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Curriculum Vitae
Curriculum Vitae Larry Guth Education B.S. Mathematics, Yale University, 2000 PhD Mathematics, MIT, 2005 Employment Samelson Fellow, Stanford University 2005-6 Szego Assistant Professor, Stanford University, 2006 - 2008 National Science Foundation Postdoctoral Fellow, 2006 - 2008 Tenure-stream Assistant Professor, University of Toronto, 2008 - 2011 Member, Institute for Advanced Study, 2010 - 2011 Professor, New York University, 2011 - 2012 Professor, MIT, 2012 - Fellowships National Science Foundation Graduate Fellowship, 2001 - 2003 National Science Foundation Postdoctoral Fellowship , 2006 - 2008 Alfred P. Sloan Research Fellowship, 2010 - 2014 Simons Investigator, 2014 - Honors Salem prize 2013 Invited talks Invited sectional speaker at the 2010 International Congress of Mathematics Marston Morse lectures, IAS, 2013 Namboodiri lectures, University of Chicago, 2015 1 Selected publications 1. Lipshitz maps from surfaces, Geom. Funct. Anal. 15 (2005), no. 5, 1052-109 2. The width-volume inequality, Geom. Funct. Anal. 17 (2007) no. 4, 1139-1179 3. Notes on Gromov's systolic estimate, Geom. Dedicata 123 (2006) 113-129 4. Symplectic embeddings of polydisks, Invent. Math. 172 (2008) no. 3, 477-489 5. Minimax problems related to cup powers and Steenrod squares, Geom. Funct. Anal. 18 (2009), no. 6, 1917{1987 6. The endpoint case of the Bennett-Carbery-Tao multilinear Kakeya conjecture, Acta Math. 205 (2010), no. 2, 263-286. 7. Systolic inequalities and minimal hypersurfaces, Geom. Funct. Anal. 19 (2010), no. 6, 1688-1692. 8. Volumes of balls in large Riemannian manifolds, Ann. of Math. (2) 173 (2011), no. 1, 5176. 9. Area-expanding embeddings of rectangles, preprint 10. Algebraic methods in discrete analogs of the Kakeya problem (joint with Nets Katz), Adv. -
Zhou Ren Email: [email protected] Homepage
Zhou Ren Email: [email protected] Homepage: http://cs.ucla.edu/~zhou.ren INDUSTRY EXPERIENCE 12/2018 – present, Senior Research Lead, Wormpex AI Research, Bellevue, WA, USA 05/2018 – 12/2018, Senior Research Scientist, Snap Inc., Santa Monica, CA, USA 10/2017 – 05/2018, Research Scientist (III), Snap Inc., Venice, CA, USA 04/2017 – 10/2017, Research Scientist (II), Snap Inc., Venice, CA, USA 07/2016 – 04/2017, Research Scientist (I), Snap Inc., Venice, CA, USA 03/2016 – 06/2016, Research Intern, Snap Inc., Venice, CA, USA 06/2015 – 09/2015, Deep Learning Research Intern, Adobe Research, San Jose, CA, USA 06/2013 – 09/2013, Research Intern, Toyota Technological Institute, Chicago, IL, USA 07/2010 – 07/2012, Project Officer, Media Technology Lab, Nanyang Technological University, Singapore PROFESSIONAL SERVICES Area Chair, CVPR 2021, CVPR 2022, WACV 2022. Senior Program Committee, AAAI 2021. Associate Editor, The Visual Computer Journal (TVCJ), 2018 – present. Director of Industrial Governance Board, Asia-Pacific Signal and Information Processing Association (APSIPA). EDUCATION Doctor of Philosophy 09/2012 – 09/2016, University of California, Los Angeles (UCLA) Major: Vision and Graphics, Computer Science Department Advisor: Prof. Alan Yuille Master of Science 09/2012 – 06/2014, University of California, Los Angeles (UCLA) Major: Vision and Graphics, Computer Science Department Advisor: Prof. Alan Yuille Master of Engineering 08/2010 – 01/2012, Nanyang Technological University (NTU), Singapore Major: Information Engineering, School -
2017-2018 Annual Report
Institute for Computational and Experimental Research in Mathematics Annual Report May 1, 2017 – April 30, 2018 Brendan Hassett, Director Mathew Borton, IT Director Jeff Brock, Associate Director Ruth Crane, Assistant Director Sigal Gottlieb, Deputy Director Elisenda Grigsby, Deputy Director Jeffrey Hoffstein, Consulting Associate Director Caroline Klivans, Associate Director Jill Pipher, Consulting Associate Director Bjorn Sandstede, Associate Director Homer Walker, Consulting Associate Director Ulrica Wilson, Associate Director for Diversity and Outreach Table of Contents Mission ....................................................................................................................................... 