<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" color="#000000"><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">  Thursday, March 18th 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" color="#000000"> </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="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b>     </font></font><font color="#000000">Zoom Virtual Talk (</font><font color="#0000ff"><b><a href="https://uchicagogroup.zoom.us/webinar/register/WN_T9w_VcEMQHOEmzX2YbDu5g" target="_blank">register in advance here</a></b></font><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" color="#000000"> </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" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Who: </b>       </font></font></font>Lingxiao Wang, UCLA</p></div><div class="gmail_default" style="color:rgb(80,0,80)"><br></div><div class="gmail_default" style="color:rgb(80,0,80)"><span style="font-size:9pt;font-family:Arial;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b><br></b></span></div><div class="gmail_default" style="color:rgb(80,0,80)"><font face="arial, sans-serif"><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b>Title: </b>       </span><span style="white-space:pre-wrap">Towards Efficient and Effective Privacy-Preserving Machine Learning</span><br></font></div><div class="gmail_default" style="color:rgb(80,0,80)"><div style="line-height:1.656;margin-top:0pt;margin-bottom:0pt"><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b><font face="arial, sans-serif"><br></font></b></span></div><div style="line-height:1.656;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"><b>Abstract:</b> </span><span style="white-space:pre-wrap;text-align:justify">The past decade has witnessed the fast growth and tremendous success of machine learning. However, recent studies showed that existing machine learning models are vulnerable to privacy attacks, such as membership inference attacks, and thus pose severe threats to personal privacy. Therefore, one of the major challenges in machine learning is to learn effectively from enormous amounts of sensitive data without giving up on privacy. In this talk, I will discuss my efforts in addressing this challenge for solving two important problems: high-dimensional sparse learning and nonconvex optimization. I will first introduce a knowledge-transfer framework that achieves improved privacy and utility guarantees for privacy-preserving sparse learning approaches. I will then present an efficient stochastic algorithm for solving nonconvex optimization problems with privacy guarantees.  Lastly, I will discuss some ongoing and future research.</span></font></div><font face="arial, sans-serif"><br></font><div style="line-height:1.656;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"><b>Bio: </b></span><span style="white-space:pre-wrap;text-align:justify">Lingxiao</span><span style="white-space:pre-wrap;text-align:justify"> </span><span style="white-space:pre-wrap;text-align:justify">Wang</span><span style="white-space:pre-wrap;text-align:justify"> is a Ph.D. candidate in the Department of Computer Science at the University of California, Los Angeles, advised by Professor Quanquan Gu. Previously he obtained his MS degree in Statistics at the University of Washington. </span><span style="white-space:pre-wrap;text-align:justify">Lingxiao</span><span style="white-space:pre-wrap;text-align:justify">’s research interests are broadly in machine learning, including privacy-preserving machine learning, low-rank matrix learning, high-dimensional graphical models, and federated learning. He is a recipient of Rising Stars in Data Science (2021) from the University of Chicago.</span></font></div></div><div class="gmail_default" style="color:rgb(80,0,80)"><font face="arial, sans-serif"><br></font></div><div class="gmail_default" style="color:rgb(80,0,80)"><font face="arial, sans-serif"><br></font></div><div class="gmail_default" style="color:rgb(80,0,80)"><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:nati@ttic.edu" target="_blank">Nathan Srebro</a></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 Thu, Mar 18, 2021 at 10:00 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" color="#000000"><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">  Thursday, March 18th 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" color="#000000"> </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="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b>     </font></font><font color="#000000">Zoom Virtual Talk (</font><font color="#0000ff"><b><a href="https://uchicagogroup.zoom.us/webinar/register/WN_T9w_VcEMQHOEmzX2YbDu5g" target="_blank">register in advance here</a></b></font><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" color="#000000"> </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" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Who: </b>       </font></font></font>Lingxiao Wang, UCLA</p></div><div><br></div><div><span style="font-size:9pt;font-family:Arial;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b><br></b></span></div><div><font face="arial, sans-serif"><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b>Title: </b>       </span><span style="white-space:pre-wrap">Towards Efficient and Effective Privacy-Preserving Machine Learning</span><br></font></div><div><div style="line-height:1.656;margin-top:0pt;margin-bottom:0pt"><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b><font face="arial, sans-serif"><br></font></b></span></div><div style="line-height:1.656;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"><b>Abstract:</b> </span><span style="white-space:pre-wrap;text-align:justify">The past decade has witnessed the fast growth and tremendous success of machine learning. However, recent studies showed that existing machine learning models are vulnerable to privacy attacks, such as membership inference attacks, and thus pose severe threats to personal privacy. Therefore, one of the major challenges in machine learning is to learn effectively from enormous amounts of sensitive data without giving up on privacy. In this talk, I will discuss my efforts in addressing this challenge for solving two important problems: high-dimensional sparse learning and nonconvex optimization. I will first introduce a knowledge-transfer framework that achieves improved privacy and utility guarantees for privacy-preserving sparse learning approaches. I will then present an efficient stochastic algorithm for solving nonconvex optimization problems with privacy guarantees.  Lastly, I will discuss some ongoing and future research.</span></font></div><font face="arial, sans-serif"><br></font><div style="line-height:1.656;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"><b>Bio: </b></span><span style="white-space:pre-wrap;text-align:justify">Lingxiao</span><span style="white-space:pre-wrap;text-align:justify"> </span><span style="white-space:pre-wrap;text-align:justify">Wang</span><span style="white-space:pre-wrap;text-align:justify"> is a Ph.D. candidate in the Department of Computer Science at the University of California, Los Angeles, advised by Professor Quanquan Gu. Previously he obtained his MS degree in Statistics at the University of Washington. </span><span style="white-space:pre-wrap;text-align:justify">Lingxiao</span><span style="white-space:pre-wrap;text-align:justify">’s research interests are broadly in machine learning, including privacy-preserving machine learning, low-rank matrix learning, high-dimensional graphical models, and federated learning. He is a recipient of Rising Stars in Data Science (2021) from the University of Chicago.</span></font></div></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:nati@ttic.edu" target="_blank">Nathan Srebro</a></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 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, Mar 17, 2021 at 5:03 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" color="#000000"><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">  Thursday, March 18th 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" color="#000000"> </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="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b>     </font></font><font color="#000000">Zoom Virtual Talk (</font><font color="#0000ff"><b><a href="https://uchicagogroup.zoom.us/webinar/register/WN_T9w_VcEMQHOEmzX2YbDu5g" target="_blank">register in advance here</a></b></font><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" color="#000000"> </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" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Who: </b>       </font></font></font>Lingxiao Wang, UCLA</p></div><div><br></div><div><span style="font-size:9pt;font-family:Arial;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b><br></b></span></div><div><font face="arial, sans-serif"><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b>Title: </b>       </span><span style="white-space:pre-wrap">Towards Efficient and Effective Privacy-Preserving Machine Learning</span><br></font></div><div><div style="line-height:1.656;margin-top:0pt;margin-bottom:0pt"><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b><font face="arial, sans-serif"><br></font></b></span></div><div style="line-height:1.656;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"><b>Abstract:</b> </span><span style="white-space:pre-wrap;text-align:justify">The past decade has witnessed the fast growth and tremendous success of machine learning. However, recent studies showed that existing machine learning models are vulnerable to privacy attacks, such as membership inference attacks, and thus pose severe threats to personal privacy. Therefore, one of the major challenges in machine learning is to learn effectively from enormous amounts of sensitive data without giving up on privacy. In this talk, I will discuss my efforts in addressing this challenge for solving two important problems: high-dimensional sparse learning and nonconvex optimization. I will first introduce a knowledge-transfer framework that achieves improved privacy and utility guarantees for privacy-preserving sparse learning approaches. I will then present an efficient stochastic algorithm for solving nonconvex optimization problems with privacy guarantees.  Lastly, I will discuss some ongoing and future research.