<div dir="ltr"><div dir="ltr"><div class="gmail_default" style="font-size:small"><div class="gmail_default"><div class="gmail_default"><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, January 20th 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><font color="#000000" style="font-family:arial,sans-serif">Zoom Virtual Talk (</font><b style="font-family:arial,sans-serif"><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_pe1CHU7SSnWzn7-xTvdbHg" target="_blank">register in advance here</a></font></b><font color="#000000" style="font-family: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></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>David Rosen, MIT</p></div><br></div><div class="gmail_default"><p style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><font face="arial, sans-serif"><b>Title:</b> Provably Sound Perception for Reliable Autonomy<br></font></p><font face="arial, sans-serif"><b>Abstract:</b> Machine perception -- the ability to construct accurate models of the world from raw sensor data -- is an essential capability for mobile robots, supporting such fundamental functions as planning, navigation, and control. However, the development of algorithms for robotic perception that are both *practical* and *reliable* presents a formidable challenge: many fundamental perception tasks require the solution of computationally hard estimation problems, yet practical methods are constrained to run in real-time on resource-limited mobile platforms. Moreover, reliable perception algorithms must also be robust to the myriad challenges encountered in real-world operation, including sensor noise, uncertain or misspecified perceptual models, and potentially corrupted data (from e.g. sensor faults).<br><br>In this talk, I present my work on the design of practical provably sound machine perception algorithms, focusing on the motivating application of spatial perception. First, I address the fundamental problem of pose-graph optimization (PGO); this is a high-dimensional estimation problem over a nonconvex state space, and so is computationally challenging to solve in general. Nevertheless, we present a convex relaxation whose minimizer provides an *exact, globally optimal* PGO solution in a noise regime that includes most practical applications in robotics and computer vision. We leverage this relaxation to develop SE-Sync, the first practical SLAM algorithm provably capable of recovering *certifiably correct* solutions.<br><br>Second, I briefly describe our recent work on learning to estimate rotations. We show that topological obstructions prevent deep neural networks (DNNs) employing common rotation parameterizations (e.g. quaternions) from learning to estimate widely-dispersed rotation targets. We describe a novel parameterization of 3D rotations that overcomes this obstruction, and that supports an explicit notion of uncertainty in our networks’ predictions. We show that DNNs employing our representation are consistently more accurate when applied to object pose estimation tasks, and that their predicted uncertainties enable the reliable identification of out-of-distribution test examples (including corrupted inputs).<br><br>Finally, I will conclude with a discussion of future directions that aim to unify provably sound estimation and learning methods, thereby enabling the creation of perception systems with both the *robustness* and *adaptability* necessary to support reliable long-term robotic autonomy in the real world.<br><br><b>Bio: </b>David M. Rosen is a postdoctoral associate in the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. Prior to joining LIDS, he was a Research Scientist at Oculus Research (now Facebook Reality Labs) in Seattle. He received his ScD in Computer Science from the Massachusetts Institute of Technology in 2016. His research addresses the design of practical provably sound methods for machine perception, using a combination of tools from optimization, geometry, algebra, and probabilistic inference. His work has been recognized with a Best Paper Award at the 2016 International Workshop on the Algorithmic Foundations of Robotics, an RSS Pioneer Award at Robotics: Science and Systems 2019, and a Best Student Paper Award at Robotics: Science and Systems 2020.</font></div><div class="gmail_default"><font face="arial, sans-serif"><br></font></div><div class="gmail_default"><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:mwalter@ttic.edu" target="_blank">Matthew Walter</a></font></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><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, Jan 20, 2021 at 10:15 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><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, January 20th 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><font color="#000000" style="font-family:arial,sans-serif">Zoom Virtual Talk (</font><b style="font-family:arial,sans-serif"><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_pe1CHU7SSnWzn7-xTvdbHg" target="_blank">register in advance here</a></font></b><font color="#000000" style="font-family: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></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>David Rosen, MIT</p></div><br></div><div><p style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><font face="arial, sans-serif"><b>Title:</b> Provably Sound Perception for Reliable Autonomy<br></font></p><font face="arial, sans-serif"><b>Abstract:</b> Machine perception -- the ability to construct accurate models of the world from raw sensor data -- is an essential capability for mobile robots, supporting such fundamental functions as planning, navigation, and control. However, the development of algorithms for robotic perception that are both *practical* and *reliable* presents a formidable challenge: many fundamental perception tasks require the solution of computationally hard estimation problems, yet practical methods are constrained to run in real-time on resource-limited mobile platforms. Moreover, reliable perception algorithms must also be robust to the myriad challenges encountered in real-world operation, including sensor noise, uncertain or misspecified perceptual models, and potentially corrupted data (from e.g. sensor faults).