David Kotz Vita
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Keynotes Mixed Signals: Audio and wearable data for health diagnostics Cecilia Mascolo Professor of Mobile System at University of Cambridge Co-director for the Centre for Mobile, Wearable Systems and Augmented Intelligence Considerable research has been conducted into mobile and wearable systems for human health monitoring. This research concentrates on either devising sensing and systems techniques to effectively and efficiently collect data about users, and patients or in studying mechanisms to analyse the data coming from these systems accurately. In both cases, these efforts raise important technical as well as ethical issues. In this talk, I plan to reflect on the challenges and opportunities that mobile and wearable health systems are introducing for community, the developers as well as the users. I will use examples from my group's ongoing research on exploring machine learning and data analysis for health application in collaboration with epidemiologists and clinicians. In particular I will discuss our project on using audio signals for disease diagnostics and our recent work in the context of COVID-19. Biography Cecilia Mascolo is the mother of a teenage daughter but also a Full Professor of Mobile Systems in the Department of Computer Science and Technology, University of Cambridge, UK. She is co-director of the Centre for Mobile, Wearable System and Augmented Intelligence and Deputy Head of Department for Research. She is also a Fellow of Jesus College Cambridge and the recipient of an ERC Advanced Research Grant. Prior joining Cambridge in 2008, she was a faculty member in the Department of Computer Science at University College London. -
Spring D85 Newsletter
Dartmouth Class of 1985 Spring 2016 SHADES OF GREEN Class Projects Make a Diference! Spotlight on Dartmouth Center for Professional Development Internships Through Class Projects, the Class of 1985 is also a sponsor of DCPD internships in a variety of professions. Read what one of our grateful interns has to say: Dear Class of 1985 Donor, Thank you so much for your contribution that funded my unpaid internship this winter! I interned as a so!ware developer at a tech start-up ca#ed Parent Co. in Burlington, VT, and I rea#y Are we officially middle aged? If 40 was the new 20 (recall our hopeful 20th enjoyed my experience there! reunion theme), is it time to begin our midlife crisis now? (Might be a great Parent Co. is the creator of Notabli, a social topic for our next issue…!) But whether due to crisis, or to sheer media app that a#ows parents to more securely share digital keepsakes (photos, videos, etc.) with confidence, conviction, and courage, some of us have stepped off the career &iends and family. I worked on the Android and path first chosen. For a few it’s been a completely new direction, and their web versions of Notabli for the duration of my stories are as compelling as their talents run wide and deep. internship. I had never worked with Android before, so I learned a great deal under the Erma Bombeck once said that “it takes a lot of courage to show your dreams mentorship of a very experienced Android to someone else.” We are grateful that the classmates featured here were developer who works for the company. -
Artificial Intelligence Goddard Space Flight Center | November 27-29, 2018 NASA Goddard Artificial Intelligence Workshop
National Aeronautics and Program Book Space Administration Lorem ipsum dolor sit amet, consectetuer sodales sagittis magna. Sed consequat, leo eget adipiscing elit. Aenean commodo ligula eget dolor. bibendum sodales, augue velit cursus nunc, Aenean massa. Cum sociis natoque penatibus et Aenean massa. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus magnis dis parturient montes, nascetur ridiculus mus. Donec quam felis, ultricies nec, pellentesque mus. Donec quam felis, ultricies nec, pellentesque eu, pretium quis, sem. Nulla consequat massa eu, pretium quis, sem. Nulla consequat massa quis enim. Donec pede justo, fringilla vel, aliquet quis enim. Donec pede justo, fringilla vel, aliquet nec, vulputate eget, arcu. In enim justo, rhoncus nec, vulputate eget, arcu. In enim justo, rhoncus ut, imperdiet a, venenatis vitae, justo. Nullam ut, imperdiet a, venenatis vitae, justo. Nullam dictum felis eu pede mollis pretium. Integer dictum felis eu pede mollis pretium. Integer tincidunt. Cras dapibus. Vivamus elementum tincidunt. Cras dapibus. Vivamus elementum semper nisi. Aenean vulputate eleifend tellus. semper nisi. Aenean vulputate eleifend tellus. Aenean leo ligula, porttitor eu, consequat vitae, Aenean leo ligula, porttitor eu, consequat vitae, eleifend ac, enim. Aliquam lorem ante, dapibus eleifend ac, enim. Aliquam lorem ante, dapibus in, viverra quis, feugiat a, tellus. Phasellus viverra in, viverra quis, feugiat a, tellus. Phasellus viverra nulla ut metus varius laoreet. Quisque rutrum. nulla ut metus varius laoreet. Quisque rutrum. Aenean imperdiet. Etiam ultricies nisi vel augue. Aenean imperdiet. Etiam ultricies nisi vel augue. Curabitur ullamcorper ultricies nisi. Nam eget Curabitur ullamcorper ultricies nisi. Nam eget dui. dui. Etiam rhoncus. Maecenas tempus, tellus Etiam rhoncus. -
