Linda Shapiro ADDRESS: Paul G
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MSB Volume 10, Number 2 (Summer 1998)
MSB Most Significant Bits Department of Computer Science & Engineering Volume 10, Number 2 University of Washington Summer 1998 Young, a graduate of Antioch College (’59) who earned his PhD from MIT Karp Receives (’63), joined the UW faculty in 1983 from Purdue, where he had spent most of his Technion’s early career. He was UW Computer Sci- ence chair from 1983 to 1988. Though his leadership style emphasized consen- Harvey Prize sus and cooperation, Richard Ladner In May Prof. Richard Karp was awarded claimed at Young’s retirement party, that the Leo M. Harvey Prize in Science and it was nevertheless difficult having Paul Engineering by the Technion - Israel In- as chair. “None of us who worked with stitute of Technology. Karp was recog- him ever learned to read Paul’s handwrit- nized for his “leadership and achieve- ing,” he quipped. A sampling of Young’s ments in the areas of theoretical computer memos and marginalia were circulated to science and operations research.” His the mystification of the partiers. fundamental contributions to combinato- rial algorithms were specifically cited. Young, whose dissertation advisor was Hartley Rogers, made seminal contribu- The Harvey Prize recognizes break- tions to recursion theory and abstract throughs in science, technology and complexity theory. medicine as well as contributions to peace in the Middle East. Along with luminar- At the transition between the Young and ies such as Mikhail S. Gorbachev and Baer administrations, the department Freeman Dyson, there have been several Paul Young moved from the College of Arts and Sci- other computational pioneers recognized: ences to the College of Engineering. -
Sachinmehtangb
Sachin Mehta #414, Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle - 98195 [email protected] | sacmehta.github.io | sacmehta | sachinmehtangb Personal Profile I am a graduate student in the GRAIL lab and the NLP group at the University of Washington, Seattle. I am advised by Prof. Linda Shaprio and Prof. Hannaneh Hajishirzi. My research interest lies in the intersection of computer vision, machine learning, natural language processing, and artificial intelligence, especially designing fast, light-weight, power efficient, and memory efficient neural architectures that can power real-world applications on edge devices, including Smartphones. I am also excited about machine vision applications across different domains, including pathology and accessibility. Education Doctor of Philosophy, Electrical and Computer Engineering 2015 - (ongoing) University of Washington - Seattle, WA, USA Advisors: Linda Shapiro and Hannaneh Hajishirzi Master of Technology (with distinction), Computer Science 2011- 2014 Coventry University, UK Bachelor of Technology, Electronics and Communication Engineering 2006 - 2010 Jaypee University of Information and Technology, India Chancellor’s Gold Medal Selected Publications Machine Learning for Computer Vision and NLP 1. Sachin Mehta, Marjan Ghazvininejad, Srinivasan Iyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. DeLighT:Very Deep and Light- weight Transformer, Under review. 2. Sachin Mehta, Hannaneh Hajishirzi, and Mohammad Rastegari. DiCENet: Dimension-wise Convolutions for Efficient Networks, Under review. 3. Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari, and Hannaneh Hajishirzi. DeFINE: Deep Factorized Input Word Embeddings for Neural Sequence Modelling, International Conference on Learning Representations (ICLR’20) 4. Sachin Mehta, Mohammad Rastegari, Linda Shapiro, and Hannaneh Hajishirzi. ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network, IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19). -
Pattern Recognition
PATTERN RECOGNITION AUTHOR INFORMATION PACK TABLE OF CONTENTS XXX . • Description p.1 • Audience p.1 • Impact Factor p.1 • Abstracting and Indexing p.2 • Editorial Board p.2 • Guide for Authors p.7 ISSN: 0031-3203 DESCRIPTION . Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. The journal Pattern Recognition was established some 50 years ago, as the field emerged in the early years of computer science. Over the intervening years it has expanded considerably. The journal accepts papers making original contributions to the theory, methodology and application of pattern recognition in any area, provided that the context of the work is both clearly explained and grounded in the pattern recognition literature. Papers whos primary concern falls outside the pattern recognition domain and which report routine applications of it using existing or well known methods, should be directed elsewhere. The publication policy is to publish (1) new original articles that have been appropriately reviewed by competent scientific people, (2) reviews of developments in the field, and (3) pedagogical papers covering specific areas of interest in pattern recognition. Various special issues will be organized from time to time on current topics of interest to Pattern Recognition. Submitted papers should be single column, double spaced, no less than 20 and no more than 35 (40 for a review) pages long, with numbered pages. -
Biomedical and Health Informatics Series
Biomedical and Health Informatics Lecture Series Tuesday, January 21, 2014 12:00 – 12:50 p.m. Health Sciences, Room T-498 Linda Shapiro, PhD Professor, Department of Computer Science & Engineering, Electrical Engineering Adjunct Professor, Department of Biomedical Informatics and Medical Education University of Washington “3D Craniofacial Image Analysis and Retrieval” Craniofacial image analysis is traditionally performed with a set of manually obtained landmarks of the face. Our work, on the contrary, uses full meshes of the subject's head to extract image features that lead to classification, quantification, and image retrieval. This talk will discuss two different but related studies. The first uses histograms of the azimuth and elevation angles of surface normals of the mesh representation of craniofacial anatomy in order to classify and quantify shape abnormalities. The second uses local curvature features to learn the characteristics of regions about facial landmarks and then learns which of these can be successfully used to find the center plane of the human face, even in the presence of clefts. Experiments on three different abnormal conditions are described. Speaker Biography Linda Shapiro, PhD is Professor of Computer Science and Engineering and of Electrical Engineering, and Adjunct Professor of Biomedical Informatics and Medical Education at the University of Washington. She received her BS degree in mathematics from the University of Illinois, Urbana, and MS and PhD degrees in computer science from the University of Iowa, Iowa City. Her research interests include computer vision, image database systems, artificial intelligence, pattern recognition, and robotics. She has co-authored three textbooks, one on data structures and two on computer vision.