</span></font></div><font face="arial, sans-serif"><br></font><div style="line-height:1.656;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"><b>Bio: </b></span><span style="white-space:pre-wrap;text-align:justify">Lingxiao</span><span style="white-space:pre-wrap;text-align:justify"> </span><span style="white-space:pre-wrap;text-align:justify">Wang</span><span style="white-space:pre-wrap;text-align:justify"> is a Ph.D. candidate in the Department of Computer Science at the University of California, Los Angeles, advised by Professor Quanquan Gu. Previously he obtained his MS degree in Statistics at the University of Washington. </span><span style="white-space:pre-wrap;text-align:justify">Lingxiao</span><span style="white-space:pre-wrap;text-align:justify">’s research interests are broadly in machine learning, including privacy-preserving machine learning, low-rank matrix learning, high-dimensional graphical models, and federated learning. He is a recipient of Rising Stars in Data Science (2021) from the University of Chicago.</span></font></div></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:nati@ttic.edu" target="_blank">Nathan Srebro</a></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><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 Thu, Mar 11, 2021 at 10:07 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" color="#000000"><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">  Thursday, March 18th 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" color="#000000"> </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="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b>     </font></font><font color="#000000">Zoom Virtual Talk (</font><font color="#0000ff"><b><a href="https://uchicagogroup.zoom.us/webinar/register/WN_T9w_VcEMQHOEmzX2YbDu5g" target="_blank">register in advance here</a></b></font><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" color="#000000"> </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" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Who: </b>       </font></font></font>Lingxiao Wang, UCLA</p></div><div style="font-size:small"><br></div><div style="font-size:small"><span style="font-size:9pt;font-family:Arial;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b><br></b></span></div><div><font face="arial, sans-serif"><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b>Title: </b>       </span><span style="white-space:pre-wrap">Towards Efficient and Effective Privacy-Preserving Machine Learning</span><br></font></div><div><div style="line-height:1.656;margin-top:0pt;margin-bottom:0pt"><span style="font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><b><font face="arial, sans-serif"><br></font></b></span></div><div style="line-height:1.656;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"><b>Abstract:</b> </span><span style="white-space:pre-wrap;text-align:justify">The past decade has witnessed the fast growth and tremendous success of machine learning. However, recent studies showed that existing machine learning models are vulnerable to privacy attacks, such as membership inference attacks, and thus pose severe threats to personal privacy. Therefore, one of the major challenges in machine learning is to learn effectively from enormous amounts of sensitive data without giving up on privacy. In this talk, I will discuss my efforts in addressing this challenge for solving two important problems: high-dimensional sparse learning and nonconvex optimization. I will first introduce a knowledge-transfer framework that achieves improved privacy and utility guarantees for privacy-preserving sparse learning approaches. I will then present an efficient stochastic algorithm for solving nonconvex optimization problems with privacy guarantees.  Lastly, I will discuss some ongoing and future research.</span></font></div><font face="arial, sans-serif"><br></font><div style="line-height:1.656;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"><b>Bio: </b></span><span style="white-space:pre-wrap;text-align:justify">Lingxiao</span><span style="white-space:pre-wrap;text-align:justify"> </span><span style="white-space:pre-wrap;text-align:justify">Wang</span><span style="white-space:pre-wrap;text-align:justify"> is a Ph.D. candidate in the Department of Computer Science at the University of California, Los Angeles, advised by Professor Quanquan Gu. Previously he obtained his MS degree in Statistics at the University of Washington. </span><span style="white-space:pre-wrap;text-align:justify">Lingxiao</span><span style="white-space:pre-wrap;text-align:justify">’s research interests are broadly in machine learning, including privacy-preserving machine learning, low-rank matrix learning, high-dimensional graphical models, and federated learning. He is a recipient of Rising Stars in Data Science (2021) from the University of Chicago.</span></font></div></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:nati@ttic.edu" target="_blank">Nathan Srebro</a></font></div><div><font face="arial, sans-serif"><br></font></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></div>
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