<br><br>In this talk, I present my work on the design of practical provably sound machine perception algorithms, focusing on the motivating application of spatial perception. First, I address the fundamental problem of pose-graph optimization (PGO); this is a high-dimensional estimation problem over a nonconvex state space, and so is computationally challenging to solve in general. Nevertheless, we present a convex relaxation whose minimizer provides an *exact, globally optimal* PGO solution in a noise regime that includes most practical applications in robotics and computer vision. We leverage this relaxation to develop SE-Sync, the first practical SLAM algorithm provably capable of recovering *certifiably correct* solutions.<br><br>Second, I briefly describe our recent work on learning to estimate rotations. We show that topological obstructions prevent deep neural networks (DNNs) employing common rotation parameterizations (e.g. quaternions) from learning to estimate widely-dispersed rotation targets. We describe a novel parameterization of 3D rotations that overcomes this obstruction, and that supports an explicit notion of uncertainty in our networks’ predictions. We show that DNNs employing our representation are consistently more accurate when applied to object pose estimation tasks, and that their predicted uncertainties enable the reliable identification of out-of-distribution test examples (including corrupted inputs).<br><br>Finally, I will conclude with a discussion of future directions that aim to unify provably sound estimation and learning methods, thereby enabling the creation of perception systems with both the *robustness* and *adaptability* necessary to support reliable long-term robotic autonomy in the real world.<br><br><b>Bio: </b>David M. Rosen is a postdoctoral associate in the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. Prior to joining LIDS, he was a Research Scientist at Oculus Research (now Facebook Reality Labs) in Seattle. He received his ScD in Computer Science from the Massachusetts Institute of Technology in 2016. His research addresses the design of practical provably sound methods for machine perception, using a combination of tools from optimization, geometry, algebra, and probabilistic inference. His work has been recognized with a Best Paper Award at the 2016 International Workshop on the Algorithmic Foundations of Robotics, an RSS Pioneer Award at Robotics: Science and Systems 2019, and a Best Student Paper Award at Robotics: Science and Systems 2020.</font></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:mwalter@ttic.edu" target="_blank">Matthew Walter</a></font></div><div><br></div><div><br></div><div><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><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Jan 19, 2021 at 12:36 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><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><br>When:</b> </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit"> Wednesday, January 20th 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><font color="#000000" style="font-family:arial,sans-serif">Zoom Virtual Talk (</font><b style="font-family:arial,sans-serif"><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_pe1CHU7SSnWzn7-xTvdbHg" target="_blank">register in advance here</a></font></b><font color="#000000" style="font-family: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></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>David Rosen, MIT</p></div><br></div><div><p style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><font face="arial, sans-serif"><b>Title:</b> Provably Sound Perception for Reliable Autonomy<br></font></p><font face="arial, sans-serif"><b>Abstract:</b> Machine perception -- the ability to construct accurate models of the world from raw sensor data -- is an essential capability for mobile robots, supporting such fundamental functions as planning, navigation, and control. However, the development of algorithms for robotic perception that are both *practical* and *reliable* presents a formidable challenge: many fundamental perception tasks require the solution of computationally hard estimation problems, yet practical methods are constrained to run in real-time on resource-limited mobile platforms. Moreover, reliable perception algorithms must also be robust to the myriad challenges encountered in real-world operation, including sensor noise, uncertain or misspecified perceptual models, and potentially corrupted data (from e.g. sensor faults).<br><br>In this talk, I present my work on the design of practical provably sound machine perception algorithms, focusing on the motivating application of spatial perception. First, I address the fundamental problem of pose-graph optimization (PGO); this is a high-dimensional estimation problem over a nonconvex state space, and so is computationally challenging to solve in general. Nevertheless, we present a convex relaxation whose minimizer provides an *exact, globally optimal* PGO solution in a noise regime that includes most practical applications in robotics and computer vision. We leverage this relaxation to develop SE-Sync, the first practical SLAM algorithm provably capable of recovering *certifiably correct* solutions.<br><br>Second, I briefly describe our recent work on learning to estimate rotations. We show that topological obstructions prevent deep neural networks (DNNs) employing common rotation parameterizations (e.g. quaternions) from learning to estimate widely-dispersed rotation targets. We describe a novel parameterization of 3D rotations that overcomes this obstruction, and that supports an explicit notion of uncertainty in our networks’ predictions. We show that DNNs employing our representation are consistently more accurate when applied to object pose estimation tasks, and that their predicted uncertainties enable the reliable identification of out-of-distribution test examples (including corrupted inputs).