ARTIFICIAL INTELLIGENCE for Executives and Policy Makers
DINO ESPOSITO SIMONE MASSARO ARTIFICIAL INTELLIGENCE for executives and policy makers Vision and tools to predict the unpredictable Dino Esposito Simone Massaro ARTIFICIAL INTELLIGENCE for executives and policy makers Vision and tools to predict the unpredictable Dino Esposito, Simone Massaro ARTIFICIAL INTELLIGENCE for executives and policy makers Vision and tools to predict the unpredictable © Dino Esposito, 2019 © Simone Massaro, 2019 No part of this publication may be reproduced, transmitted, transcribed, stored in a retrieval system, or translated into any language or computer language, in any form or by any means, electronic, mechanical, magnetic, optical, chemical, manual or otherwise, without the prior written permission of one of the authors. A monumental Thank You goes to the engineering team at For a deeper technical overview of machine learning algorithms BaxEnergy for their general support and the effort put in reviewing and programming techniques, including a basic explanation of the the text. mathematics behind and some programming examples in Python and .NET, here’s a further reference. An even bigger Thank You is reserved to a couple of special people for their relentless help in making sense of the trickiest aspects of machine learning algorithms: Francesco Esposito (Youbiquitous) and Tiago Santos (Smartwatt). Introducing Machine Learning If you will find this reading easy and pleasant, well, that was because Dino Esposito, Francesco Esposito of them! If you still find it hard, well, that’s only our fault. Microsoft Press, 2019 ISBN-10: 0135565669 ISBN-13: 978-0135565667 Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. -
This Thesis Has Been Submitted in Fulfilment of the Requirements for a Postgraduate Degree (E.G
This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use: This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author. The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author. When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given. Multimodal Sensing for Robust and Energy-Efficient Context Detection with Smart Mobile Devices Valentin Radu Doctor of Philosophy Institute of Computing Systems Architecture School of Informatics University of Edinburgh 2017 Abstract Adoption of smart mobile devices (smartphones, wearables, etc.) is rapidly grow- ing. There are already over 2 billion smartphone users worldwide [1] and the per- centage of smartphone users is expected to be over 50% in the next five years [2]. These devices feature rich sensing capabilities which allow inferences about mobile device user’s surroundings and behavior. Multiple and diverse sensors common on such mobile devices facilitate observing the environment from different perspectives, which helps to increase robustness of inferences and enables more complex context detection tasks. Though a larger number of sensing modalities can be beneficial for more accurate and wider mobile context detection, integrating these sensor streams is non-trivial. -
Digitalization Transformation Guides
ISSUE Royal Digitalization Transformation 02 Academy of February 2020 Engineering Guides 1. In this issue: Solutions for a • What is Artificial Intelligence? New Economy • How to bring benefit from Machine Learning in industry? There are numerous applications in • Dangers and ethical which Machine considerations of AI Learning is the right solution and can bring benefit to the Industrial Implementation of Artificial1. Dangers andIntelligent ethical industry. Systems in Industry considerations. 3 . Trends & New Software P.4 The prominence of AI is demonstrated in popular culture with television and movies. Artificial Intelligence refers to that of human intelligence demonstrated by machines which are programmed to think like humans and replicate decisions, behaviours and actions. The application of AI is widely associated to machines having the capability to learn and to solve problems as humans would, or to a better degree. Machine Learning (ML), and AI in general, are still relatively new research fields and are not necessarily the solution to every technological and scientific problem. While it is expected that the presence of AI in the industry will largely increase in the near future, there is a broad spectrum of problems for which AI may not be yet a satisfactory solution. Contents WHAT IS ARTIFICIAL INTELLIGENCE? 3 BRIEF HISTORY 3 DIFFERENCE BETWEEN AI AND MACHINE LEARNING (ML) 3 ML BASICS 4 TAXONOMY OF ML TECHNIQUES 4 LIMITATIONS OF ML 6 THE ML LANDSCAPE BEYOND THE HYPE 6 HOW TO BRING BENEFIT FROM ML IN INDUSTRY? 7 DANGERS AND ETHICAL CONSIDERATIONS. 8 What is Artificial Intelligence? Brief History Although not generally reported, formal research on Artificial Intelligence can be said to have originated at the Macy conferences on Cybernetics (1945-1953). -
Principles for Designing Context-Aware Applications for Physical Activity Promotion
