U.S. Department of Defense NDSEG Fellow. </span><br></div><div style="color:rgb(80,0,80)"><font face="arial, sans-serif"><b><span class="gmail_default"><br></span></b></font></div><div style="color:rgb(80,0,80)"><font face="arial, sans-serif"><b>Host: <a href="mailto:mcallester@ttic.edu" target="_blank">David McAllester</a></b></font></div><br></div><div style="font-size:small"><br></div><div style="font-size:small"><br></div><div style="font-size:small"><br></div><div><div dir="ltr"><div dir="ltr"><div><span style="font-family:arial,helvetica,sans-serif;font-size:x-small">Mary C. Marre</span><br></div><div><div><font face="arial, helvetica, sans-serif" size="1">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1">6045 S. Kenwood Avenue</font></i></div><div><font size="1"><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL  60637</font></i><br></font></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif" size="1">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Sun, Apr 10, 2022 at 3:46 PM Mary Marre <<a href="mailto:mmarre@ttic.edu" target="_blank">mmarre@ttic.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div dir="ltr"><div style="font-size:small"><div><div style="color:rgb(80,0,80)"><font face="arial, sans-serif"><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b>    </font></font><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit">    Mon<span class="gmail_default">day, April 11th</span> at<b> <span style="background-color:rgb(255,255,0)"><span>11</span>:30 am CT</span></b></font></font></font></div><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;color:rgb(80,0,80);margin:0px"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b><span style="background-color:rgb(255,255,0)"><br></span></b></font></font></font></p><div><font style="color:rgb(0,0,0);font-family:arial,sans-serif;vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b>       </font></font><span style="font-family:arial,sans-serif"><font color="#000000">Zoom Virtual Talk </font><font color="#500050">(</font></span><b style="color:rgb(80,0,80);font-family:arial,sans-serif"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_TkyVf0mWQjWq0a6PjUZNPg" target="_blank"><font color="#0000ff">register in advance here</font></a></b><span style="color:rgb(80,0,80);font-family:arial,sans-serif">)</span><br></div><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><font color="#000000"><b>Who: </b> </font><font color="#500050">    </font><font color="#000000">    </font></font></font></font><span style="color:rgb(34,34,34)">Sam Buchanan, Columbia University</span></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><br></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><br></p><div style="color:rgb(80,0,80)"><b style="color:rgb(34,34,34)">Title:          </b><span style="color:rgb(34,34,34)">Deep Networks Through the Lens of Low-Dimensional Structure</span><br style="color:rgb(34,34,34)"><br style="color:rgb(34,34,34)"><b style="color:rgb(34,34,34)">Abstract: </b><span style="color:rgb(34,34,34)">I will describe two recent works that study the interactions between deep neural networks and data with low-dimensional geometric structure.  First, I will discuss the multiple manifold problem, a binary classification task that uses a deep fully-connected neural network to classify data drawn from two disjoint submanifolds of the unit sphere.  We obtain in this model the first end-to-end algorithmic result for guaranteed classification of low-dimensional nonlinear manifold data with deep neural networks, as well as essentially optimal rates of concentration for features and gradients in randomly-initialized ReLU networks.  Second, I will discuss a conceptual approach to deriving resource-efficient invariant neural network architectures for computing with visual data, and demonstrate proofs-of-concept on simple vision-inspired tasks.  Together, these results suggest a promising approach to studying questions of resource-efficiency and performance for deep neural networks, with implications for practical computation.</span><br style="color:rgb(34,34,34)"><br style="color:rgb(34,34,34)"><b style="color:rgb(34,34,34)">Bio: </b><span style="color:rgb(34,34,34)">Sam Buchanan is a Ph.D. candidate in the Electrical Engineering Department at Columbia University, advised by Prof. John Wright. His research interests include the theoretical analysis of deep neural networks, particularly in connection with structured data, and associated applications in machine learning and signal processing. He is a 2017 U.S. Department of Defense NDSEG Fellow. </span><br></div><div style="color:rgb(80,0,80)"><font face="arial, sans-serif"><b><span class="gmail_default"><br></span></b></font></div><div style="color:rgb(80,0,80)"><font face="arial, sans-serif"><b>Host: <a href="mailto:mcallester@ttic.edu" target="_blank">David McAllester</a></b></font></div><br></div><div><br></div><div><br></div><div><br></div></div><div><div dir="ltr"><div dir="ltr"><div><span style="font-family:arial,helvetica,sans-serif;font-size:x-small">Mary C. Marre</span><br></div><div><div><font face="arial, helvetica, sans-serif" size="1">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1">6045 S. Kenwood Avenue</font></i></div><div><font size="1"><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL  60637</font></i><br></font></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif" size="1">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, Apr 4, 2022 at 6:01 PM Mary Marre <<a href="mailto:mmarre@ttic.edu" target="_blank">mmarre@ttic.