19 th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2016

Ponents

<a name="emilia"></a>Emilia Gómez (October, 19th)

 <img alt="" src="/_files/_event/_4119/_editorFiles/file/EmiliaGomez.jpg" style="width: 119px; height: 157px; float: left; border-width: 1px; border-style: solid; margin-left: 10px; margin-right: 10px;" />Music information retrieval: from signals to meaning; from classical music to flamenco. 
 
 In this talk I will provide an overview of my research in the field of Music Information Retrieval (MIR), which tries to understand the way humans describe music and emulate these descriptions by computational models dealing with big music data. By integrating knowledge from signal processing, music theory, cognition and artificial intelligence, we have developed methods to automatically describe music audio signals in terms of melody, tonality and rhythm; to measure similarity between pieces and automatically classify music according to style, emotion or culture. Over the last years, we have focused on two different application contexts. On one hand, we try to innovate the way we experience classical music concerts. On the other hand, we research on the computational modeling of flamenco music, improving current techniques for singing voice description and style classification.

 
 Bio 
 Emilia Gómez (emiliagomez.wordpress.com) is an Associate Professor (Serra-Húnter and ICREA Fellow) at the Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, where she leads the Music Information Research Lab at the Music Technology Group. She graduated as a Telecommunication Engineer at Universidad de Sevilla (1999) and studied piano performance at Seville Conservatoire of Music. She then received a DEA in Acoustics, Signal Processing and Computer Science applied to Music at IRCAM, Paris (2000) and a PhD in Computer Science at the UPF (2006). She has been lecturer at Escola Superior de Música de Catalunya and visiting researcher at the Royal Institute of Technology, Stockholm (Marie Curie Fellow), McGill University, Montreal, and Queen Mary University of London. She has co-authored more than a 100 peer-reviewed publications and software libraries, and contributed to more than 15 projects, mostly funded by the European Commission. She is currently president-elect of the International Society for Music Information Retrieval.

  

  

  

 <a name="Jose"></a>José Antonio Ortega (October, 19th)

 <img alt="" src="/_files/_event/_4119/_editorFiles/file/twitter (1).jpg" style="width: 150px; height: 150px; border-width: 1px; border-style: solid; margin-left: 10px; margin-right: 10px; float: left;" />Automating Machine Learning workflows.

 ML services are quickly becoming a commodity, and they will be taken for granted by developers and computer users alike in the near future. The building blocks for ML as an ubiquitous service are already in place, almost always in the form of remote APIs that provide a first level of abstraction over ML problem-solving and, specially, obviate scalability and resource allocation issues. But that's not enough: those building blocks still leak implementation details inessential to the application developer that needs to provide domain-specific solutions. We need to ascend a couple of rungs in the abstraction ladder and provide domain-specific languages to describe ML solutions without nitty-gritty details unrelated to the problem at hand, offering non-experts the possibility of automating their ML solutions. In this talk, we'll discuss our experience designing and developing BigML's data wrangling and ML workflow DSLs, Flatline and WhizzML, and how they generalize to similar ML services and APIs. 

  

 Bio

 is part of the founding team of BigML, a startup that applies Machine Learning and other AI techniques to make them accessible to non-specialists. He was hacking for Oblong from 2008 to early 2011. Before that, he worked for Google (from July 2007). From June 2005 to May 2007, he worked on embedded software development for the scientific payload of LISA Pathfinder. He was a theoretical physicist in a previous life, and wrote a Ph. D. thesis on gravitational wave detectors. He also got a bachelor’s degree in computer science. Between 2003 and 2005, he taught courses on programming and computer networks at the Universitat Autonoma of Barcelona, where he was part of the mobile agents research group.

  

  

 
 
 <a name="petia"></a>Petia Radeva  (October, 20th)

 <img alt="" src="/_files/_event/_4119/_editorFiles/file/petia.png" style="width: 119px; height: 178px; border-width: 1px; border-style: solid; margin-left: 5px; margin-right: 5px; float: left;" />Can Deep Learning and Egocentric Vision for Visual Lifelogging help us eat better? 
 
 The analysis of people's nutrition habits is one of the most important mechanisms for applying a thorough monitorisation of several medical conditions (e.g. diabetes, obesity, etc.) that affect a high percentage of the global population.  Methods for automatically logging one's meals could not only make the process easier, but also make it objective to the user's point of view and interpretability. One of the solutions adopted recently that could ease the automatic construction of  nutrition diaries is to ask individuals to take photos with their mobile phones. An alternative technique is visual lifelogging  that consists of using a wearable camera that automatically captures pictures from the user point of view (egocentric point of view) with the aim to analyse different patterns of his/her daily life and extract highly relevant information like nutritional habits. In this talk we will show how deep learning applied to the food detection and food recognition problems can help to automatically infer the user's eating pattern. 
 
