<div dir="ltr"><div dir="ltr"><div class="gmail_default" style="font-size:small"><div class="gmail_default" style="color:rgb(80,0,80)"><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;margin:0px"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b>    </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit">  Wednesday, February 17th at<b> 11:10 am CT</b></font></font><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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"><b>Where:</b>     </font></font><font color="#000000">Zoom Virtual Talk (</font><b><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_aJxlZzpcSeWJqnh6Hjmq2w" target="_blank">register in advance here</a></font></b><font color="#000000">)</font></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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"><b>Who: </b>       </font></font></font>Amirali Aghazadeh, UC Berkeley</p></div><div class="gmail_default" style="color:rgb(80,0,80)"><br></div><div class="gmail_default" style="color:rgb(80,0,80)"><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><br></span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Title</span><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.        Inferring Biological Functions with Explainable Algorithms</span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><br></span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-family:arial,sans-serif;font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Abstract</span><span style="font-family:arial,sans-serif;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. Data-driven machine learning models that infer biological functions from sequences are replacing the costly experimental measurements in a number of application areas in biology including protein, small-molecule, and genome engineering. These data-driven models largely owe their success to the recent advancements in over-parameterized models in machine learning such as deep neural networks (DNNs). However, </span><span style="font-family:arial,sans-serif;color:rgb(25,25,25);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">the number of labeled sequences available for training such models has remained small compared to the vastness of the combinatorial sequence space. In addition, these biological functions are typically complex, manifesting as rugged landscapes with high-order epistatic (nonlinear) interactions. The combination of these two factors makes the biological inference problem statistically challenging</span><span style="font-family:arial,sans-serif;color:rgb(25,25,25);font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.</span><br></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="color:rgb(25,25,25);font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> </span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="color:rgb(25,25,25);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><font face="arial, sans-serif">In this talk, I view the problem of inferring biological functions from a statistical signal processing perspective. I first discuss a fundamental interpretation-computation tradeoff in explaining DNNs in terms of their epistatic interactions. I then discuss how to develop a new hybrid algorithm that blends techniques from optimization and coding theory to regularize DNNs for inducing a biologically-relevant prior into their architecture. Our combinatorial method enables DNNs to predict protein functions using up to three times less number of sequences and explains them in terms of their higher-order epistatic interactions.</font></span></p><font face="arial, sans-serif"><br style="color:rgb(0,0,0)"><span style="color:rgb(0,0,0);font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Bio.</span><span style="color:rgb(0,0,0);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> Amirali Aghazadeh is a postdoctoral researcher in the Electrical Engineering and Computer Science department at the University of California, Berkeley, working with Kannan Ramchandran and Jennifer Listgarten. Prior to that, he was a postdoctoral researcher at Stanford University working with David Tse. He received his PhD degree in Electrical and Computer Engineering from Rice University with Richard Baraniuk in 2017. His research interest is at the intersection of machine learning, signal processing, inverse problems, and computational biology. He is the recipient of the Hershel M. Rich Invention Award for his thesis on rapid methods for DNA sensing as well as the Texas Instruments Fellowship for his graduate studies. He received his Bachelor’s degree in Electrical Engineering from Sharif University of Technology.