Principles for Designing Context-Aware Applications for Physical Activity Promotion by Gaurav Paruthi A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Information) in the University of Michigan 2018 Doctoral Committee: Associate Professor Mark W. Newman, Chair Associate Professor Natalie Colabianchi Assistant Professor Predrag 'Pedja' Klasnja Professor Ken Resnicow Gaurav Paruthi [email protected] ORCID iD: 0000-0002-5100-1578 © Gaurav Paruthi 2018 Acknowledgement I owe the most thanks to my supervisor Mark Newman for his unconditional support and guidance during my Ph.D. I most enjoyed the freedom given to me to pursue multiple intellectual streams. At times, I wasn’t sure how things would converge, but Mark’s confidence in me allowed me to keep pursuing the things that interest me the most. From choosing research projects to exploring startup opportunities, I was fortunate to have an advisor who encouraged and supported me in my endeavors. This freedom allowed me to explore three state of the art technologies- crowdsourcing, machine learning, and hardware prototyping that I was deeply excited about and then successfully explored through the projects described in this dissertation. I want to thank my dissertation committee members: Pedja Klasnja, Natalie Colabianchi, and Ken Resnicow. Their support and feedback helped me to focus and take the individual projects to completion. I would also like to note that I wouldn’t have had the opportunity to explore the area of Health behavior without Pedja’s support. I was always motivated by his excitement and vision of how research and design can have a significant impact in improving the health behavior of people. -
Department of Computer Science Activity 1998-2004
Dartmouth College Dartmouth Digital Commons Computer Science Technical Reports Computer Science 3-20-2005 Department of Computer Science Activity 1998-2004 David Kotz Dartmouth College Follow this and additional works at: https://digitalcommons.dartmouth.edu/cs_tr Part of the Computer Sciences Commons Dartmouth Digital Commons Citation Kotz, David, "Department of Computer Science Activity 1998-2004" (2005). Computer Science Technical Report TR2005-534. https://digitalcommons.dartmouth.edu/cs_tr/267 This Technical Report is brought to you for free and open access by the Computer Science at Dartmouth Digital Commons. It has been accepted for inclusion in Computer Science Technical Reports by an authorized administrator of Dartmouth Digital Commons. For more information, please contact [email protected]. Department of Computer Science Activity 1998–2004 David Kotz (editor) [email protected] Technical Report TR2005-534 Department of Computer Science, Dartmouth College http://www.cs.dartmouth.edu March 20, 2005 1 Contents 1 Introduction 1 2 Courses 2 3 Information Retrieval (Javed Aslam, Daniela Rus) 4 3.1 Activities and Findings ....................................... 4 3.1.1 Mobile agents for information retrieval. .......................... 4 3.1.2 Automatic information organization ............................ 4 3.1.3 Metasearch ......................................... 5 3.1.4 Metasearch, Pooling, and System Evaluation ....................... 6 3.2 Products ............................................... 7 3.3 -
Mirco Musolesi
Mirco Musolesi Curriculum Vitae Office Address: Department of Geography, University College London. Pearson Building. Gower Street. WC1E 6BT London. Mobile Phone Number: +44 (0) 790 9965484 E-mail Address: [email protected] Personal Webpage: http://www.ucl.ac.uk/~ucfamus/ Current Position Reader in Data Science at the Department of Geography, University College London. Faculty Fellow at the Alan Turing Institute, the UK National Institute for Data Science. Education May 2007 PhD in Computer Science from University College London, United Kingdom. PhD Thesis title: “Context-aware Adaptive Routing for Delay Tolerant Networking”. Supervisor: Prof. Cecilia Mascolo (Computer Laboratory, University of Cambridge). December 2002 Laurea in Ingegneria Elettronica (MSci in Electronic Engineering) from University of Bologna, Italy. Master thesis title: “A Data Sharing Middleware for Mobile Computing“. Final Mark: 96/100. Winner of a competitive scholarship from the School of Engineering of the University of Bologna for a research period abroad for the preparation of the Master thesis degree (based on the presentation of an innovative research proposal and academic merit). The thesis was prepared at the Department of Computer Science, University College London from June to November 2002. July 1995 Maturita’ scientifica (baccalaureate) from Liceo Scientifico Augusto Righi, Bologna, Italy. I was enrolled in a special teaching programme with a focus on mathematics, physics and computing. Final Mark: 60/60. Research and Teaching Employment History June 2015-now Reader in Data Science at the Department of Geography, University College London. April 2016-now Faculty Fellow at the Alan Turing Institute, the UK National Institute for Data Science. June 2015-now Honorary Senior Research Fellow at the School of Computer Science, University of Birmingham. -
Inventing Intelligence: on the History of Complex Information Processing and Artificial Intelligence in the United States in the Mid-Twentieth Century