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div style="font-size:small"><div style="color:rgb(80,0,80)"><font face="arial, sans-serif"><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b>    </font></font><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit">    Mon<span class="gmail_default">day, April 11th</span> at<b> <span style="background-color:rgb(255,255,0)">11:30 am CT</span></b></font></font></font></div><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;color:rgb(80,0,80);margin:0px"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b><span style="background-color:rgb(255,255,0)"><br></span></b></font></font></font></p><div><font style="color:rgb(0,0,0);font-family:arial,sans-serif;vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b>       </font></font><span style="font-family:arial,sans-serif"><font color="#000000">Zoom Virtual Talk </font><font color="#500050">(</font></span><b style="color:rgb(80,0,80);font-family:arial,sans-serif"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_TkyVf0mWQjWq0a6PjUZNPg" target="_blank"><font color="#0000ff">register in advance here</font></a></b><span style="color:rgb(80,0,80);font-family:arial,sans-serif">)</span><br></div><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><font color="#000000"><b>Who: </b> </font><font color="#500050">    </font><font color="#000000">    </font></font></font></font><span style="color:rgb(34,34,34)">Sam Buchanan, Columbia University</span></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><br></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><br></p><div style="color:rgb(80,0,80)"><b style="color:rgb(34,34,34)">Title:          </b><span style="color:rgb(34,34,34)">Deep Networks Through the Lens of Low-Dimensional Structure</span><br style="color:rgb(34,34,34)"><br style="color:rgb(34,34,34)"><b style="color:rgb(34,34,34)">Abstract: </b><span style="color:rgb(34,34,34)">I will describe two recent works that study the interactions between deep neural networks and data with low-dimensional geometric structure.  First, I will discuss the multiple manifold problem, a binary classification task that uses a deep fully-connected neural network to classify data drawn from two disjoint submanifolds of the unit sphere.  We obtain in this model the first end-to-end algorithmic result for guaranteed classification of low-dimensional nonlinear manifold data with deep neural networks, as well as essentially optimal rates of concentration for features and gradients in randomly-initialized ReLU networks.  Second, I will discuss a conceptual approach to deriving resource-efficient invariant neural network architectures for computing with visual data, and demonstrate proofs-of-concept on simple vision-inspired tasks.  Together, these results suggest a promising approach to studying questions of resource-efficiency and performance for deep neural networks, with implications for practical computation.</span><br style="color:rgb(34,34,34)"><br style="color:rgb(34,34,34)"><b style="color:rgb(34,34,34)">Bio: </b><span style="color:rgb(34,34,34)">Sam Buchanan is a Ph.D. candidate in the Electrical Engineering Department at Columbia University, advised by Prof. John Wright. His research interests include the theoretical analysis of deep neural networks, particularly in connection with structured data, and associated applications in machine learning and signal processing. He is a 2017 U.S. Department of Defense NDSEG Fellow. </span><br></div><div style="color:rgb(80,0,80)"><font face="arial, sans-serif"><b><span class="gmail_default"><br></span></b></font></div><div style="color:rgb(80,0,80)"><font face="arial, sans-serif"><b>Host: <a href="mailto:mcallester@ttic.edu" target="_blank">David McAllester</a></b></font></div><br></div><div style="font-size:small"><br></div><div style="font-size:small"><br></div><div style="font-size:small"><br></div><div><div dir="ltr"><div dir="ltr"><div><span style="font-family:arial,helvetica,sans-serif;font-size:x-small">Mary C. Marre</span><br></div><div><div><font face="arial, helvetica, sans-serif" size="1">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1">6045 S. Kenwood Avenue</font></i></div><div><font size="1"><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL  60637</font></i><br></font></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif" size="1">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div>
</blockquote></div></div>
</blockquote></div></div>
</blockquote></div></div>