Abstract:

This talk concentrates on our efforts over the years to make the harvesting of relevant data from mobile devices accurate and efficient, to allow on device data interpretation and to produce models able to interpret the data so that it can be exploited for a wide range of applications. In this sense I will describe specifics of our work which range from fitting mobile sensing inference on devices and how we are able to exploit local device heterogeneous computation resources efficiently to data analytics for mobile health and urban computing. I will discuss challenges and opportunities of the field throughout the talk.

Speaker Bio:

Cecilia Mascolo is a mother of a teenage daughter. She is also Full Professor of Mobile Systems in the Computer Laboratory, University of Cambridge, UK, a Fellow of Jesus College Cambridge and a Faculty Fellow at the Alan Turing Institute for Data Science in London. Prior joining Cambridge in 2008, she has been a faculty member in the Department of Computer Science at University College London. She holds a PhD from the University of Bologna. Her research interests are in human mobility modelling, mobile and sensor systems and networking and spatio-temporal data analysis. She has published in a number of top tier conferences and journals in the area and her investigator experience spans projects funded by Research Councils and industry. She has received numerous best paper awards and in 2016 was listed in “10 Women in Networking /Communications You Should Know”. She has served as steering, organizing and programme committee member of mobile, sensor systems, networking, data science conferences and workshops. She has delivered a number of keynote talks at conferences and workshops in the area of mobility, data science, pervasive computing and systems. She is Associate Editor in Chief for IEEE Pervasive Computing and sits on the editorial boards of IEEE Transactions on Mobile Computing, ACM Transactions on Sensor Networks and ACM Transactions on Interactive, Mobile, Wearable and Ubiquitous Technologies. More details at www.cl.cam.ac.uk/users/cm542.

Abstract:
Searching for complex objects (e.g. images, faces, audio or video), is an everyday problem in computer science, motivated by many applications. Efficient algorithms are demanded for reverse searching, also known as query by content, in large repositories. Current industrial solutions are ad hoc, domain-dependant, hardware intensive and have limited scaling. However, those disparate domains can be modelled, for indexing and searching, as a metric space. This model has been championed to become a solution to generic proximity searching problems. In practice, however, the metric space approach has been limited by the amount of main memory available.

In this talk we will explore the main ideas behind this technology, present a successful example in audio indexing and retrieval. The application scales well for large amounts of audio because the representation is quite compact and the full audio streams are not needed for indexing and searching.

Speaker Bio:
Edgar Chavez received his Phd from the Center for Mathematical Research in Guanajuato, Mexico in 1999. He founded the information retrieval group at Universidad Michoacana where he worked until 2012. After a brief period in the Institute of Mathematics in UNAM, he joined the computer science department in CICESE in 2013, where he founded the data science group. His main research interest include access and retrieval of data and data representation, such as fingerprints and point clouds. In 2009 he obtained the Thompson-Reuters award for having the most cited paper in computer science in Mexico and Latin America. In 2008 he co-funded, with Gonzalo Navarro, the conference Similarity Search and Applications, which is an international reference in the area. He has published more than 100 scientific contributions, with about 3500 citations in google scholar.