UNIVERSITY OF CAMBRIDGE Inventing Intelligence: On the History of Complex Information Processing and Artificial Intelligence in the United States in the Mid-Twentieth Century This thesis is submitted for the degree of Doctor of Philosophy In History and Philosophy of Science By Jonathan Nigel Ross Penn (‘Jonnie Penn’) Pembroke College, Cambridge Examiners: Professor Simon Schaffer, University of Cambridge Professor Jon Agar, University College London Supervisor: Dr. Richard Staley, University of Cambridge Advisor: Dr. Helen Anne Curry, University of Cambridge Word Count: 78,033 Date: 14 December 2020 This thesis is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. I further state that no substantial part of my thesis has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. It does not exceed the prescribed word limit for the Degree Committee of the Department of History and Philosophy of Science at the University of Cambridge. Copyright Jonnie Penn, 2020. All rights reserved. 2 Abstract Inventing Intelligence: On the History of Complex Information Processing and Artificial Intelligence in the United States in the Mid-Twentieth Century In the mid-1950s, researchers in the United States melded formal theories of problem solving and intelligence with another powerful new tool for control: the electronic digital computer. -
Shyam A. Tailor
Shyam A. Tailor Email: [email protected] | Website: www.shyamtailor.me | GitHub: shyam196 | LinkedIn: shyam-tailor Education University of Cambridge PhD in Computer Science October 2019 — Present • Second year student in the Machine Learning Systems group; supervised by Dr Nicholas Lane. • Interested in techniques for efficient on-device machine learning applications operating on non-uniformly structured data. Particular interest in graph neural networks (GNNs), including applications to problems such as computer vision and system optimisation. • Full papers at HotMobile and ICLR; workshop papers at MLSys, ICML and ICLR. First year spent at University of Oxford (until September 2020) before research group move. Skills: Machine Learning Edge Compute Graph Neural Networks Computer Vision Python C++ PyTorch University of Cambridge MEng Computer Science October 2018 — June 2019 • Distinction (rank 2/16, 87%). Specialised in cyber-physical systems and machine learning. • Dissertation: “Continuous Auscultation in the Wild”. Supervised by Prof Cecilia Mascolo; investigated wearable devices that listen to the body in real-time for health applications. Awarded best paper at WellComp workshop at UbiComp 2020. Skills: Machine Learning Cyber-Physical Systems Wearables Audio Analysis Python C++ University of Cambridge BA Computer Science October 2015 — June 2018 • 1st class honours (rank 3/98, 84%). 1st class achieved every year of degree. • Dissertation: “Anonymous Proximity Beacons from Smartphones”. Supervised by Dr Robert Harle. • Investigated anonymous proximity detection using Bluetooth-enabled smartphones. Similar approaches used for COVID-19 contact tracing. Results published at PerCom 2018. Skills: Smartphones Android Bluetooth Java SQL Bash Git Unix Tools Work Experience ARM Research Intern May 2021 — August 2021 • Investigating techniques to optimize models operating on point cloud data. -
Artificial Intelligence” – 2-Month Dartmouth Workshop; 10 Attendees – Name Was Chosen
AI Methodology Theoretical aspects – Mathematical formalizations, properties, algorithms Engineering aspects – The act of building (useful) machines Empirical science – Experiments What's involved in Intelligence? A) Ability to interact with the real world to perceive, understand, and act speech recognition and understanding (natural language) image understanding (computer vision) B) Reasoning and Planning CS4700 modeling the external world problem solving, planning, and decision making ability to deal with unexpected problems, uncertainties C) Learning and Adaptation Lots of data. Use to train statistical models. We are continuously learning and adapting. We want systems that adapt to us! AI leverages from different disciplines philosophy e.g., foundational issues (can a machine think?), issues of knowledge and believe, mutual knowledge psychology and cognitive science e.g., problem solving skills neuro-science e.g., brain architecture computer science and engineering e.g., complexity theory, algorithms, logic and inference, programming languages, and system building. mathematics and physics e.g., statistical modeling, continuous mathematics, statistical physics, and complex systems. Historical Perspective Obtaining an understanding of the human mind is one of the final frontiers of modern science. Founders: George Boole, Gottlob Frege, and Alfred Tarski • formalizing the laws of human thought Alan Turing, John von Neumann, and Claude Shannon • thinking as computation John McCarthy (Stanford), Marvin Minsky (MIT), Herbert Simon and Allen Newell (CMU) • the start of the field of AI (1956) History of AI: The gestation of AI 1943-1956 1943 McCulloch and Pitts – McCulloch and Pitts’ model of artificial neurons – Minsky’s 40-neuron network 1950 Turing’s “Computing machinery and intelligence” 1950s Early AI programs, including Samuel’s checkers program, Newell and Simon’s Logic theorist 1956 Dartmouth meeting : Birth of “Artificial Intelligence” – 2-month Dartmouth workshop; 10 attendees – Name was chosen.