</span></font></div><div class="gmail_default" style="color:rgb(80,0,80)"><font color="#000000" face="arial, sans-serif"><span style="white-space:pre-wrap"><br></span></font></div><div class="gmail_default" style="color:rgb(80,0,80)"><b style="font-family:arial,sans-serif">Host:</b><span style="font-family:arial,sans-serif"> </span><a href="mailto:j3xu@ttic.edu" target="_blank" style="font-family:arial,sans-serif">Jinbo Xu</a>  <font color="#000000" face="arial, sans-serif"><span style="white-space:pre-wrap"><br></span></font></div><br class="gmail-Apple-interchange-newline"></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 517</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL  60637</font></i></div><div><i><font face="arial, helvetica, sans-serif">p:(773) 834-1757</font></i></div><div><i><font face="arial, helvetica, sans-serif">f: (773) 357-6970</font></i></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, Feb 17, 2021 at 10:31 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><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;margin:0px"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b>    </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit">  Wednesday, February 17th at<b> 11:10 am CT</b></font></font><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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"><b>Where:</b>     </font></font><font color="#000000">Zoom Virtual Talk (</font><b><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_aJxlZzpcSeWJqnh6Hjmq2w" target="_blank">register in advance here</a></font></b><font color="#000000">)</font></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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"><b>Who: </b>       </font></font></font>Amirali Aghazadeh, UC Berkeley</p></div><div><br></div><div><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><br></span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Title</span><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.        Inferring Biological Functions with Explainable Algorithms</span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><br></span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-family:arial,sans-serif;font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Abstract</span><span style="font-family:arial,sans-serif;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. Data-driven machine learning models that infer biological functions from sequences are replacing the costly experimental measurements in a number of application areas in biology including protein, small-molecule, and genome engineering. These data-driven models largely owe their success to the recent advancements in over-parameterized models in machine learning such as deep neural networks (DNNs). However, </span><span style="font-family:arial,sans-serif;color:rgb(25,25,25);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">the number of labeled sequences available for training such models has remained small compared to the vastness of the combinatorial sequence space. In addition, these biological functions are typically complex, manifesting as rugged landscapes with high-order epistatic (nonlinear) interactions. The combination of these two factors makes the biological inference problem statistically challenging</span><span style="font-family:arial,sans-serif;color:rgb(25,25,25);font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.</span><br></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="color:rgb(25,25,25);font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> </span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="color:rgb(25,25,25);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><font face="arial, sans-serif">In this talk, I view the problem of inferring biological functions from a statistical signal processing perspective. I first discuss a fundamental interpretation-computation tradeoff in explaining DNNs in terms of their epistatic interactions. I then discuss how to develop a new hybrid algorithm that blends techniques from optimization and coding theory to regularize DNNs for inducing a biologically-relevant prior into their architecture. Our combinatorial method enables DNNs to predict protein functions using up to three times less number of sequences and explains them in terms of their higher-order epistatic interactions.</font></span></p><font face="arial, sans-serif"><br style="color:rgb(0,0,0)"><span style="color:rgb(0,0,0);font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Bio.</span><span style="color:rgb(0,0,0);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> Amirali Aghazadeh is a postdoctoral researcher in the Electrical Engineering and Computer Science department at the University of California, Berkeley, working with Kannan Ramchandran and Jennifer Listgarten. Prior to that, he was a postdoctoral researcher at Stanford University working with David Tse. He received his PhD degree in Electrical and Computer Engineering from Rice University with Richard Baraniuk in 2017. His research interest is at the intersection of machine learning, signal processing, inverse problems, and computational biology. He is the recipient of the Hershel M. Rich Invention Award for his thesis on rapid methods for DNA sensing as well as the Texas Instruments Fellowship for his graduate studies. He received his Bachelor’s degree in Electrical Engineering from Sharif University of Technology.</span></font></div><div><font color="#000000" face="arial, sans-serif"><span style="white-space:pre-wrap"><br></span></font></div><div><b style="font-family:arial,sans-serif">Host:</b><span style="font-family:arial,sans-serif"> </span><a href="mailto:j3xu@ttic.edu" style="font-family:arial,sans-serif" target="_blank">Jinbo Xu</a>  <font color="#000000" face="arial, sans-serif"><span style="white-space:pre-wrap"><br></span></font></div><br></div><div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 517</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL  60637</font></i></div><div><i><font face="arial, helvetica, sans-serif">p:(773) 834-1757</font></i></div><div><i><font face="arial, helvetica, sans-serif">f: (773) 357-6970</font></i></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Feb 16, 2021 at 3:30 