6 Core Programs and Events ......................................................................................................... 6 Participant Summaries by Program Type ................................................................................... 9 ICERM Funded Participants ................................................................................................................. 9 All Participants (ICERM funded and Non-ICERM funded) .................................................................. 10 ICERM Funded Speakers ................................................................................................................... 11 All Speakers (ICERM funded and Non-ICERM funded) ...................................................................... 12 ICERM Funded Postdocs .................................................................................................................. -
Polynomial Methods in Combinatorics
UNIVERSITY LECTURE SERIES VOLUME 64 Polynomial Methods in Combinatorics Larry Guth American Mathematical Society https://doi.org/10.1090//ulect/064 Polynomial Methods in Combinatorics UNIVERSITY LECTURE SERIES VOLUME 64 Polynomial Methods in Combinatorics Larry Guth American Mathematical Society Providence, Rhode Island EDITORIAL COMMITTEE Jordan S. Ellenberg Robert Guralnick William P. Minicozzi II (Chair) Tatiana Toro 2010 Mathematics Subject Classification. Primary 05D99. For additional information and updates on this book, visit www.ams.org/bookpages/ulect-64 Library of Congress Cataloging-in-Publication Data Names: Guth, Larry, 1977– Title: Polynomial methods in combinatorics / Larry Guth. Description: Providence, Rhode Island : American Mathematical Society, [2016] | Series: Univer- sity lecture series ; volume 64 | Includes bibliographical references. Identifiers: LCCN 2016007729 | ISBN 9781470428907 (alk. paper) Subjects: LCSH: Combinatorial geometry. | Polynomials. | Geometry, Algebraic. | AMS: Combina- torics – Extremal combinatorics – None of the above, but in this section. msc Classification: LCC QA167 .G88 2016 | DDC 511/.66–dc23 LC record available at http://lccn.loc. gov/2016007729 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy select pages for use in teaching or research. Permission is granted to quote brief passages from this publication in reviews, provided the customary acknowledgment of the source is given. Republication, systematic copying, or multiple reproduction of any material in this publication is permitted only under license from the American Mathematical Society. Permissions to reuse portions of AMS publication content are handled by Copyright Clearance Center’s RightsLink service. For more information, please visit: http://www.ams.org/rightslink. -
Compositional Convolutional Neural Networks: a Deep Architecture with Innate Robustness to Partial Occlusion
Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion Adam Kortylewski Ju He Qing Liu Alan Yuille Johns Hopkins University Abstract Recent findings show that deep convolutional neural net- works (DCNNs) do not generalize well under partial occlu- sion. Inspired by the success of compositional models at classifying partially occluded objects, we propose to inte- grate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion. We term this architecture Compositional Convolutional Neural Net- work. In particular, we propose to replace the fully con- nected classification head of a DCNN with a differentiable compositional model. The generative nature of the compo- sitional model enables it to localize occluders and subse- Figure 1: Partially occluded cars from the MS-COCO quently focus on the non-occluded parts of the object. We dataset [20] that are misclassified by a standard DCNN conduct classification experiments on artificially occluded but correctly classified by the proposed CompositionalNet. images as well as real images of partially occluded objects Intuitively, a CompositionalNet can localize the occluders from the MS-COCO dataset. The results show that DC- (occlusion scores on the right) and subsequently focus on NNs do not classify occluded objects robustly, even when the non-occluded parts of the object to classify the image. trained with data that is strongly augmented with partial occlusions. Our proposed model outperforms standard DC- NNs by a large margin at classifying partially occluded ob- One approach to overcome this limitation is to use data jects, even when it has not been exposed to occluded ob- augmentation in terms of partial occlusion [6, 35]. -