 Bio 
 Petia Radeva completed her undergraduate study on Applied Mathematics at the University of Sofia, Bulgaria, in 1989. In 1996, she received a Ph.D. degree in Computer Vision at UAB. In 2007, she moved as Tenured Associate professor at the Universitat de Barcelona (UB), Department of Mathematics and Informatics, where from 2009 to 2013 she was Director of Computer Science Undergraduate Studies. Petia Radeva is Head of the Consolidated Group Computer Vision at the University of Barcelona (CVUB) at UB (www.ub.edu/cvub) and Head of the Medical Imaging Laboratory of Computer Vision Center (www.cvc.uab.es). Petia Radeva’s research interests are on Development of learning-based approaches (specially, deep learning) for computer vision, and their application to health. Currently, she is involved on projects that study the application of wearable cameras and life-logging, to extract visual diary of individuals to be used for memory reinforcement of patients with mental diseases (e.g. Mild cognitive impairment). Moreover, she is exploring how to extract semantically meaningful events that characterize lifestyle and healthy habits of people  from egocentric data. Other projects she is involved are: Machine learning tools for large scale object recognition, Food analysis by Computer Vision, Tissue characterisation and plaque analysis in carotid images, etc. She has h-index of 33 (Google Academic), with 1138 citations publishing 95 JCR articles and 232 international scientific publications. She is a coautor of 24 international patents in the field of Computer Vision applied to Medical Imaging. Associate editor of International Journal of Visual Communication and Image Representation. She obtained the ICREA award from the Catalonian Government for her scientific merits in 2014 and the Prize “Antonio Caparrós” for the best technology transfer project of 2013.

 
 
 <a name="jordi"></a>Jordi Nin (October, 21st)

 <img alt="" src="/_files/_event/_4119/_editorFiles/file/nin.jpg" style="font-family: verdana, geneva, sans-serif; font-size: 16px; font-style: italic; font-weight: bold; width: 119px; height: 121px; border-width: 1px; border-style: solid; float: left; margin-left: 5px; margin-right: 5px;" />

 Lessons learned about Deep Learning for Credit Card Fraud Scoring

 Data is changing our society. Because of data we are rethinking our industries to build better products: agriculture, education, finance, legal, etc. With the advent of data, a prodigal son of the machine learning family has returned to the fore to play a main role: artificial neural networks, also known as Deep Learning. In this talk, I will provide some insights about its application to detect fraudulent credit card transactions conducted in online stores and retailers. I will also describe the data we use, how neural networks are trained and how their performance is measured. Besides, I will discuss more general thoughts about how the possibility of processing huge amount of data has boosted deep learning and machine learning in the industry. 
 
 Bio 
 Dr. Jordi Nin holds a Master degree and Ph.D. in Computer Science from the Autonomous University of Barcelona (UAB, 2005, 2008 respectively). He was granted with the Outstanding PhD Award of the UAB computer science department. From 2006 to 2008, He worked as a Phd. Student at III-CSIC. Later, He moved for two years as a Marie Curie Post-Doc researcher at LAAS-CNRS (France). From 2010 to 2015, he worked as a lecturer in the computer architecture dpt. of UPC and as an associate researcher at the Barcelona Supercomputing Center (BSC). Nowadays, he is a senior data scientist of BBVA D&A. His principal research interests are applied machine learning and big data analytics. He has about 75 research publications in journals, conferences and book chapters. Besides, he has been involved in several national (Catalan, Spanish and French) and European research projects.

Dates Hora local de l'esdeveniment

feb

'16

08:00 Obertura de pujada de resums

feb

'16

23:05 Obertura d'inscripcions

mai

'16

21:59 Tancament de pujada de resums

mai

'16

21:59 Full paper submission

jun

'16

07:00 Author notification

jul

'16

07:00 Final version deadline

jul

'16

21:59 Early registration deadline

jul

'16

07:00 Starts Late Registration

oct

'16

09:00 Data d'inici

oct

'16

13:00 Tancament d'inscripcions

oct

'16

19:00 Data de finalització

Lloc de l'esdeveniment

19 th International Conference of the Catalan Association for Artificial Intelligence, CCIA 201...

Organitzat per DTIC, UPF