Seminars take place at UPF, Campus Poblenou, Roc Boronat, 138, Barcelona and will only be streamed/recorded if the speaker has granted permission.  Rooms 55.309 / 55.410 streaming / Auditorium streaming 

EiTIC members: If you are interested in giving a Research Seminar or you would like to invite a speaker, please  fill in the following form RSDetails Form

 

  Past Research Seminars
JUNE  

June, 17

 

15.30

 

55.309

Invited Research Seminar 

Spatial representations and high level descriptors for symbolic music analysis

By Louis Bigo 

Abstract

This talk focus on two computational approaches to symbolic music analysis. The first approach includes the notion of pitch/chord space, that we use to reformulate in spatial terms different musical tasks as style recognition and harmonic transformations. Musical spaces are formalized with topological structures named simplicial complexes. Elementary musical objects (for example pitches or chords) are represented by simplices that are organized according to a neighborhood relationship which translates a musical property, for example consonance. Such spatial formulation of musical structures enables the application of geometrical and topological operations, such as translations, rotations, embeddings and filtrations, that bring original approaches to music composition and analysis.
The second approach focus on the extraction of high level musicological features that can serve automated analysis of classical forms. We will discuss the detection of cadences and the retrieval of section boundaries in sonata form with different machine learning methods.

Biography

I am associate professor (maître de conférence) in computational musicology at University of Lille in the Algomus team (Algorithmic musicology) at CRIStAL laboratory since 2016. My research interests include mathematical models and machine learning for automatic music generation, analysis and classification. I received my Ph.D in Computer Science on the topic of symbolic music representations and spatial computing in 2013 at IRCAM (French Institute for Research and Coordination in Acoustics/Music) and LACL, University Paris 12. I joined the European project Learning To Create on music and machine learning from 2014 to 2016 in the Music Informatics Group at the University of Basque Country in San Sebastian, Spain.

Host: Xavier Serra

June, 18

 

15.30

 

55.309

Invited Research Seminar 

Exact Time Series Motif Discovery using a Commercial GPU Cluster: How to 
Execute More than One Quintillion Pairware Comparisons in a Single Day

By Philip Brisk

Abstract 

The ability to discover motifs, conserved (repeated) patterns in time series, is arguably the most important computational primitive in time series data mining. Time series motifs are useful in their own right, as they provide insight to domain scientists about the behavior or physical  phenomena that the time series characterizes. Time series motifs can also be used as inputs into and are also used as inputs into classification, clustering, segmentation, visualization, and anomaly detection algorithms. In recent years, the Matrix Profile has emerged as a promising way to represent highly similar subsequences within a larger time series, and allows the efficient exact computation of the top-k motifs. Many scientific domains, such as astronomy and seismology exhibit an insatiable appetite to consider ever-large time series data 
sets. To meet the needs of scientists, the Matrix Profile can be computed in a rapid and scalable manner by deploying on commercial GPU clusters in the cloud; using this framework, it is possible to achieve throughput as high as one quintillion exact pairwise time series comparisons in a single day. For the first time, it has been possble to perform exact motif discover on more than one year's worth of continuous earthquake data in a single run; this has led to the discovery of what may be subtle precusor earthquakes that have previously escape attention, along with other novel seismic irregularities that domain scientists are presently studying.

Biography

Philip Brisk received the B.S., M.S., and Ph.D., all in Computer Science, from UCLA in 2002, 2003, and 2006 respectively. From 2006-2009, he was a postdoctoral scholar at EPFL in Lausanne, Switzerland. Since 2009, he has been with the University of California, Riverside; he has been promoted to Professor effective July 1, 2019. Dr. Brisk's research interests lie at the intersection between processor architecture, VLSI/CAD, compilers, FPGAs, and reconfigurable computing; most recently, he has been applying these principles to the design and analysis of biological instruments. He is a Senior Member of the ACM and IEEE, and is presently an Associate Editor of the IEEE Transactions of Computer-Aided Design on Integrated Circuits and Systems (TCAD) and Integration: The VLSI Journal. 

 

 

June, 20

 

15.30

 

52.S29

PhD Research Seminar 

Impact of machine intelligence in healthcare

By Sergio Sánchez-Martínez

Abstract:

This talk aims at advancing the scientific understanding of machine learning (ML) related to healthcare and at studying the impact of ML algorithms on humans, focusing on clinical decision-making. The talk will be articulated around the essential building blocks to achieving the high-level task of clinical decision-making, namely data acquisition, feature extraction, interpretation and decision support. For each of these blocks, the speaker will provide a concise review of state-of-the-art applications, followed by the challenges still to overcome and the potential benefits of their application in clinical practice. At the end, there will be a discussion on the main problems to tackle when creating algorithms to analyze clinical data and also implementation challenges, such as which interaction paradigms we should use, or the competences medical doctors should have. 

Biography:

Sergio Sánchez-Martínez, Postdoctoral Research Fellow at Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS).

JULY  

July, 10

15:30 

 

55.309

Invited Research Seminar

Facebook News Feed Integrity

By Lluís Garcia-Pueyo

Abstract: 

Facebook goal with News Feed is to show people the stories that matter most to them, every time they visit Facebook. News Feed is a personalized, ever-changing collection of photos, videos, links, and updates from the friends, family, businesses, and news sources they've connected to on Facebook. People on Facebook value meaningful, informative stories, and accurate and authentic content. Integrity teams within Facebook are responsible to maintain and enforce Community Standards that reflect our collective values for what should and should not be allowed on the platform. In this talk, I'll give an overview of how the News Feed works, and how we enforce Integrity in the News Feed, as well as the challenges in combining maximizing relevance while minimizing bad experiences. 

Biography:

Lluis Garcia-Pueyo is an Engineer Manager at Facebook (2017 - now), working on discovering and reducing negative experiences in News Feed ranking. Prior to this, he worked in information extraction and information retrieval at Google Research (2012-2017), and multimedia retrieval and display advertising at Yahoo Research (2007-2012). Luis holds an MS in Computer Science from the Universitat Politècnica de Catalunya (UPC). His research has been published in top-tier conferences such as WWW, KDD, SIGIR, ACM Multimedia, and WSDM, and he is a usual PC member for KDD, WWW and other conferences.
 
Host: Aurelio Ruíz