Mathematical Research in High School: the PRIMES Experience
Mathematical Research in High School: The PRIMES Experience The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Etingof, Pavel, Slava Gerovitch, and Tanya Khovanova. “Mathematical Research in High School: The PRIMES Experience.” Notices of the American Mathematical Society 62, no. 08 (September 1, 2015): 910–918. © American Mathematical Society (AMS) As Published http://dx.doi.org/10.1090/noti1270 Publisher American Mathematical Society (AMS) Version Final published version Citable link http://hdl.handle.net/1721.1/110267 Terms of Use Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Mathematical Research in High School: The PRIMES Experience Pavel Etingof, Slava Gerovitch, and Tanya Khovanova Consider a finite set of lines in 3-space. A joint is a point where three of these lines (not lying in the same plane) intersect. If there are L lines, what is the largest possible number of joints? Well, let’s try our luck and randomly choose k planes. Any pair of planes produces a line, and any triple of planes, a joint. Thus, they produce L := k(k − 1)/2 lines and J :=p k(k − 1)(k − 2)/6 joints. If k is large, J is 2 3=2 about 3 L . For many years it was conjectured that one cannot do much better than that, in the 3=2 sense that if L is large, thenp J ≤ CL , where C is a constant (clearly, C ≥ 2 ). -
Brain Mapping Methods: Segmentation, Registration, and Connectivity Analysis
UCLA UCLA Electronic Theses and Dissertations Title Brain Mapping Methods: Segmentation, Registration, and Connectivity Analysis Permalink https://escholarship.org/uc/item/1jt7m230 Author Prasad, Gautam Publication Date 2013 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California University of California Los Angeles Brain Mapping Methods: Segmentation, Registration, and Connectivity Analysis A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science by Gautam Prasad 2013 c Copyright by Gautam Prasad 2013 Abstract of the Dissertation Brain Mapping Methods: Segmentation, Registration, and Connectivity Analysis by Gautam Prasad Doctor of Philosophy in Computer Science University of California, Los Angeles, 2013 Professor Demetri Terzopoulos, Chair We present a collection of methods that model and interpret information represented in structural magnetic resonance imaging (MRI) and diffusion MRI images of the living hu- man brain. Our solution to the problem of brain segmentation in structural MRI combines artificial life and deformable models to develop a customizable plan for segmentation real- ized as cooperative deformable organisms. We also present work to represent and register white matter pathways as described in diffusion MRI. Our method represents these path- ways as maximum density paths (MDPs), which compactly represent information and are compared using shape based registration for population studies. In addition, we present a group of methods focused on connectivity in the brain. These include an optimization for a global probabilistic tractography algorithm that computes fibers representing connectivity pathways in tissue, a novel maximum-flow based measure of connectivity, a classification framework identifying Alzheimer's disease based on connectivity measures, and a statisti- cal framework to find the optimal partition of the brain for connectivity analysis. -
LARRY GUTH Curriculum Vitae Department Of