<br><br>Finally, I will conclude with a discussion of future directions that aim to unify provably sound estimation and learning methods, thereby enabling the creation of perception systems with both the *robustness* and *adaptability* necessary to support reliable long-term robotic autonomy in the real world.<br><br><b>Bio: </b>David M. Rosen is a postdoctoral associate in the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. Prior to joining LIDS, he was a Research Scientist at Oculus Research (now Facebook Reality Labs) in Seattle. He received his ScD in Computer Science from the Massachusetts Institute of Technology in 2016. His research addresses the design of practical provably sound methods for machine perception, using a combination of tools from optimization, geometry, algebra, and probabilistic inference. His work has been recognized with a Best Paper Award at the 2016 International Workshop on the Algorithmic Foundations of Robotics, an RSS Pioneer Award at Robotics: Science and Systems 2019, and a Best Student Paper Award at Robotics: Science and Systems 2020.</font></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:mwalter@ttic.edu" target="_blank">Matthew Walter</a></font></div><div><br></div><div><br></div><div><br></div><div><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><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, Jan 13, 2021 at 8:02 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"><br clear="all"></div><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, January 20th 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><font color="#000000" style="font-family:arial,sans-serif">Zoom Virtual Talk (</font><b style="font-family:arial,sans-serif"><font color="#0000ff"><a href="https://uchicagogroup.zoom.us/webinar/register/WN_pe1CHU7SSnWzn7-xTvdbHg" target="_blank">register in advance here</a></font></b><font color="#000000" style="font-family: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></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>David Rosen, MIT</p></div><br></div><div><p style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><font face="arial, sans-serif"><b>Title:</b> Provably Sound Perception for Reliable Autonomy<br></font></p><font face="arial, sans-serif"><b>Abstract:</b> Machine perception -- the ability to construct accurate models of the world from raw sensor data -- is an essential capability for mobile robots, supporting such fundamental functions as planning, navigation, and control. However, the development of algorithms for robotic perception that are both *practical* and *reliable* presents a formidable challenge: many fundamental perception tasks require the solution of computationally hard estimation problems, yet practical methods are constrained to run in real-time on resource-limited mobile platforms. Moreover, reliable perception algorithms must also be robust to the myriad challenges encountered in real-world operation, including sensor noise, uncertain or misspecified perceptual models, and potentially corrupted data (from e.g. sensor faults).<br><br>In this talk, I present my work on the design of practical provably sound machine perception algorithms, focusing on the motivating application of spatial perception. First, I address the fundamental problem of pose-graph optimization (PGO); this is a high-dimensional estimation problem over a nonconvex state space, and so is computationally challenging to solve in general. Nevertheless, we present a convex relaxation whose minimizer provides an *exact, globally optimal* PGO solution in a noise regime that includes most practical applications in robotics and computer vision. We leverage this relaxation to develop SE-Sync, the first practical SLAM algorithm provably capable of recovering *certifiably correct* solutions.<br><br>Second, I briefly describe our recent work on learning to estimate rotations. We show that topological obstructions prevent deep neural networks (DNNs) employing common rotation parameterizations (e.g. quaternions) from learning to estimate widely-dispersed rotation targets. We describe a novel parameterization of 3D rotations that overcomes this obstruction, and that supports an explicit notion of uncertainty in our networks’ predictions. We show that DNNs employing our representation are consistently more accurate when applied to object pose estimation tasks, and that their predicted uncertainties enable the reliable identification of out-of-distribution test examples (including corrupted inputs).<br><br>Finally, I will conclude with a discussion of future directions that aim to unify provably sound estimation and learning methods, thereby enabling the creation of perception systems with both the *robustness* and *adaptability* necessary to support reliable long-term robotic autonomy in the real world.<br><br><b>Bio: </b>David M. Rosen is a postdoctoral associate in the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. Prior to joining LIDS, he was a Research Scientist at Oculus Research (now Facebook Reality Labs) in Seattle. He received his ScD in Computer Science from the Massachusetts Institute of Technology in 2016. His research addresses the design of practical provably sound methods for machine perception, using a combination of tools from optimization, geometry, algebra, and probabilistic inference. His work has been recognized with a Best Paper Award at the 2016 International Workshop on the Algorithmic Foundations of Robotics, an RSS Pioneer Award at Robotics: Science and Systems 2019, and a Best Student Paper Award at Robotics: Science and Systems 2020.</font></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:mwalter@ttic.edu" target="_blank">Matthew Walter</a></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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