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><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;margin:0px"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b>    </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit">  Wednesday, February 17th at<b> 11:10 am CT</b></font></font><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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"><b>Where:</b>     </font></font><font color="#000000">Zoom Virtual Talk (</font><b><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_aJxlZzpcSeWJqnh6Hjmq2w" target="_blank">register in advance here</a></font></b><font color="#000000">)</font></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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"><b>Who: </b>       </font></font></font>Amirali Aghazadeh, UC Berkeley</p></div><div><br></div><div><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><br></span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Title</span><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.        Inferring Biological Functions with Explainable Algorithms</span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><br></span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-family:arial,sans-serif;font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Abstract</span><span style="font-family:arial,sans-serif;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. Data-driven machine learning models that infer biological functions from sequences are replacing the costly experimental measurements in a number of application areas in biology including protein, small-molecule, and genome engineering. These data-driven models largely owe their success to the recent advancements in over-parameterized models in machine learning such as deep neural networks (DNNs). However, </span><span style="font-family:arial,sans-serif;color:rgb(25,25,25);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">the number of labeled sequences available for training such models has remained small compared to the vastness of the combinatorial sequence space. In addition, these biological functions are typically complex, manifesting as rugged landscapes with high-order epistatic (nonlinear) interactions. The combination of these two factors makes the biological inference problem statistically challenging</span><span style="font-family:arial,sans-serif;color:rgb(25,25,25);font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.</span><br></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="color:rgb(25,25,25);font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> </span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="color:rgb(25,25,25);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><font face="arial, sans-serif">In this talk, I view the problem of inferring biological functions from a statistical signal processing perspective. I first discuss a fundamental interpretation-computation tradeoff in explaining DNNs in terms of their epistatic interactions. I then discuss how to develop a new hybrid algorithm that blends techniques from optimization and coding theory to regularize DNNs for inducing a biologically-relevant prior into their architecture. Our combinatorial method enables DNNs to predict protein functions using up to three times less number of sequences and explains them in terms of their higher-order epistatic interactions.</font></span></p><font face="arial, sans-serif"><br style="color:rgb(0,0,0)"><span style="color:rgb(0,0,0);font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Bio.</span><span style="color:rgb(0,0,0);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> Amirali Aghazadeh is a postdoctoral researcher in the Electrical Engineering and Computer Science department at the University of California, Berkeley, working with Kannan Ramchandran and Jennifer Listgarten. Prior to that, he was a postdoctoral researcher at Stanford University working with David Tse. He received his PhD degree in Electrical and Computer Engineering from Rice University with Richard Baraniuk in 2017. His research interest is at the intersection of machine learning, signal processing, inverse problems, and computational biology. He is the recipient of the Hershel M. Rich Invention Award for his thesis on rapid methods for DNA sensing as well as the Texas Instruments Fellowship for his graduate studies. He received his Bachelor’s degree in Electrical Engineering from Sharif University of Technology.</span></font></div><div><font color="#000000" face="arial, sans-serif"><span style="white-space:pre-wrap"><br></span></font></div><div><b style="font-family:arial,sans-serif">Host:</b><span style="font-family:arial,sans-serif"> </span><a href="mailto:j3xu@ttic.edu" style="font-family:arial,sans-serif" target="_blank">Jinbo Xu</a>  <font color="#000000" face="arial, sans-serif"><span style="white-space:pre-wrap"><br></span></font></div><br></div><div style="font-size:small"><br></div><div style="font-size:small"><br></div><div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 517</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL  60637</font></i></div><div><i><font face="arial, helvetica, sans-serif">p:(773) 834-1757</font></i></div><div><i><font face="arial, helvetica, sans-serif">f: (773) 357-6970</font></i></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, Feb 10, 2021 at 10:09 