LARRY GUTH Curriculum Vitae Department of Mathematics 617-253-4326 Room 2-278 [email protected] 77 Massachusetts Avenue Cambridge, MA 02139 Degrees: B.S. Mathematics, Yale University, 2000 Ph.D. Mathematics, MIT, 2005 Employment: Samelson Fellow, Stanford University, 2005-2006 Szego Assistant Professor, Stanford University, 2006-2008 National Science Foundation Postoctoral Fellow, 2006-2008 Tenure-stream Assistant Professor, University of Toronto, 2008-2011 Member, Institute for Advanced Study, 2010-2011 Professor, New York University, 2011-2012 Professor, MIT, Department of Mathematics, 2012- Claude Shannon Professor of Mathematics, MIT, 2019- Fellowships: National Science Foundation Graduate Fellowship, 2001-2003 National Science Foundation Postdoctoral Fellowship, 2006-2008 Alfred P. Sloan Research Fellowship, 2010-2014 Simons Investigator, 2014- Honors: Sloan Research Fellowship, 2010 Salem Prize, 2013 Simons Investigator, Simons Fnd, 2014 Clay Research Award, 2015 School of Science Prize for Excellence in Graduate Teaching, 2015 New Horizons in Mathematics Breakthrough Prize, 2015 Fellow of the American Academy of Arts and Sciences, 2018 Best Paper Award, International Consortium of Chinese Math., 2018 Fellow of the American Mathematical Society, 2019 B^ocher Memorial Prize, 2020 Maryam Mirzakhani Prize in Mathematics, 2020 Margaret MacVicar Faculty Fellow, MIT, 2021 Member of the National Academy of Sciences, 2021 1 B.S. Student Thesis Supervised (University of Toronto): Lisha (Janet) Li: University of Toronto: Received a 2017 PhD in Statistics & Machine Learning, from U.C. Berkeley; currently at Amplify Partners, S.F. Bay Area Graduate Students Supervised: University of Toronto: Dominic Dotterrer: PhD, University of Toronto, 2013: Dickson Instr., University of Chicago, 2013-2016; currently: Senior Research Scientist, Goldman Sachs, 2019-. -
2016-2017 Annual Report
Institute for Computational and Experimental Research in Mathematics Annual Report May 1, 2016 – April 30, 2017 Brendan Hassett, Director Mathew Borton, IT Director Jeff Brock, Associate Director Ruth Crane, Assistant Director Jeffrey Hoffstein, Consulting Associate Director Caroline Klivans, Associate Director Jill Pipher, Consulting Associate Director Sinai Robins, Deputy Director Bjorn Sandstede, Associate Director Homer Walker, Deputy Director Ulrica Wilson, Associate Director for Diversity and Outreach 1 Table of Contents Mission ......................................................................................................................................................... 5 Core Programs and Events ............................................................................................................................. 5 Participant Summaries by Program Type ....................................................................................................... 8 ICERM Funded Participants .................................................................................................................................................................................. 8 All Participants (ICERM funded and Non-ICERM funded) ....................................................................................................................... 9 ICERM Funded Speakers .................................................................................................................................................................................... -
Compositional Generative Networks and Robustness to Perceptible Image Changes
Compositional Generative Networks and Robustness to Perceptible Image Changes Adam Kortylewski Ju He Qing Liu Christian Cosgrove Dept. Computer Science Dept. Computer Science Dept. Computer Science Dept. Computer Science Johns Hopkins University Johns Hopkins University Johns Hopkins University Johns Hopkins University Baltimore, USA Baltimore, USA Baltimore, USA Baltimore, USA [email protected] [email protected] [email protected] [email protected] Chenglin Yang Alan L. Yuille Dept. Computer Science Dept. Computer Science Johns Hopkins University Johns Hopkins University Baltimore, USA Baltimore, USA [email protected] [email protected] Abstract—Current Computer Vision algorithms for classifying paradigm of measuring research progress in computer vision objects, such as Deep Nets, lack robustness to image changes in terms of performance improvements on well-known datasets which, although perceptible, would not fool a human observer. for large-scale image classification [8], segmentation [10], We quantify this by showing how performances of Deep Nets de- grades badly on images where the objects are partially occluded [25], pose estimation [40], and part detection [4]. and degrades even worse on more challenging and adversarial However, the focus on dataset performance encourages situations where, for example, patches are introduced in the researchers to develop computer vision models that work well images to target the weak points of the algorithm. To address this on a particular dataset, but do not transfer well to other problem we develop a novel architecture, called Compositional datasets. We argue that this lack of robustness is caused by Generative Networks (Compositional Nets) which is innately robust to these types of image changes. This architecture replaces the paradigm of evaluating computer vision algorithms on the fully connected classification head of the deep network by balanced annotated datasets (BAD). -
Jason Joseph Corso
Jason Joseph Corso Prepared on October 11, 2020 Contact University of Michigan Voxel51 1301 Beal Avenue 410 N 4th Ave 4238 EECS 3rd Floor Ann Arbor, MI 48109-2122 Ann Arbor, MI 48104-1104 Phone: 734-647-8833 Phone: 734-531-9349 Email: [email protected] Email: [email protected] Web: http://web.eecs.umich.edu/∼jjcorso Web: https://voxel51.com Background Date of Birth: 6 August 1978 Place of Birth: New York, USA Citizenship: USA Security Clearance: On Request Appointments Director of the Stevens Institute for Artificial Intelligence Hoboken, NJ Viola Ward Brinning and Elbert Calhoun Brinning Professor of Computer Science Stevens Institute of Technology 1/2021 - Ongoing Professor Ann Arbor, MI Electrical Engineering and Computer Science University of Michigan 8/2019 - 12/2020 Co-Founder and CEO Ann Arbor, MI Voxel51, Inc. 12/2016 - Present Full list of appointments begins on page 26 Research Focus Cognitive systems and their entanglement with vision, language, physical constraints, robotics, autonomy, and the semantics of the natural world, both in corner-cases and at scale. Research Areas Computer Vision Robot Perception Human Language Data Science / Machine Learning Artificial Intelligence Software Systems Education University of California, Los Angeles Los Angeles, CA Post-Doc in Neuroscience and Statistics 2006-2007 Advisors: Dr. Alan Yuille and Dr. Arthur Toga The Johns Hopkins University Baltimore, MD Ph.D. in Computer Science 6/2006 Advisor: Dr. Gregory D. Hager Dissertation Title:“Techniques for Vision-Based Human-Computer Interaction” -
Polynomial Methods in Combinatorial Geometry
The University of Melbourne Department of Mathematics and Statistics Polynomial Methods in Combinatorial Geometry Kevin Scott Fray Supervised by David Wood Submitted in partial fulfillment of the requirements for the degree of Master of Science May, 2013 Contents Abstract iii 1 The Erd}osDistance Problem 1 2 Incidence Geometry 5 2.1 The distinct distances incidence problem . .5 2.2 The geometry of Elekes' incidence problem . 11 2.3 Ruled Surfaces . 14 3 Dvir's Polynomial Method 17 3.1 The Polynomial Method . 17 3.2 Properties of Polynomials . 18 3.3 Proof of the Finite Field Kakeya Conjecture . 21 3.4 Extensions to the Method . 22 4 The Joints Conjecture 24 4.1 Algebraic Tools . 24 4.2 The Joints Conjecture . 31 5 Polynomial Partitioning 34 5.1 The Szemer´edi-Trotter Theorem . 34 5.2 Decompositions of Space . 36 5.3 Proof of the Szemer´edi-Trotter Theorem via Polynomial Partitioning . 38 6 The Guth-Katz Proof 42 6.1 Proof of the k 3 case . 42 6.2 Proof of the k =≥ 2 case . 48 6.3 Applications to arithmetic combinatorics . 51 7 The Dirac-Motzkin Conjecture 53 8 Extremal Examples 56 8.1 Projective Space . 56 8.2 The B¨or¨oczkyexamples . 59 i 8.3 The Sylvester Examples . 62 8.4 An `Almost Group Law' and the B¨or¨oczkyexamples . 63 9 Vanishing Polynomials 65 9.1 Melchior's Inequality . 65 9.2 Polynomials Vanishing on P ........................ 69 9.3 Reducing to a single cubic . 76 10 The Green-Tao proof 79 10.1 Background Material .