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"><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;margin:0px"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b>    </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit">  Wednesday, February 17th at<b> 11:10 am CT</b></font></font><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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"><b>Where:</b>     </font></font><font color="#000000">Zoom Virtual Talk (</font><b><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_aJxlZzpcSeWJqnh6Hjmq2w" target="_blank">register in advance here</a></font></b><font color="#000000">)</font></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;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"><b>Who: </b>       </font></font></font>Amirali Aghazadeh, UC Berkeley</p></div><div style="font-size:small"><br></div><div><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><br></span></font></p><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Title</span><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.        Inferring Biological Functions with Explainable Algorithms</span></font></p><font face="arial, sans-serif"><br style="color:rgb(0,0,0)"></font><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><font face="arial, sans-serif"><span style="font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Abstract</span><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. Data-driven machine learning models that infer biological functions from sequences are replacing the costly experimental measurements in a number of application areas in biology including protein, small-molecule, and genome engineering. These data-driven models largely owe their success to the recent advancements in over-parameterized models in machine learning such as deep neural networks (DNNs). However, </span><span style="color:rgb(25,25,25);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">the number of labeled sequences available for training such models has remained small compared to the vastness of the combinatorial sequence space. In addition, these biological functions are typically complex, manifesting as rugged landscapes with high-order epistatic (nonlinear) interactions. The combination of these two factors makes the biological inference problem statistically challenging</span><span style="color:rgb(25,25,25);font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. </span></font></p><font face="arial, sans-serif"><br style="color:rgb(0,0,0)"></font><p dir="ltr" style="color:rgb(0,0,0);line-height:1.656;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="color:rgb(25,25,25);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><font face="arial, sans-serif">In this talk, I view the problem of inferring biological functions from a statistical signal processing perspective. I first discuss a fundamental interpretation-computation tradeoff in explaining DNNs in terms of their epistatic interactions. I then discuss how to develop a new hybrid algorithm that blends techniques from optimization and coding theory to regularize DNNs for inducing a biologically-relevant prior into their architecture. Our combinatorial method enables DNNs to predict protein functions using up to three times less number of sequences and explains them in terms of their higher-order epistatic interactions.</font></span></p><font face="arial, sans-serif"><br style="color:rgb(0,0,0)"><span style="color:rgb(0,0,0);font-weight:700;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Bio.</span><span style="color:rgb(0,0,0);font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> Amirali Aghazadeh is a postdoctoral researcher in the Electrical Engineering and Computer Science department at the University of California, Berkeley, working with Kannan Ramchandran and Jennifer Listgarten. Prior to that, he was a postdoctoral researcher at Stanford University working with David Tse. He received his PhD degree in Electrical and Computer Engineering from Rice University with Richard Baraniuk in 2017. His research interest is at the intersection of machine learning, signal processing, inverse problems, and computational biology. He is the recipient of the Hershel M. Rich Invention Award for his thesis on rapid methods for DNA sensing as well as the Texas Instruments Fellowship for his graduate studies. He received his Bachelor’s degree in Electrical Engineering from Sharif University of Technology.</span></font></div><div><font color="#000000" face="arial, sans-serif"><span style="white-space:pre-wrap"><br></span></font></div><div><b style="font-family:arial,sans-serif">Host:</b><span style="font-family:arial,sans-serif"> </span><a href="mailto:j3xu@ttic.edu" style="font-family:arial,sans-serif" target="_blank">Jinbo Xu</a>  <font color="#000000" face="arial, sans-serif"><span style="white-space:pre-wrap"><br></span></font></div><div><font color="#000000"><span style="white-space:pre-wrap"><font face="Arial" style="font-size:14.6667px"><br></font></span></font><div style="font-size:small;outline:none;padding:10px 0px;width:22px;margin:2px 0px 0px"><br></div></div><div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 517</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL  60637</font></i></div><div><i><font face="arial, helvetica, sans-serif">p:(773) 834-1757</font></i></div><div><i><font face="arial, helvetica, sans-serif">f: (773) 357-6970</font></i></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>
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